This document summarizes a research paper that uses a semi-supervised classifier to predict extreme CPU utilization in an enterprise IT environment. The paper extracts workload patterns from transactional data collected over a year. It then trains a semi-supervised classifier using this data to predict CPU utilization under high traffic loads. The model is validated in a test environment that simulates the complex, distributed production environment. The semi-supervised model can predict burst CPU utilization 3-4 hours in advance, compared to 1-2 weeks using previous methods, allowing IT teams to better optimize resources.
Stream Processing Environmental Applications in Jordan ValleyCSCJournals
This document discusses stream processing applications for environmental monitoring in Jordan Valley. It presents statistical data collected from weather stations in different Jordan Valley locations. Stream processing is important for continuous monitoring systems to detect events in real-time. The document outlines considerations for stream processing engine design like communication, computation, and flexibility. It also describes Jordan's Irrigation Management Information System, which uses real-time meteorological data from weather stations to optimize water usage for agriculture.
The adoption of cloud environment for various application uses has led to security and privacy concern of user’s data. To protect user data and privacy on such platform is an area of concern.
Many cryptography strategy has been presented to provide secure sharing of resource on cloud platform. These methods tries to achieve a secure authentication strategy to realize feature such as self-blindable access tickets, group signatures, anonymous access tickets, minimal disclosure of tickets and revocation but each one varies in realization of these features. Each feature requires different cryptography mechanism for realization. Due to this it induces computation complexity which affects the deployment of these models in practical application. Most of these techniques are designed for a particular application environment and adopt public key cryptography which incurs high cost due to computation complexity.
To address these issues this work present an secure and efficient privacy preserving of mining data on public cloud platform by adopting party and key based authentication strategy. The proposed SCPPDM (Secure Cloud Privacy Preserving Data Mining) is deployed on Microsoft azure cloud platform. Experiment is conducted to evaluate computation complexity. The outcome shows the proposed model achieves significant performance interm of computation overhead and cost.
IRJET- Research Paper on Energy-Aware Virtual Machine Migration for Cloud Com...IRJET Journal
This document discusses using artificial neural networks and genetic algorithms to optimize virtual machine migration for energy efficiency in cloud computing. Virtual machine migration allows live movement of virtual machines between physical machines for load balancing and maintenance. The proposed approach uses an artificial neural network for virtual machine classification and a genetic algorithm for optimization to reduce energy consumption and service level agreement violations during migration. The goal is to develop an energy-aware virtual machine migration method for cloud computing data centers.
IRJET - Efficient Load Balancing in a Distributed EnvironmentIRJET Journal
This document discusses load balancing algorithms for distributed computing environments. It begins by defining load balancing and describing its importance in distributed systems for optimizing resource utilization and system performance. Several static and dynamic load balancing algorithms are then summarized, including round robin, random, min-min, and max-min algorithms. The document also outlines key issues in load balancing, advantages, metrics for evaluating algorithms, and provides more detailed descriptions of 13 load balancing algorithms.
Combating Software Aging: Use Two-Level Rejuvenation to Maximize Average Reso...Igor Oliveira
This document proposes a two-level rejuvenation strategy to combat software aging in computer systems. It aims to maximize average resource performance and minimize tasks' deadline miss rates. The strategy interleaves a set of "warm rejuvenations" with one "cold rejuvenation". Warm rejuvenations partially restore performance while cold rejuvenations fully restore it. The document presents a resource model accounting for performance degradation and rejuvenations. It then analyzes resource supply to determine the optimal number of warm rejuvenations between cold ones, maximizing average performance when no task information is known. It also designs a dynamic strategy minimizing task deadline miss rates when a real-time periodic task is deployed. Sim
The document discusses a proposed approach for evaluating the performance of file systems. It begins by reviewing existing techniques for performance evaluation like benchmarking and profiling. It then proposes a new evaluation approach that involves: (1) understanding the deployment scenario and applications/workloads, (2) setting up a test bed, (3) configuring the system and defining metrics, (4) executing tests, and (5) using tracer logs of operations and metrics to generate a statistical performance model for analysis and tuning. The proposed approach aims to provide a more precise evaluation of performance at the individual component and file system logic unit levels.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
AN EFFICIENT HYBRID SCHEDULER USING DYNAMIC SLACK FOR REAL-TIME CRITICAL TASK...ijesajournal
This document proposes a hybrid scheduling algorithm for real-time critical tasks in multicore automotive ECUs. It describes the existing static priority and partitioned scheduling approach used in AUTOSAR that has issues like poor core utilization and low priority tasks missing deadlines. The proposed algorithm uses both global and partitioned queues, allows task migration, and prioritizes tasks based on their slack (period minus remaining execution time). It was tested on a simulation tool using different task models and showed improvements over the existing approach in core utilization, average response time, and deadline missing rates.
Stream Processing Environmental Applications in Jordan ValleyCSCJournals
This document discusses stream processing applications for environmental monitoring in Jordan Valley. It presents statistical data collected from weather stations in different Jordan Valley locations. Stream processing is important for continuous monitoring systems to detect events in real-time. The document outlines considerations for stream processing engine design like communication, computation, and flexibility. It also describes Jordan's Irrigation Management Information System, which uses real-time meteorological data from weather stations to optimize water usage for agriculture.
The adoption of cloud environment for various application uses has led to security and privacy concern of user’s data. To protect user data and privacy on such platform is an area of concern.
