This document summarizes an article from the International Journal of Research in Advent Technology that proposes algorithms for energy-aware resource allocation in datacenters with minimized virtual machine migrations. It discusses how virtualization allows servers to be consolidated onto fewer physical machines to reduce hardware and power consumption. The algorithms aim to dynamically reallocate VMs according to current resource needs while ensuring quality of service and reliability, with the goal of minimizing the number of active physical nodes and switching idle nodes to a low-power state. It describes two proposed VM selection policies - the Minimum Migrations policy that selects the minimum number of VMs to migrate from overloaded hosts, and the Highest Potential Growth policy that migrates VMs with the lowest current CPU usage to prevent future
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
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AUTO RESOURCE MANAGEMENT TO ENHANCE RELIABILITY AND ENERGY CONSUMPTION IN HET...IJCNCJournal
A classic information processing has been replaced by cloud computing in more studies where cloud computing becomes more popular and growing than other computing models. Cloud computing works for providing on-demand services for users. Reliability and energy consumption are two hot challenges and tradeoffs problem in the cloud computing environment that requires accurate attention and research. This paper proposes an Auto Resource Management (ARM) scheme to enhance reliability by reducing the Service Level Agreement (SLA) violation and reduce energy consumed by cloud computing servers. In this context, the ARM consists of three compounds, they are static/dynamic threshold, virtual machine selection policy, and short prediction resource utilization method. The Minimum Utilization Non-Negative (MUN) virtual machine selection policy and Rate of Change (RoC) dynamic threshold present in this paper. Also, a method of choosing a value as the static threshold is proposed. To improve ARM performance, the paper proposes a Short Prediction Resource Utilization (SPRU) that aims to improve the process of decision making by including the resources utilization of future time and the current time. The output results show that SPRU enhanced the decision-making process for managing cloud computing resources and reduced energy consumption and the SLA violation. The proposed scheme tested under real workload data over the CloudSim simulator.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Cloud computing offers to users worldwide a low cost on-demand services, according to their requirements. In the recent years, the rapid growth and service quality of cloud computing has made it an attractive technology for different Tech Companies. However with the growing number of data centers resources, high levels of energy cost are being consumed with more carbon emissions in the air. For instance, the Google data center estimation of electric power consumption is equivalent to the energy requirement of a small sized city. Also, even if the virtualization of resources in cloud computing datacenters may reduce the number of physical machines and hardware equipments cost, it is still restrained by energy consumption issue. Energy efficiency has become a major concern for today’s cloud datacenter researchers, with a simultaneous improvement of the cloud service quality and reducing operation cost. This paper analyses and discusses the literature review of works related to the contribution of energy efficiency enhancement in cloud computing datacenters. The main objective is to have the best management of the involved physical machines which host the virtual ones in the cloud datacenters.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
AUTO RESOURCE MANAGEMENT TO ENHANCE RELIABILITY AND ENERGY CONSUMPTION IN HET...IJCNCJournal
A classic information processing has been replaced by cloud computing in more studies where cloud computing becomes more popular and growing than other computing models. Cloud computing works for providing on-demand services for users. Reliability and energy consumption are two hot challenges and tradeoffs problem in the cloud computing environment that requires accurate attention and research. This paper proposes an Auto Resource Management (ARM) scheme to enhance reliability by reducing the Service Level Agreement (SLA) violation and reduce energy consumed by cloud computing servers. In this context, the ARM consists of three compounds, they are static/dynamic threshold, virtual machine selection policy, and short prediction resource utilization method. The Minimum Utilization Non-Negative (MUN) virtual machine selection policy and Rate of Change (RoC) dynamic threshold present in this paper. Also, a method of choosing a value as the static threshold is proposed. To improve ARM performance, the paper proposes a Short Prediction Resource Utilization (SPRU) that aims to improve the process of decision making by including the resources utilization of future time and the current time. The output results show that SPRU enhanced the decision-making process for managing cloud computing resources and reduced energy consumption and the SLA violation. The proposed scheme tested under real workload data over the CloudSim simulator.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBR...Nexgen Technology
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Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
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Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES OF WORKLOADS NICELY AND IMPROVE THE OVERALL UTILIZATION OF SERVER RESOURCES.
