Service level agreement based energy-efficient resource management in cloud data centers

Gao, Yongqiang; Guan, Haibing; Qi, Zhengwei; Song, Tao; Huan, Fei; Liu, Liang

doi:10.1016/j.compeleceng.2013.11.001

Cited by 69 publications

(25 citation statements)

References 20 publications

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“…Because it allows VMs on hosts to migrate to other suitable hosts when the work load of hosts is low, and the hosts that have become idle to switch to sleep mode [5].…”

Section: Introductionmentioning

confidence: 99%

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An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Nikzad¹,

Alavi²,

Soltanaghaei³

2016

ijacsa

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Abstract-Nowadays, as the use of cloud computing service becomes more extensive and the customers welcome this service, an increasing trend in energy consumption and operational costs of these centers may be seen. To reduce operational costs, the providers should decrease energy consumption to an extent that Service Level Agreement (SLA) maintains at a desirable level. This paper adopts the virtual machine consolidation problem in cloud computing data centers as a solution to achieve this goal, putting forward solutions to make the decision regarding the necessity of migration from hosts and finding appropriate hosts as destinations of migration. Using time-series forecasting method and Double Exponential Smoothing (DES) technique, the proposed algorithm predicts CPU utilization in near future. It also proposes an optimal equation for the dynamic lower threshold. Comparing current and predicted CPU utilization with dynamic upper and lower thresholds, this algorithm identifies and categorizes underloaded and overloaded hosts. According to this categorization, migration then occurs from the hosts that meet the necessary conditions for migration. This paper identifies a certain type of hosts as "troublemaker hosts". Most probably, the process of prediction and decision making regarding the necessity of migration will be disrupted in the case of these hosts. Upon encountering this type of hosts, the algorithm adopts policies to modify them or switch them to sleep mode, thereby preventing the adverse effects caused by their existence. The researchers excluded all overloaded, prone-to-beoverloaded, underloaded, and prone-to-be-underloaded hosts from the list of suitable hosts to find suitable hosts as destinations of migration. An average improvement of 86.2%, 28.4%, and 87.2% respectively for the number of migrations of virtual machines, energy consumption, and SLA violation is among the simulation achievements of this algorithm using Clouds tool.

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“…Because it allows VMs on hosts to migrate to other suitable hosts when the work load of hosts is low, and the hosts that have become idle to switch to sleep mode [5].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in light of the ever increasing expansion of the use of cloud computing services and due to the fact that customers welcomed this service, cloud computing service providers have increased the number and volume of greedy data centres that consume huge amounts of energy [5]. This has incurred enormous operational costs.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Nikzad¹,

Alavi²,

Soltanaghaei³

2016

ijacsa

View full text Add to dashboard Cite

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“…They ensure that the Quality of Service (QoS) requirements are guaranteed before any energy optimization is done. Similarly in [29] Gao et al use the reinforcement learning techniques for VM consolidation and placement. Gupta et al [30] propose a resource management scheme which combines dynamic voltage/frequency scaling and server consolidation to achieve energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

2016

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Abstract:A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called 'active network subset' that satisfies the existing network traffic conditions while switching off the residual unused network components. A 'passive network subset' is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%-70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision.

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“… Cost: the cost that each client pays for the time use of VM.  Throughput: the number of task done in the unit of the time [11].  Utility: the duration of task execution in each of VM in proportion to time of total execution.…”

Section: B Dependent Parameters In Schedulingmentioning

confidence: 99%

Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey

Soltani¹,

Soleimani²,

Barekatain³

2017

IJCNIS

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Abstract-Cloud computing became so important due to virtualization and IT systems in this decade. It has introduced as a distributed and heterogeneous computing pattern to sharing resources. Task Scheduling is necessary to make high performance heterogeneous computing. The optimization of related parameters, and using heuristic and meta-heuristic algorithms can lead to a reduction of the search space complexity and execution time. So, several studies have tried using a variety of algorithms to solve this issue and improve relative efficiency in their environments. This paper considered examines existing heuristic task scheduling algorithms. First, the concepts of scheduling, the layer of cloud computing, especially scheduling concept in the SaaS and PaaS layer, the main limits for improving the quality of service, evaluation methods of algorithms and applied tools for evaluating these ideas and practical experimental used methods were discussed and compared. Finally, future works in this area were also concluded and a summary of this article is presented in the form of a mind map.

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Service level agreement based energy-efficient resource management in cloud data centers

Cited by 69 publications

References 20 publications

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey

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