Many cryptography strategy has been presented to provide secure sharing of resource on cloud platform. These methods tries to achieve a secure authentication strategy to realize feature such as self-blindable access tickets, group signatures, anonymous access tickets, minimal disclosure of tickets and revocation but each one varies in realization of these features. Each feature requires different cryptography mechanism for realization. Due to this it induces computation complexity which affects the deployment of these models in practical application. Most of these techniques are designed for a particular application environment and adopt public key cryptography which incurs high cost due to computation complexity.
To address these issues this work present an secure and efficient privacy preserving of mining data on public cloud platform by adopting party and key based authentication strategy. The proposed SCPPDM (Secure Cloud Privacy Preserving Data Mining) is deployed on Microsoft azure cloud platform. Experiment is conducted to evaluate computation complexity. The outcome shows the proposed model achieves significant performance interm of computation overhead and cost.
IRJET- Research Paper on Energy-Aware Virtual Machine Migration for Cloud Com...IRJET Journal
This document discusses using artificial neural networks and genetic algorithms to optimize virtual machine migration for energy efficiency in cloud computing. Virtual machine migration allows live movement of virtual machines between physical machines for load balancing and maintenance. The proposed approach uses an artificial neural network for virtual machine classification and a genetic algorithm for optimization to reduce energy consumption and service level agreement violations during migration. The goal is to develop an energy-aware virtual machine migration method for cloud computing data centers.
IRJET - Efficient Load Balancing in a Distributed EnvironmentIRJET Journal
This document discusses load balancing algorithms for distributed computing environments. It begins by defining load balancing and describing its importance in distributed systems for optimizing resource utilization and system performance. Several static and dynamic load balancing algorithms are then summarized, including round robin, random, min-min, and max-min algorithms. The document also outlines key issues in load balancing, advantages, metrics for evaluating algorithms, and provides more detailed descriptions of 13 load balancing algorithms.
Combating Software Aging: Use Two-Level Rejuvenation to Maximize Average Reso...Igor Oliveira
This document proposes a two-level rejuvenation strategy to combat software aging in computer systems. It aims to maximize average resource performance and minimize tasks' deadline miss rates. The strategy interleaves a set of "warm rejuvenations" with one "cold rejuvenation". Warm rejuvenations partially restore performance while cold rejuvenations fully restore it. The document presents a resource model accounting for performance degradation and rejuvenations. It then analyzes resource supply to determine the optimal number of warm rejuvenations between cold ones, maximizing average performance when no task information is known. It also designs a dynamic strategy minimizing task deadline miss rates when a real-time periodic task is deployed. Sim
The document discusses a proposed approach for evaluating the performance of file systems. It begins by reviewing existing techniques for performance evaluation like benchmarking and profiling. It then proposes a new evaluation approach that involves: (1) understanding the deployment scenario and applications/workloads, (2) setting up a test bed, (3) configuring the system and defining metrics, (4) executing tests, and (5) using tracer logs of operations and metrics to generate a statistical performance model for analysis and tuning. The proposed approach aims to provide a more precise evaluation of performance at the individual component and file system logic unit levels.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
AN EFFICIENT HYBRID SCHEDULER USING DYNAMIC SLACK FOR REAL-TIME CRITICAL TASK...ijesajournal
This document proposes a hybrid scheduling algorithm for real-time critical tasks in multicore automotive ECUs. It describes the existing static priority and partitioned scheduling approach used in AUTOSAR that has issues like poor core utilization and low priority tasks missing deadlines. The proposed algorithm uses both global and partitioned queues, allows task migration, and prioritizes tasks based on their slack (period minus remaining execution time). It was tested on a simulation tool using different task models and showed improvements over the existing approach in core utilization, average response time, and deadline missing rates.
GENERATIVE SCHEDULING OF EFFECTIVE MULTITASKING WORKLOADS FOR BIG-DATA ANALYT...IAEME Publication
This document proposes an evolutionary ordinal optimization (eOO) approach for scheduling dynamic and multitasking workloads for big data analytics in cloud computing environments. The eOO approach iteratively applies ordinal optimization to obtain suboptimal schedules faster than exhaustive searching, while adapting to workload fluctuations over time. Experimental results show the eOO approach achieves up to 30% higher task throughput compared to existing Monte Carlo and blind pick scheduling methods.
Performance Review of Zero Copy TechniquesCSCJournals
E-government and corporate servers will require higher performance and security as usage increases. Zero copy refers to a collection of techniques which reduce the number of copies of blocks of data in order to make data transfer more efficient. By avoiding redundant data copies, the consumption of memory and CPU resources are reduced, thereby improving performance of the server. To eliminate costly data copies between user space and kernel space or between two buffers in the kernel space, various schemes are used, such as memory remapping, shared buffers, and hardware support. However, the advantages are sometimes overestimated and new security issues arise. This paper describes different approaches to implementing zero copy and evaluates these methods for their performance and security considerations, to help when evaluating these techniques for use in e-government applications
A NOVEL SLOTTED ALLOCATION MECHANISM TO PROVIDE QOS FOR EDCF PROTOCOLIAEME Publication
The IEEE 802.11e EDCF mechanism cannot guarantee the QOS of high-priority traffic as the bandwidth consumption of the low-priority traffic increases. Also, in the presence of high priority traffic dampen link utilization of low priority traffic. To overcome these problems, we propose the Novel mechanism in our research that extends IEEE 802.11e EDCF by introducing a Super Slot and Virtual Collision. Compared to EDCF, our proposed approach has EDCF has two advantages: (a) Higher priority traffic achieves Quality of service regardless of the amount of low priority traffic, and (b) Low priority traffic obtains a higher throughput in the presence of same amount of high priority traffic.