WE DEVELOP A SET OF HEURISTICS THAT PREVENT OVERLOAD IN THE SYSTEM EFFECTIVELY WHILE SAVING ENERGY USED.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Load Balancing in Cloud Computing Environment: A Comparative Study of Service...Eswar Publications
Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. The
load balancing service is usually provided by dedicated software or hardware, such as a multilayer switch or a Domain Name System server. In this paper, the existing static algorithms used for simple cloud load balancing have been identified and also a hybrid algorithm for developments in the future is suggested.
COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBR...Nexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Enviro...SaikiranReddy Sama
In Dynamic Resource Allocation, WE PRESENT A SYSTEM THAT USES VIRTUALIZATION TECHNOLOGY TO ALLOCATE DATA CENTER RESOURCES DYNAMICALLY.
WE INTRODUCE THE CONCEPT OF “SKEWNESS”.
And BY MINIMIZING SKEWNESS, WE CAN COMBINE DIFFERENT TYPES OF WORKLOADS NICELY AND IMPROVE THE OVERALL UTILIZATION OF SERVER RESOURCES.
WE DEVELOP A SET OF HEURISTICS THAT PREVENT OVERLOAD IN THE SYSTEM EFFECTIVELY WHILE SAVING ENERGY USED.
Dynamic resource Allocation using Virtual Machines For Cloud Computing
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...ijccsa
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
KEYWORDS
Energy Consumption, Virtual Machine Placement, Harmony Search Algorithm, Server Consolidati
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...neirew J
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
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Optimizing the placement of cloud data center in virtualized environmentIJECEIAES
In cloud mobile networks, precise assessment for the position of the virtualization powered cloud center would improve the capacity limit, latency and energy efficiency (EEf). This paper utilized the Monte Carlo oriented particle swarm optimization (PSO) and genetic algorithm (GA) to first, obtain the optimal number of virtual machines (VMs) that maximize the EEf of the mobile cloud center, second, optimize the position of the mobile data center. To fulfil such examination, a power evaluation framework is proposed to shape the power utilization of a virtualized server while hosting an amount of VMs. In addition, the total power consumption of the network is examined, including data center and radio units (RUs). This evaluation is based on linear modelling of the network parameters, such as resource blocks, number of VMs, transmitted and received powers, and overhead power consumption. Finally, the EEf is constrained to many quality of service (QoS) metrics, including number of resource blocks, total latency and minimum user's data rate.
PROPOSED LOAD BALANCING ALGORITHM TO REDUCE RESPONSE TIME AND PROCESSING TIME...IJCNCJournal
Cloud computing is a new technology that brings new challenges to all organizations around the world.
Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As
for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development
of the scale and number of applications, this access is often threatened by overload. Therefore, this paper
our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud
computing to improve performance for end-user. We have simulated the proposed algorithm with the
CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the
cloud data center.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine P...IJECEIAES
With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the 퐾-Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
A hybrid algorithm to reduce energy consumption management in cloud data centersIJECEIAES
There are several physical data centers in cloud environment with hundreds or thousands of computers. Virtualization is the key technology to make cloud computing feasible. It separates virtual machines in a way that each of these so-called virtualized machines can be configured on a number of hosts according to the type of user application. It is also possible to dynamically alter the allocated resources of a virtual machine. Different methods of energy saving in data centers can be divided into three general categories: 1) methods based on load balancing of resources; 2) using hardware facilities for scheduling; 3) considering thermal characteristics of the environment. This paper focuses on load balancing methods as they act dynamically because of their dependence on the current behavior of system. By taking a detailed look on previous methods, we provide a hybrid method which enables us to save energy through finding a suitable configuration for virtual machines placement and considering special features of virtual environments for scheduling and balancing dynamic loads by live migration method.
Power consumption prediction in cloud data center using machine learningIJECEIAES
The flourishing development of the cloud computing paradigm provides several ser- vices in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.