Mc calley pserc_final_report_s35_special_protection_schemes_dec_2010_nm_nsrcNeil McNeill
This document provides a summary of a report on system protection schemes (SPS). It discusses SPS standards, practices, and advancements. It also examines relationships between SPS and other industries like process control and nuclear. The report proposes frameworks to identify risks to SPS from both a process and system view. It contributes methods to assess SPS operational complexity and incorporate this into transmission planning studies. The frameworks and models developed in this report can be applied to real utility systems to evaluate SPS reliability and impacts on the power grid.
This document discusses software rejuvenation techniques to address software aging in complex systems. It introduces the problem of performance degradation over long periods of usage due to data corruption, errors, and excessive resource usage. Software rejuvenation aims to clear these issues and prevent failures by optimizing the rejuvenation time based on variable workload. Two approaches are described: time-based periodic rejuvenation and closed-loop monitoring of system health to estimate resource exhaustion. The objectives are reducing failure rates, avoiding downtime, and improving availability. The methodology simulates rejuvenation using time and load balancing based on RAM utilization.
A Runtime Evaluation Methodology and Framework for Autonomic SystemsIDES Editor
An autonomic system provides self-adaptive ability
that enables system to dynamically adjust its behavior on
environmental changes or system failure. Fundamental
process of adaptive behavior in an autonomic system is consist
of monitoring system or/and environment information,
analyzing monitored information, planning adaptation policy
and executing selected policy. Evaluating system utility is
one of a significant part among them. We propose a novel
approach on evaluating autonomic system at runtime. Our
proposed method takes advantage of a goal model that has
been widely used at requirement elicitation phase to capture
system requirements. We suggest the state-based goal model
that is dynamically activated as the system state changes. In
addition, we defined type of constraints that can be used to
evaluate goal satisfaction level. We implemented a prototype
of autonomic computing software engine to verity our proposed
method. We simulated the behavior of the autonomic
computing engine with the home surveillance robot scenario
and observed the validity of our proposed method
This document introduces software rejuvenation techniques for complex systems. It discusses how software aging can degrade system performance over time due to resource exhaustion and error accumulation. Software rejuvenation proactively reboots systems to clear internal states and prevent failures. The document compares different rejuvenation policies and techniques, such as time-based approaches and approaches using workload monitoring. It also examines how rejuvenation affects virtual machines and discusses methods like cold restarts, warm suspends, and live migration. The goal of this project is to optimize rejuvenation times based on varying workloads to reduce downtime and improve system availability for complex environments.
High availability of data using Automatic Selection Algorithm (ASA) in distri...journalBEEI
High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.
This document discusses common performance testing mistakes and provides recommendations to avoid them. The five main "wrecking balls" that can ruin a performance testing project are: 1) lacking knowledge of the application under test, 2) not seeing the big picture and getting lost in details, 3) disregarding monitoring, 4) ignoring workload specification, and 5) overlooking software bottlenecks. The document emphasizes the importance of understanding the application, building a mental model to identify potential bottlenecks, and using monitoring to measure queues and resource utilization rather than just time-based metrics.
Oracle database performance diagnostics - before your beginHemant K Chitale
This is an article that I had written in 2011 for publication on OTN. It never did appear. So I am making it available here. It is not "slides" but is only 7 pages long. I hope you find it useful.
This document discusses using machine learning models to predict whether a loan applicant should be approved or not based on their application details. It first describes collecting past loan applicant data and comparing different machine learning models on this data. The most promising model is then trained on the data. New applicants' details are tested against the trained model to predict if they should be approved or not. Several machine learning methods are evaluated, including decision trees, random forests, support vector machines, linear models, neural networks and AdaBoost. Parameters for each model are also specified. The system is concluded to be an efficient way to help banks process loan applications while reducing risk.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
The document describes the development of an employee management system. It discusses analyzing the data needed for the system and designing relational database tables to store employee information. This includes tables for employee details, work history, time records, salary, contacts, and holidays. The document also covers using C# and Microsoft Access to build the graphical user interface and connect it to the backend database. Functions are implemented to retrieve, add, update and delete employee records from the database.
Performing initiative data prefetchingKamal Spring
Abstract—This paper presents an initiative data prefetching scheme on the storage servers in distributed file systems for cloud
computing. In this prefetching technique, the client machines are not substantially involved in the process of data prefetching, but the
storage servers can directly prefetch the data after analyzing the history of disk I/O access events, and then send the prefetched data
to the relevant client machines proactively. To put this technique to work, the information about client nodes is piggybacked onto the
real client I/O requests, and then forwarded to the relevant storage server. Next, two prediction algorithms have been proposed to
forecast future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetched
data can be pushed to the relevant client machine from the storage server. Through a series of evaluation experiments with a
collection of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed
file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud
are not responsible for predicting I/O access operations, which can definitely contribute to preferable system performance on them.
This document proposes a new task scheduling algorithm called Dynamic Heterogeneous Shortest Job First (DHSJF) for heterogeneous cloud computing systems. DHSJF aims to improve performance metrics like reduced makespan and low energy consumption by considering the heterogeneity of resources and workloads. It discusses existing scheduling algorithms like Round Robin, First Come First Serve and their limitations. The proposed DHSJF algorithm prioritizes tasks with the shortest estimated completion time to optimize resource utilization and improve overall performance of the cloud computing system. Simulation results show that DHSJF provides better results for metrics like average waiting time and turnaround time as compared to Round Robin and First Come First Serve scheduling algorithms.
This document discusses the challenges cloud providers face in managing the performance of enterprise applications deployed in the cloud. It outlines how queuing models can be used to analyze application performance, identify bottlenecks, determine optimal resource allocation, and ensure performance meets SLAs. The key points are:
1) Cloud providers must monitor application workloads, characterize transactions and usage patterns, and plan capacity based on changing demands.
2) Queuing models can simulate application behavior under different workloads and help size resources needed to meet performance targets.
3) Both hardware and software bottlenecks must be identified and addressed, as insufficient tuning parameters can impact performance more than hardware capacity.
Performance Evaluation of a Network Using Simulation Tools or Packet TracerIOSRjournaljce
Today, the importance of information and accessing information is increasing rapidly. With the advancement of technology, one of the greatest means of achieving knowledge are, computers have entered in many areas of our lives. But the most important of them are the communication fields. This study will be a practical guide for understanding how to assemble and analyze various parameters in network performance evaluation and when designing a network what is necessary to looking for to remove the consequences of degrading performance. Therefore, what can you do in a network performance evaluation using simulation tools such as Network Simulation or Packet tracer and how various parameters can be brought together successfully? CCNA, CCNP, HCNA and HCNP educational level has been used and important setting has been simulated one by one. At the result this is a good guide for a local or wide area network. Finally, the performance issues precautions described. Considering the necessary parameters, imaginary networks were designed and evaluated both in CISCO Packet Tracer and Huawei's eNSP simulation program. But it should not be left unsaid that the networks have been designed and evaluated in free virtual environments, not in a real laboratory. Therefore, it is impossible to make actual performance appraisal and output as there is no actual data available.
A CLOUD BASED ARCHITECTURE FOR WORKING ON BIG DATA WITH WORKFLOW MANAGEMENTIJwest
In real environment there is a collection of many noisy and vague data, called Big Data. On the other hand,
to work on the data middleware have been developed and is now very widely used. The challenge of
working on Big Data is its processing and management. Here, integrated management system is required
to provide a solution for integrating data from multiple sensors and maximize the target success. This is in
situation that the system has constant time constrains for processing, and real-time decision-making
processes. A reliable data fusion model must meet this requirement and steadily let the user monitor data
stream. With widespread using of workflow interfaces, this requirement can be addressed. But, the work
with Big Data is also challenging. We provide a multi-agent cloud-based architecture for a higher vision to
solve this problem. This architecture provides the ability to Big Data Fusion using a workflow management
interface. The proposed system is capable of self-repair in the presence of risks and its risk is low.
Solving big data challenges for enterprise applicationTrieu Dao Minh
This document discusses the challenges of application performance monitoring (APM) systems that deal with "big data". APM systems instrument enterprise applications to monitor metrics like response times and failures across distributed systems. This generates enormous amounts of monitoring data. The document evaluates six open-source data stores (Cassandra, HBase, Voldemort, Redis, VoltDB, MySQL Cluster) for their ability to handle the throughput of APM workloads in memory-bound and disk-bound cluster setups. It aims to provide performance results, lessons learned on setup complexity, and insights for using these data stores in an industrial APM system context.
This document discusses performance testing, which determines how a system responds under different workloads. It defines key terms like response time and throughput. The performance testing process is outlined as identifying the test environment and criteria, planning tests, implementing the test design, executing tests, and analyzing results. Common metrics that are monitored include response time, throughput, CPU utilization, memory usage, network usage, and disk usage. Performance testing helps evaluate systems, identify bottlenecks, and ensure performance meets criteria before production.
GENERATIVE SCHEDULING OF EFFECTIVE MULTITASKING WORKLOADS FOR BIG-DATA ANALYT...IAEME Publication
This document proposes an evolutionary ordinal optimization (eOO) approach for scheduling dynamic and multitasking workloads for big data analytics in cloud computing environments. The eOO approach iteratively applies ordinal optimization to obtain suboptimal schedules faster than exhaustive searching, while adapting to workload fluctuations over time. Experimental results show the eOO approach achieves up to 30% higher task throughput compared to existing Monte Carlo and blind pick scheduling methods.
Performance Review of Zero Copy TechniquesCSCJournals
E-government and corporate servers will require higher performance and security as usage increases. Zero copy refers to a collection of techniques which reduce the number of copies of blocks of data in order to make data transfer more efficient. By avoiding redundant data copies, the consumption of memory and CPU resources are reduced, thereby improving performance of the server. To eliminate costly data copies between user space and kernel space or between two buffers in the kernel space, various schemes are used, such as memory remapping, shared buffers, and hardware support. However, the advantages are sometimes overestimated and new security issues arise. This paper describes different approaches to implementing zero copy and evaluates these methods for their performance and security considerations, to help when evaluating these techniques for use in e-government applications
A NOVEL SLOTTED ALLOCATION MECHANISM TO PROVIDE QOS FOR EDCF PROTOCOLIAEME Publication
The IEEE 802.11e EDCF mechanism cannot guarantee the QOS of high-priority traffic as the bandwidth consumption of the low-priority traffic increases. Also, in the presence of high priority traffic dampen link utilization of low priority traffic. To overcome these problems, we propose the Novel mechanism in our research that extends IEEE 802.11e EDCF by introducing a Super Slot and Virtual Collision. Compared to EDCF, our proposed approach has EDCF has two advantages: (a) Higher priority traffic achieves Quality of service regardless of the amount of low priority traffic, and (b) Low priority traffic obtains a higher throughput in the presence of same amount of high priority traffic.