A load balancing strategy for reducing data loss risk on cloud using remodif...IJECEIAES
Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
1. International Journal of Research in Advent Technology, Vol.4, No.1, January 2016
E-ISSN: 2321-9637
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Energy-aware Datacenter Resource Allocation
with Minimized Virtual Machine Migrations
Saravana M K1
, Harish K2
Assistant Professor, Department of Computer Science & Engg, Jyothy Institue of Technology, Bangalore.1, 2
Email: mksaravanamk1@gmail.com
1
, kharish1987gmail.com
2
Abstract- In recent years, IT infrastructures continue to grow rapidly driven by the demand for computational
power created by modern compute-intensive business and scientific applications. However, a large-scale
computing infrastructure consumes enormous amounts of electrical power leading to operational costs that
exceed the cost of the infrastructure in few years. Except for overwhelming operational costs, high power
consumption results in reduced system reliability and devices lifetime due to overheating. Another problem is
significant CO2 emissions that contribute to the greenhouse effect. One of the way to reduce power consumption
by a data center is to apply virtualization technology. This technology allows one to consolidate several servers
to one physical node as Virtual Machines (VMs) reducing the amount of the hardware in use. Recently emerged
Cloud computing paradigm leverages virtualization and provides on-demand resource provisioning over the
Internet on a pay-as-you-go basis. This allows enterprises to drop the costs of maintenance of their own
computing environment and out-source the computational needs to the Cloud. It is essential for Cloud providers
to offer reliable Quality of Service (QoS) for the customers that is negotiated in terms of Service Level
Agreements (SLA), e.g. throughput, response time. Therefore, to ensure efficient resource management and
provide higher utilization of resources, Cloud providers (e.g. Amazon EC2) have to deal with power-
performance trade-off, as aggressive consolidation of VMs can lead to performance loss. In this work we
leverage live migration of VMs and propose heuristics for dynamic reallocation of VMs according to current
resources requirements, while ensuring reliable QoS. The objective of the reallocation is to minimize the number
of physical nodes serving current workload, whereas idle nodes are switched off in order to decrease power
consumption. A lot of research has been done in power efficient resource management in data centers. In contrast
to previous studies, the proposed approach can effectively handle strict QoS requirements, heterogeneous
infrastructure and heterogeneous VMs. The algorithms are implemented as fast heuristics, they do not depend on
a par-ticular type of workload and do not require any knowledge about applications executing on VMs.
Index Terms- VM Migration, Cloud Datacenter, VM Allocation, QoS.
1. INTRODUCTION
Virtual machines consolidation aims at reducing the
number of active physical servers in a data centre with
the goal to reduce the total power consumption. In this
context, most of the existing solutions rely on
aggressive virtual machine migration, thus resulting in
unnecessary overhead and energy wastage. This article
presents a virtual machine consolidation algorithm
with usage prediction (VMCPU) for improving the
energy efficiency of cloud data centers. Our algorithm
is executed during the virtual machine consolidation
process to estimate the short term future CPU
utilization based on the local history of the the
considered servers. The joint use of current and
predicted CPU utilization metrics allows a reliable
characterization of overloaded and underloaded
servers, thereby reducing both the load and the power
consumption after consolidation. We evaluate our
proposed solution through simulations on Cloudsim
Simulator. In comparison with the state of the art, the
obtained results show that consolidation with usage
prediction reduces the total migrations and the power
consumption of the servers while complying with the
service level agreement. Minimizing the use of
energy/network communication overhead with
maximum resource utilization in large DCs is a
challenging task as computing applications and data
are growing so quickly for which increasing larger
servers and disks are required to process them fast
enough within the required time period. In order to
minimize network load and maximize resource
utilization in cloud DCs, we simulated our proposed
network load-aware scheduling algorithm that ensures
minimum VMs migration while delivering the
negotiated Quality-of-Service (QoS).
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2. LITERATURE SURVEY
Farahnakian, Ashraf , Pahikkala [1]. presented a novel
dynamic Virtual Machine consolidation approach
called ACS-based VMConsolidation. It reduces the
energy consumption of datacenters by consolidating
VMs into a reduced number of active Physical
Machines while preserving Quality of Service
requirements. Since the VM consolidation problem is
strictly NP-hard, they used the Ant Colony System to
find a near-optimal solution. We defined a multi-
objective function that considers both the number of
dormant PMs and the number of migrations. When
compared to the existing dynamic VM consolidation
approaches, ACS-VMC not only reduced the energy
consumption, but also minimized SLA violations and
the number of migrations. They evaluated the
performance of proposed approach by conducting
experiments with ten different real workload traces.