Mc calley pserc_final_report_s35_special_protection_schemes_dec_2010_nm_nsrcNeil McNeill
This document provides a summary of a report on system protection schemes (SPS). It discusses SPS standards, practices, and advancements. It also examines relationships between SPS and other industries like process control and nuclear. The report proposes frameworks to identify risks to SPS from both a process and system view. It contributes methods to assess SPS operational complexity and incorporate this into transmission planning studies. The frameworks and models developed in this report can be applied to real utility systems to evaluate SPS reliability and impacts on the power grid.
This document discusses software rejuvenation techniques to address software aging in complex systems. It introduces the problem of performance degradation over long periods of usage due to data corruption, errors, and excessive resource usage. Software rejuvenation aims to clear these issues and prevent failures by optimizing the rejuvenation time based on variable workload. Two approaches are described: time-based periodic rejuvenation and closed-loop monitoring of system health to estimate resource exhaustion. The objectives are reducing failure rates, avoiding downtime, and improving availability. The methodology simulates rejuvenation using time and load balancing based on RAM utilization.
A Runtime Evaluation Methodology and Framework for Autonomic SystemsIDES Editor
An autonomic system provides self-adaptive ability
that enables system to dynamically adjust its behavior on
environmental changes or system failure. Fundamental
process of adaptive behavior in an autonomic system is consist
of monitoring system or/and environment information,
analyzing monitored information, planning adaptation policy
and executing selected policy. Evaluating system utility is
one of a significant part among them. We propose a novel
approach on evaluating autonomic system at runtime. Our
proposed method takes advantage of a goal model that has
been widely used at requirement elicitation phase to capture
system requirements. We suggest the state-based goal model
that is dynamically activated as the system state changes. In
addition, we defined type of constraints that can be used to
evaluate goal satisfaction level. We implemented a prototype
of autonomic computing software engine to verity our proposed
method. We simulated the behavior of the autonomic
computing engine with the home surveillance robot scenario
and observed the validity of our proposed method
This document introduces software rejuvenation techniques for complex systems. It discusses how software aging can degrade system performance over time due to resource exhaustion and error accumulation. Software rejuvenation proactively reboots systems to clear internal states and prevent failures. The document compares different rejuvenation policies and techniques, such as time-based approaches and approaches using workload monitoring. It also examines how rejuvenation affects virtual machines and discusses methods like cold restarts, warm suspends, and live migration. The goal of this project is to optimize rejuvenation times based on varying workloads to reduce downtime and improve system availability for complex environments.
High availability of data using Automatic Selection Algorithm (ASA) in distri...journalBEEI
High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.
This document discusses common performance testing mistakes and provides recommendations to avoid them. The five main "wrecking balls" that can ruin a performance testing project are: 1) lacking knowledge of the application under test, 2) not seeing the big picture and getting lost in details, 3) disregarding monitoring, 4) ignoring workload specification, and 5) overlooking software bottlenecks. The document emphasizes the importance of understanding the application, building a mental model to identify potential bottlenecks, and using monitoring to measure queues and resource utilization rather than just time-based metrics.
Oracle database performance diagnostics - before your beginHemant K Chitale
This is an article that I had written in 2011 for publication on OTN. It never did appear. So I am making it available here. It is not "slides" but is only 7 pages long. I hope you find it useful.
This document discusses using machine learning models to predict whether a loan applicant should be approved or not based on their application details. It first describes collecting past loan applicant data and comparing different machine learning models on this data. The most promising model is then trained on the data. New applicants' details are tested against the trained model to predict if they should be approved or not. Several machine learning methods are evaluated, including decision trees, random forests, support vector machines, linear models, neural networks and AdaBoost. Parameters for each model are also specified. The system is concluded to be an efficient way to help banks process loan applications while reducing risk.
Cloud computing is that ensuing generation of computation. In all probability folks can have everything they need on the cloud. Cloud computing provides resources to shopper on demand. The resources also are code package resources or hardware resources. Cloud computing architectures unit distributed, parallel and serves the requirements of multiple purchasers in various things. This distributed style deploys resources distributive to deliver services with efficiency to users in various geographical channels. Purchasers in a very distributed setting generate request haphazardly in any processor. So the most important disadvantage of this randomness is expounded to task assignment. The unequal task assignment to the processor creates imbalance i.e., variety of the processors sq. measure over laden and many of them unit of measurement to a lower place loaded. The target of load equalisation is to transfer the load from over laden technique to a lower place loaded technique transparently. Load equalisation is one altogether the central issues in cloud computing. To comprehend high performance, minimum interval and high resource utilization relation we want to transfer the tasks between nodes in cloud network. Load equalisation technique is utilized to distribute tasks from over loaded nodes to a lower place loaded or idle nodes. In following sections we have a tendency to tend to stand live discuss concerning cloud computing, load equalisation techniques and additionally the planned work of our load equalisation system. Proposed load equalisation rule is simulated on Cloud Analyst toolkit. Performance is analyzed on the parameters of overall interval, knowledge transfer, average knowledge center mating time and total value of usage. Results area unit compared with 3 existing load equalisation algorithms specifically spherical Robin, Equally unfold Current Execution Load, and Throttled. Results on the premise of case studies performed shows additional knowledge transfer with minimum interval.
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USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
DOI : 10.5121/ijaia.2020.11104 45
USING SEMI-SUPERVISED CLASSIFIER TO FORECAST
EXTREME CPU UTILIZATION
Nitin Khosla1
and Dharmendra Sharma2
1
Assistant Director- Performance Engineering, ICTCAPM, Dept. of Home Affairs,
Canberra, Australia
2
Professor – Computer Science, University of Canberra, Australia
ABSTRACT
A semi-supervised classifier is used in this paper is to investigate a model for forecasting unpredictable
load on the IT systems and to predict extreme CPU utilization in a complex enterprise environment with
large number of applications running concurrently. This proposed model forecasts the likelihood of a
scenario where extreme load of web traffic impacts the IT systems and this model predicts the CPU
utilization under extreme stress conditions. The enterprise IT environment consists of a large number of
applications running in a real time system. Load features are extracted while analysing an envelope of the
patterns of work-load traffic which are hidden in the transactional data of these applications. This method
simulates and generates synthetic workload demand patterns, run use-case high priority scenarios in a test
environment and use our model to predict the excessive CPU utilization under peak load conditions for
validation. Expectation Maximization classifier with forced-learning, attempts to extract and analyse the
parameters that can maximize the chances of the model after subsiding the unknown labels. As a result of
this model, likelihood of an excessive CPU utilization can be predicted in short duration as compared to
few days in a complex enterprise environment. Workload demand prediction and profiling has enormous
potential in optimizing usages of IT resources with minimal risk.
KEYWORDS
Semi-Supervised Learning, Performance Engineering, Load And Stress Testing, Machine Learning.
1. INTRODUCTION
The current cloud based environment is very dynamic in which the web traffic or number of hits
to some applications increases exponentially in a short span of time (burst in traffic) and it
drastically slows down the enterprise application system. At many occasions the IT system
crashes because it cannot sustain the excessive load under stress conditions. In enterprise
applications environment in big departments, some crucial applications providing services to the
public, e.g. benefits payments, custom clearances at airports, etc., halt suddenly. It is observed
that at many occasions the systems crash randomly due to unpredictable load because of
excessive web traffic for short period. This results in loss of efficiency and productivity of service
providers. Many at times, the system crashes without any alerts and practically it is not feasible to
take any remedial actions e.g. load balancing, etc. The high transaction-rate (excessive number of
hits / second) at some moment of time for a very short duration can drag the IT applications or the
computer systems to be very sluggish because the system becomes irresponsive and unable to
process large number of transactions simultaneously at different servers.
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
46
As we know, our reliance on cloud internet computing is increasing every day and it has become
unavoidable. Therefore, it has become very important for big enterprises to keep the key
applications running 24/7 to an acceptable efficiency during the whole year. Marinating the key
applications running at a high efficiency level in the big enterprise system and developing new
functionality at the same time is a constant challenge between functionality and resource
management [8]. T many instances it is observed that the system has arrived to a situation when
practically very negligible memory is available for the critical applications to run in an enterprise
set-up and this situation can lead to a system crash. The scenario becomes even more complex
when transactions are generated from wide area distributed networks where the network traffic,
latency and bandwidth are key factors impacting the performance and behaviour of IT
applications.
The main aim of this research paper is to develop and implement a practical approach to forecast
unpredictable burst in traffic by using semi-supervised neural nets classifier. To perform this we
have analysed the work-load patterns hidden in of the key transactions over a year and observed
CPU utilization under stress conditions (high volume of web traffic) using data mining
techniques.
2. RESEARCH PROBLEM
In this research, we have studied the load profiles of the last year to identify patterns at different
time periods. These patterns were analysed and used to develop test scenarios in the test
environment. We also analysed the big transactional data and extracted load patterns from the raw
transactions with the help of implementing profile points [2]. This enabled us to identify issues
related to load estimation in testing environment as well the real world (production)
environments. We then developed a performance predictive model to forecast the CPU
performance in enterprise IT infrastructure under extreme stress conditions.
2.1.Performance Issues
Computer applications are developed and based upon business specifications and these
specifications are primarily depend upon the user requirements [1]. We have noticed in our
department that there are some critical limitations in determining performance of applications, in
the current practices such as –
Not Reliable: Predicting system behaviour is not reliable e.g. response time, performance,
specially under a short burst traffic (hits) situation
Not Robust: Lack of a robust approach due to the volatile and unpredictable web traffic
Using Risk Based Approach: Performance testing (load and stress testing) is mainly done
using the key or crucial transactions which are considered as a high-risk to the
departments. Testing each and every scenario is in an enterprise applications environment
is extremely time consuming and costly [2]. So, load tests are generally performed on -
Key transactions which have critical impact on systems or services
Critical functions which could impact people or important services
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
47
3. FEATURE EXTRACTION AND DATA ANALYSIS IN LARGE ENTERPRISE
ENVIRONMENT
Big public departments or corporates comprises of different types of system architectures (latest
and legacy) e.g. mobile applications, cloud computing, etc. [1]. To cater most of the real work
scenarios, we have performed our experiments in a test environment which simulates a large and
complex environment having more than 350 servers and large number of which were distributed
across multiple sites (countries). This test environment (called as “pre-production” environment)
is a subset of the whole enterprise set-up with all applications integrated as per the specifications
and represent the most recent releases but with limited data set. This test environment also
represents a system simulating all the applications working in more than 52 overseas posts across
the world.
Raw transnational data was captured from data logs / files which were created at periodic
intervals. To perform the data collection, profile points are configured in the IT system
architecture at different layers which collects the data continuously at pre-defined time intervals
[1]. Different type of transactional data was captured e.g. % CPU utilization, transaction response
times, bandwidth, memory usages, etc. and used for analysis and debugging purposes.
Load and stress experiments for validation were done in the IT test environment (called as pre-
production environment) which represents the production environment. This test environment
emulates the real-world type of scenarios or behavior. Profile points are used to monitor the
transactions, responses times and other key parameters during the full path both ways (server to
client and client to server). The analysis of data, recognition of patterns are used and extremely
important for optimization [1]. This also helps to continuously improve the models to predict
reaching critical load while meeting the dynamic needs and variability of the dynamic load
patterns [12].