Shahzad,.Umer, Nazir, [2] developed an efficient load
balancing algorithm by using scheduling to minimize
VMs migration, which avoid different network
performance parameters like congestion, latency etc.
There work categories VMs by showing priorities of
the VMs, which will help to reduce network overhead.
Zhao, Lu [3] developed online VM placement
algorithms to increase cloud provider’s revenue by
reducing SLA violation cost. First-Fit and Harmonic
algorithms are devised without considering VM
migrations, while LRF and DDG are devised for VM
migration considering VM migrations. There analysis
shows that First Fit and Harmonic perform the same in
their worst case, and is comparable to the ratio of
lower bound to upper bound. They have conduct
experiments to evaluate the algorithms using synthetic
and real data, respectively. It is found that Harmonic
could create more revenue than First-Fit by more than
10 percent when job arriving rate is greater than 1.0.
DDG algorithm is applicable in scenarios when SLA
penalty is high, job arriving rate is low and the
migration cost is low, while LRF performs better in
the opposite situations. Through evaluation on the real
trace from TJUHPCC, they find that First-Fit could
yield more revenue than Harmonic by 1.7 percent if
migration is not allowed, and DDG creates more
revenue than LRF by 1.23percent if migration is
allowed. By comparing proposals against the
algorithms adopted in Open-stack and Cloudstack,
they find that FirstFit and DDGcould create more
revenue in the TJUHPCC case. There algorithms
currently could take effect in IaaS systems, where VM
requests are not constrained by their communications.
Dhanoa & Khurmi, [4] have analyzed the impact of
VM size and network bandwidth on VM migration
time and energy consumption of the source system.
Variation in VM size and network bandwidth results a
significant impact on energy consumption of source
system during VM Live migration. Further we can
reduce energy consumption and migration time of
subsystems by selecting VM with least memory size
for migration and increased network bandwidth.
Results of this study would help to design algorithm to
optimize energy requirements in live migration of
VMs. Live migration feature of Virtualization has
great potential to optimize energy efficiency during
live migration.
Vahora & Patel [5] Presents VM management
technique for efficient utilization of resources which
leads to reduce energy consumption and number of
VM migration in virtualized data centers. There are
number of research has been carried out in this
subject, some are practical based and some are
simulator-based. By analyzing related work on this
subject, they found that it is critical and essential to
handle three main things: first, how to allocate VM on
host such that it is not over- loaded, second, which
VM should be selected for migration from overloading
hosts, third where to place (Reallocate) VM which is
selected for migration. For this we have to balance the
load such that host is not over loaded or under loaded
with the requests. If host is over loaded one or more
number of VM migrated from the host and if host is
under loaded resources are not properly utilized which
leads to unnecessary energy consumed by them. So,
proper management of VM is necessary which is done
by the algorithm and experiments shown that VM
management techniques proposed by them performs
greatly better than previous work on the whole.
3. PRELIMINARIES
Recent developments in virtualization have resulted in
its proliferation across datacenters. By supporting the
movement of VMs between physical nodes, it enables
dynamic migration of VMs according to the
performance requirements. When VMs do not use all
the provided resources, they can be logically resized
and consolidated to the minimum number of physical
nodes, while idle nodes can be switched to the sleep
mode to eliminate the idle Power consumption and
reduce the total energy consumption by the datacenter.
Currently, resource allocation in a Cloud data center
aims to provide high performance while meeting
SLAs, without focusing on allocating VMs to
minimize energy consumption. To explore both
performance and energy efficiency, three crucial
issues must be addressed. First, excessive power
cycling of a server could reduce its reliability. Second,
turning resources off in a dynamic environment is
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75
risky from the QoS perspective. Due to the variability
of the workload and aggressive consolidation, some
VMs may not obtain required resources under peak
load, and fail to meet the desired QoS. Third, ensuring
SLAs brings challenges to accurate application
performance management in virtualized environments.
All these issues require effective consolidation
policies that can minimize energy consumption
without compromising the user-specified QoS
requirements.