4. IDENTIFYING WORKLOAD PATTERNS
We have developed a trace-based approach to identify patterns of the CPU utilization of servers
over a period. For this we have captured transactions for over a year and collected relevant data
for the last one year. The profile points were configured at different threads and nodes of the
applications path flows and capture the data at regular pre-defined intervals. Then we have
studied the work load patterns of different transactions and their respective behaviour.
Workload patterns under stress conditions were quite typical and different from normal behaviour
of a CPU [1]. Signs of high CPU utilization can be predicted while simulating the virtual traffic in
test environment. The test environment also executes large number of applications simultaneously
as like real word scenario.
Assumption: it is assumed that CPU utilization follows a cyclic behaviour for some types of
transactions. These patterns can be represented by a time series consisting of a pattern and/or a
cyclical component [1],[2].
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
48
Figure 1. Different Load pattern of CPU Utilization, approx. 3000 – 4000 hits per minute.
Figure 1 shows the load patterns which follow a cyclic sequence. In the second graph, % CPU
suddenly drops from high usages (95%) to about 12% average. When it was about 95%, the
transaction response times were very high and the system was showing sluggishness during the
peak spikes. Data mining is done to capture two peak intervals where we observed a pattern,
reaching a higher CPU utilization for a longer duration of time, this clearly shows abnormal
behaviour of CPU utilization. We have also collected some data and did analytics on other
parameters e.g. hard disk usages, database hits, etc. and these can also provide insights from these
patterns for predictive modelling. In this paper, it is out of scope and will be investigated as an
extension to current work.
5. TRAINING WITH SEMI SUPERVISED LEARNING (SSL)
We have used an expected-maximization semi-supervised classifier to train our model. In this
approach we used labelled data with some amount of unlabelled data. This is used in conjunction
with a small amount of data can produce considerable improvement in learning accuracy over
unsupervised learning [5]. There are some advantages, in context to this research work, such as –
a) A scalable probabilistic approach
b) It can generate a model which simulates analogies of patterns based upon on different
profile data sets in a complex enterprise applications environment
c) Can achieve optimisation in terms of time and accuracy by predicting results
Considering some assumptions for the semi-supervised learning to work e.g. if two distinct points
d1, d2 are close enough, then there might be respective outputs b1, b2. If we do not consider these
assumptions, it would be hard to develop a practical model for a known number of training data
sets to predict a set of infinitely possible test-cases which are mainly unseen [16]. We also have
used other parameters in using labelled data points such as - effort, time, tools and resources.
Based upon the nature and potential implementation of this research, semi-supervised learning
with forced-training [3][7] may provide some useful outcomes as it is based upon –
a) Learning (training) of data set with both labelled and unlabelled data
b) Results are obtained in less time
c) Assumptions of forced-training can reduce the training time
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
49
6. EXPERIMENTS FOR VALIDATION
In our validation process, we simulated the load pattern showing burst in traffic in complex
enterprise test environment as we observed after collecting the data. This represented the real-
world scenario patterns representing different transactions. The test environment has limited data
(a sub-set of full data) which proportionally represents large data sets associated with the
integrated applications. More than 129 live applications fully functional as the real applications
environment. The process included –
i) Data collection using profile points and analysis. Data logs were created and extracted for a
very short intervals of 5 minutes
ii) Data extraction and analysis of the of workload demand patterns over a long period of time –
during last one year
iii) Generate synthetic workloads patterns
iv) Execute stress tests in test environment with large number of virtual users using system-
applications as in real world scenario
v) Validate the results by extracting data from different profile points of the application threads
and nodes on completion of the tests
vi) Training the model using semi-supervised learning approach (deep learning paradigm)
[7],[11].
vi) Forecast the likelihood of the traffic burst (excessive CPU usages) using the trained model
[4],[6].
6.1. Experimental Set-Up
We designed and configured the following experiment set-up to perform our experiments in the
test environment.
I) Virtual User Generator: used to simulate key end-user business processes and transactions
Ii) Controller: to manage, control and monitors the execution of load tests
Iii) Load Generators: to generate virtual load and simulate work-load patterns while large
number of virtual users generating web-traffic and exhibiting load patterns simulating web-traffic
bursts
Figure 2. A Typical Load Profile with Virtual Users
Figure 2 shows user work-load profile (stress conditions) with different ramp up (slopes) times.
This set up is used for validation of our results.
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
50
7. FORECASTING TRENDS
To study and analyse a trend in the load patterns we have worked out the aggregate demand
difference of each occurrence of the pattern from the original workload [15]. We used a modified
ETS (exponential smoothing) algorithm with ETS point predicts are equal to the medians of the
predict distributions.
Figure 3 (a) Semi-supervised v/s Supervised learning (1year data set)
Figure 3(b) CPU Utilization under burst of traffic load conditions
Figure 3(a) shows the results of a modified semi-supervised neural network model, which is used
to predict burst of traffic [9][10]. This model is now a part of our monitoring process in
continuous evaluation of the demand patterns, as shown in Figure 3(b). This model predicts the
burst of traffic behaviour and sets alarm for the system architects to take remedial actions e.g. re-
allocation of IT resources to avoid a system crash or failure.
8. CONCLUSION
We have developed and implemented a novel practical approach to predict burst in traffic
behaviour in a complex and highly integrated environment (test or pre-production) where more
than 130 IT applications were live and thousands virtual users generate user-load under stress
conditions. Our integrated enterprise environment had a distributed system with more than 300
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
51
servers serving more than 450 clients simultaneously. With a semi-supervised neural net
classifier, the proposed approach predicts and identifies the burst in traffic in a complex
enterprise IT infrastructure.
Data analytics enabled the system architects and system capacity planners to distribute the work-
load appropriately. The proposed practical approach helped the IT architects to mitigate the risk
of an unexpected failure of the IT systems, due to burst of traffic patterns, within a very short
duration of time (3 to 4 hours) compared to 1 - 2 weeks as in the current practice. Validation of
our results were done in an integrated test environment where alerts are activated as soon as the
collective CPU utilization of the server’s crosses 70% threshold critical limit. Experiments
performed in test environment validated that our approach to predict potential burst of traffic
worked effectively. In addition, we have found that this approach has benefited our department in
efficient management of IT resources and helped to plan IT capacity for future demand
predictions. This resulted in saving cost due to the optimum resource allocation in our IT
enterprise IT environment.
As further work, we are working on investigating the impact of different parameters e.g. hard -
disk failures, network latency [14], different types of transactions [13] and trying to develop a
hierarchical semi-supervised learning model to extract patterns and to design an accelerated semi-
supervised learning for predictive modelling.
REFERENCES
[1] Daniel Gmach, Jerry Rolia, Ludmila Cherkasova, Alfons Kemper, (2007) “Workload Analysis And
Demand Prediction Of Enterprise Data Center Applications”, IEEE 10th International Symposium On
Workload Characterization, Boston, USA.
[2] Jia Li, Andrew W. Moore, (2008) “Forecasting Web Page Views: Methods And Observations”,
Journal Of Machine Learning Research.
[3] Adams, R. P. And Ghahramani, Z. (2009) “Archipelago: Nonparametric Bayesian Semi-Supervised
Learning”, In Proceedings Of The International Conference On Machine Learning (ICML).
[4] H. Zhao, N. Ansari, (2012) “Wavelet Transform Based Network Traffic Prediction: A Fast Online
Approach”, Journal Of Computing And Information Technology, 20(1).
[5] Yuzong Liu, Katrin Krichhoff, (2013), “Graph Based Semi-Supervised Learning For Phone And
Segment Classification”, France.
[6] Danilo J Rezende, Shakir Mohamed, Daan Wierstra, (2014) “Stochastic Backpropagation And
Approximate Inference In Deep Generative Models”, Proceedings Of The 31st International
Conference On Machine Learning, Beijing, China.
[7] Diederik P. Kingma, Danilo J Rezende, Shakir Mohamad, Max Welling, (2014) “Semi-Supervised
Learning With Deep Generative Models”, Proceedings Of Neural Information Processing Systems
(NIPS), Cornell University, USA.
[8] Pitelis, N., Russell, C., And Agapito, L. (2014) “Semi-Supervised Learning Using An Unsupervised
Atlas”. In Proceedings Of The European Conference On Machine Learning (ECML), Volume LNCS
8725, Pages 565 –580.
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
52
[9] Kingma Diederik, Rezende Danilo, Mohamed Shakir, Welling M, (2014) “Semi-Supervised Learning
With Deep Generative Models”, Proceedings Of Neural Information Processing Systems (NIPS).
[10] L. Nie, D. Jiang, S. Yu, H. Song, (2017) “Network Traffic Prediction Based On Deep Belief Network
In Wireless Mesh Backbone Networks”, IEEE Wireless Communication And Networking
Conference, USA.
[11] Chao Yu, Dongxu Wang, Tianpei Yang, Et., (2018) “Adaptive Shaping Reinforcement Learning
Agents Vis Human Reward”, PRICAI Proceedings Part-1, Springer.
[12] Xishun Wang, Minjie Zhang, Fenghui Ren, (2018) “Deep RSD: A Deep Regression Method For
Sequential Data”, PRICAI Proceedings Part-1, Springer.
[13] Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, Et. (2018) “Realistic Evaluation Of
Semi-Supervised Learning Algorithms”, 6th International Conference On Learning Representations,
ICLR, Vancouver, BC, Canada.
[14] Kenndy John, Satran Michael, (2018) “Preventing Memory Leaks In Windows Applications”,
Microsoft Windows Documents.
[15] M.F. Iqbal, M.Z. Zahid, D. Habib, K. John, (2019) “Efficient Prediction Of Network Traffic For Real
Time Applications”, Journal Of Computer Networks And Communications.
[16] Verma. V, Lamb. A, Kannala. J, Bengio. Y, Paz DL, (2019) “Interpolation Consistency Training For
Semi Supervised Learning”, Proceedings Of 28th International Joint Conference On Artificial
Intelligence IJCAI Macao, China.
AUTHORS
Nitin Khosla Mr Khosla has worked about 15 years as Asst. Professor at MNIT in the Department of
Electronics and Communication Engineering before moving to Australia. He acquired
Master of Philosophy (Artificial Intelligence) from Australia, Master of Engineering
(Computer Technology) from AIT Bangkok and Bachelor of Engineering (Electronics)
from MNIT. His expertise is in Artificial Intelligence (neural nets), Software Quality
Assurance and IT Performance Engineering. Also, he is a Certified Quality Test
Engineer, Certified Project Manager and a Quality Lead Assessor. During last 14 years,
he worked in private and public services in New Zealand and Australia as a Senior
Consultant in Software Quality. Currently he is Asst. Director in Australian Federal
Government in Performance and Capacity Management and leading multiple IT
projects.