4. VM PLACEMENT
The problem of VM allocation can be divided in two
parts: the first part is the admission of new requests for
VM provisioning and placing the VMs on hosts,
whereas the second part is the optimization of the
current VM allocation. The first part can be seen as a
bin packing problem with variable bin sizes and
prices.
To solve it we apply a variant of the Best Fit
Decreasing (BFD) algorithm that is shown to use no
more than 11/9· OPT+ 1 bins (where OPT is the
number of bins given by the optimal solution) [31]. In
our modification, the Modified Best Fit Decreasing
(MBFD) algorithms, we sort all VMs in decreasing
order of their current CPU utilizations, and allocate
each VM to a host that provides the least increase of
power consumption due to this allocation. This allows
leveraging the heterogeneity of resources by choosing
the most power-efficient nodes first. The pseudo-code
for the algorithm is presented in Algorithm1.The
complexity of the allocation part of the algorithm is n .
m, where n is the number of VMs that have to be
allocated and m is the number of hosts.
Algorithm 1: Improved Best Fit Decreasing (IBFD)
1 Input: hostList, vmList Output: allocation of VMs
2 vmList.sortDecreasingUtilization()
3 foreach vm in vmList do
4 minPower←MAX
5 allocated Host←NULL
6 for each host in hostList do
7 if host has enough resource for vm then
8 power←estimatePower(host, vm)
9 if power < minpower then
10 allocatedHost←host
11 minPower←power
12 if allocated Host ≠ NULL then
13 allocate vm to allocated Host
14 return allocation
4.1. VM Selection
The optimization of the current VM allocation is
carried out in two steps: at the first step we select VMs
that need to be migrated, at the second step the chosen
VMs are placed on the hosts using the IBFD
algorithm. To determine when and which VMs should
be migrated, we introduce three double-threshold VM
selection policies. The basic idea is to set upper and
lower utilization.
Thresholds for hosts and keep the total utilization of
the CPU by all the VMs allocated to the host between
these thresholds. If the CPU utilization of a host falls
below the lower threshold ,all VMs have to be
migrated from this host and the host has to be
switched to the sleep mode in order to eliminate the
idle power consumption.
If the utilization exceeds the upper threshold, some
VMs have to be migrated from the host to reduce the
utilization. The aim is to preserve free resources in
order to prevent SLA violations due to the
consolidation in cases when the utilization by VMs
increases.
The difference between the old and new placements
forms a set of VMs that have to be reallocated. The
new placement is achieved using live migration of
VMs. In the following sections we discuss the
proposed VM selection policies.
4.1.1 The minimization of migrations policy
The Minimum Migrations (MM) policy selects the
minimum number of VMs needed to migrate from a
host to lower the CPU utilization below the upper
utilization threshold if the upper Threshold is violated.
Let Vj be a set of VMs currently allocated to the host
j.Then P(Vj) is the powerset of Vj. The MM policy
finds a set R∈P (Vj) defined in (3).
R=
ە
ۖ
۔
ۖ
ۓ ቊ
ܵ|ܵƐܲ ൫ݒ൯ݑ − ∑ ݑ()ݒ < ܶ௨,௩Ɛௌ
|ܵ| → ݉݅݊} ݂݅ݑ > ܶ௨
ܸ, ݂݅ ݑ > ܶ
߶, ݐℎ݁݁ݏ݅ݓݎ
(3)
Where Tu is the upper utilization threshold; Tl is the
lower utilization threshold, uj is the current CPU
utilization of the host j; and ua(v) is the fraction of the
CPU utilization allocated to the VMv. The pseudo-
code for the MM algorithm for the over-utilization
case is presented in Algorithm 2. The algorithm sorts
the list of VMs in the decreasing order of the CPU
utilization. Then, it repeatedly looks through the list of
VMs and finds a VM that is the best to migrate from
the host. The best VM is the one that satisfies two
conditions. First, the VM should have the utilization
higher than the difference between the host’s overall
utilization and the upper utilization threshold. Second,
if the VM is migrated from the host, the difference
between the upper threshold and the new utilization is
the minimum across the values provided by all the
4. International Journal of Research in Advent Technology, Vol.4, No.1, January 2016
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76
VMs. If there is no such a VM, the algorithm selects
the VM with the highest utilization, removes it from
the list of VMs, and proceeds to a new iteration. The
algorithm stops when the new utilization of the host is
below the upper utilization threshold. The complexity
of the algorithm is proportional to the product of the
number of over-utilized hosts and the number of VMs
allocated to these hosts.
Algorithm 2: Min Migrations (MM)
1 Input: hos tList Output: migration List
2 foreach h in host List do
3 vmList←h.get VmList()
4 vmList.sort DecreasingUtilization()
5 hUtil←h.getUtil()
6 bestFitUtil←MAX
7 while hUtil>THRESH_UP do
8 foreach vm in vmList do
9 if vm.getUtil()>hUtil−THRESH_UP then
10 t←vm.getUtil()−hUtil + THRESH_UP
11 if t < bestFitUtil then
12 bestFitUtil←t
13 bestFitVm←vm
14 else
15 if bestFitUtil=MAX then
16 bestFitVm←vm
17 break
18 hUtil←hUtil−bestFitVm.getUtil()
19 migrationList.add(bestFitVm)
20 vmList.remove(bestFitVm)
21 if hUtil<THRESH_LOW then
22 migrationList.add(h.getVmList())
23 vmList.remove(h.getVmList())
24 return migrationList
4.1.2. The highest potential growth policy
When the upper threshold is violated, the Highest
Potential Growth (HPG) policy migrates VMs that
have the lowest usage of the CPU relatively to the
CPU capacity defined by the VM parameters in order
to minimize the potential increase of the host’s
utilization and prevent an SLA violation, as
formalized in (4)
R=
ە
ۖ
۔
ۖ
ۓ ൝
ܵ|ܵ Ɛߩ ൫ݒ൯ݑ − ∑ ݑ < ܶݒ௨௩Ɛௌ
∑
௨ೌ(௩)
௨ೝ(௩)
→ ݉݅݊}, ݂݅ ݑ > ܶ௨; (4)௩ఌௌ
ܸ, ݂݅ ݑ > ܶ
߶, ݐℎ݁݁ݏ݅ݓݎ
where ur(v) is the fraction of the CPU capacity
initially requested for the VMv and defined as the
VM’s parameter. we do not provide the pseudo-code
for the HPG algorithm, as it is similar to the MM
algorithm presented earlier.
4.1.3. The random choice policy
The Random Choice (RC) policy relies on ar and om
selection of a number of VMs needed to decrease the
CPU utilization by a host below the upper utilization
threshold. According to a uniformly distributed
discrete random variable(X), whose values index
subsets of Vj, the policy selects a set R∈P (Vj),as
shown in (5).
R=
ە
ۖ
۔
ۖ
ۓ
ቐ
ܵ|ܵ Ɛܲ ൫ݒ൯, ݑ − ∑ ݑ()ݒ < ܶ௨௩Ɛௌ
ܺ =ฎ
ௗ
ܷ൫0, หܲ൫0, หܲ൫ܸ൯ห − 1൯ൟ, ݂݅ ݑ > ܶ௨
ܸ, ݂݅ ݑ > ܶ
߶, ݐℎ݁݁ݏ݅ݓݎ
(5)
Where X is a uniformly distributed discrete random
variable used to select a subset of Vj. The results of a
simulation-based evaluation of the proposed
algorithms in terms of power consumption, SLA
violations and the number of VM migrations are
presented in Section 5.
5. EXPERIMENTS AND RESULTS
In this section, we discuss a performance analysis of
the energy-aware allocation heuristics presented in
Section 4. We have conducted our experiments on
cloudSim Simulator , we calculate the time needed to
perform a live migration of a VM as the size of its
memory divided by the available network bandwidth.
For the simulations, the utilization of the CPU by a
VM is generated as a uniformly distributed random
variable. This is appropriate due to unknown types of
applications running on VMs, and as it is not possible
to build the exact model of such a mixed workload.
We have simulated a data center comprising 100
heterogeneous physical nodes. Each node is modeled
to have one CPU core with the performance equivalent
to1000, 2000 or 3000MIPS, 8GB Of RAM and 1TB of
storage.
A host consumes from 175W with 0% CPU
utilization, up to 250W with 100% CPU utilization.
Each VM requires one CPU core with 250, 500, 750
or 1000 MIPS, 128 MB of RAM and 1GB of storage.
The users submit requests for provisioning of 290
Heterogeneous VMs that fill the full capacity of the
simulated data center. Each VM runs a web-
application or any kind of application with variable
workload, which is modeled to generate the utilization
of CPU according to a uniformly distributed random
variable.
The application runsfor150,000 MI that is equal to 10
min of the execution on 250MIPS CPU with 100%
utilization. Initially, the VMs are allocated according
to the requested characteristics assuming 100% CPU
utilization. Each experiment has been run 10 times.
5.1. Performance metrics
5. International Journal of Research in Advent Technology, Vol.4, No.1, January 2016
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In order to compare the efficiency of the algorithms
we use several metrics to evaluate their performance.
The first metric is the total energy consumption by the
physical resources of a data center caused by the
application workloads. The second performance
metric is called the SLA violation percentage, or
simply the SLA violations, which is defined as the
percentage of SLA violation events relatively to the
total number of the processed timeframes. We define
that an SLA violation occurs when a given VM cannot
get the amount of Million Instructions Per Second
(MIPS) that are requested.
5.2. Simulation Results
For the benchmark experimental results we have used
a VM migration aware policy called Single Threshold
(ST). It is based on the idea of setting the upper
utilization threshold for hosts and placing VMs, while
keeping the total utilization of CPU below this
threshold. At each time frame all VMs are reallocated
using the IBFD algorithm with additional condition of
keeping the upper utilization threshold not violated.
To evaluate the ST policy we have conducted several
experiments with different values of the utilization
threshold. The simulation results are presented in
Fig.2.
Fig.2. The energy consumption and SLA violations by
the ST plicy
The results show that energy consumption can be
significantly reduced relatively to the NPA and DVFS
policies by77% and 53% respectively with 5.4% of
SLA violations. The results show that with the growth
of the utilization threshold energy consumption
decreases, whereas the percentage of SLA violations
increases. This is due to the fact that a higher
utilization threshold allows more aggressive
consolidation of VMs by the cost of the increased risk
of SLA violations.
To evaluate the double-threshold policies it is
necessary to determine the best values for the
thresholds in terms of the energy consumption and
QoS delivered. We have chosen the MM policy to
conduct the analysis of the utilization thresholds. We
have simulated the MM policy varying the absolute
values of the lower and upper thresholds as well as the
interval between them. The results showing the mean
energy consumption achieved using the MM policy
for different values of the lower utilization.
Threshold and the interval between the thresholds are
presented in Fig.3. The graph shows that an increase
of the lower utilization threshold leads to decreased
energy consumption. However, the low level of
energy consumption can be achieved with different
intervals between the thresholds. Therefore, to
determine the best interval we have to consider
another factor, the level of SLA violations.
Fig 3.The mean energy consumption by the MM
policy for different values of the utilization thresholds.
6. CONCLUSION AND DISCUSSION
In this article, we address the VM consolidation
problem by adopting CPU usage prediction. Our aim
was to reduce the frequency of the number of VM
migrations and the number of server switches in order
to save energy. To this end, based on a resource
prediction scheme, we proposed a consolidation with
usage prediction algorithm for energy efficient cloud
data centers. The proposed algorithm effectively
reduces not only the number of migrations, the
number of power state changes and the energy
consumption of the servers, but also the average
number of SLA violations. The simulation results has
shown that the proposed approach can significantly
decrease the energy consumption that results from VM
migrations and host switches with a better compliance
with the SLA. As a future work, we seek to evaluate
the performance of the proposed algorithm across
multiple resource dimensions.
REFERENCES
[1] Using Ant Colony System to Consolidate VMs for Green
Cloud Computing
[2] Reduced VM Migration in bandwidth over subscribed
cloud data Centers
[3] Online Virtual Machine Placement for Increasing
Cloud Provider’s Revenue.
6. International Journal of Research in Advent Technology, Vol.4, No.1, January 2016
E-ISSN: 2321-9637
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