Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers

Arianyan, Ehsan; Taheri, Hassan; Sharifian, Saeed

doi:10.1016/j.compeleceng.2015.05.006

Cited by 104 publications

(81 citation statements)

References 19 publications

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“…Arianyan et al [17] proposed a holistic resource management procedure and a heuristic intelligent technology method based on multi-criteria decision-making method to determine underloaded hosts for placement of migratable VMs. They presented a multi-criteria method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) by focusing their work on methods for determining the time to consider a host as underloaded and by finding a new location for placement of the VMs selected among underloaded and overloaded hosts.…”

Section: IImentioning

confidence: 99%

“…In most research studies conducted in this area [6,8,[17][18][19][20][21], the necessary decisions are made based on current utilization of hosts and VMs are immediately migrated from hosts which researchers currently identify them as overloaded [8,15]. The proposed algorithm in the present work attempts to predict CPU utilization using Time-Series Method and SES technique and identifies a VM as overloaded or underloaded based on current and predicted utilization values and their comparison with dynamic upper and lower thresholds.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Najari¹,

Alavi²,

Noorimehr³

2016

ijacsa

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Abstract-The present study aims at recognizing the problem of dynamic virtual machine (VM) Consolidation using virtualization, live migration of VMs from underloaded and overloaded hosts and switching idle nodes to the sleep mode as a very effective approach for utilizing resources and accessing energy efficient cloud computing data centres. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level.

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Section: IImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Najari¹,

Alavi²,

Noorimehr³

2016

ijacsa

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“…In [6], the authors proposed policies to determine underloaded hosts as well as a policy for the placement of migratable VMs, where they used a multi-criteria decision making technique. They also put forward a novel and comprehensive procedure for cloud resource management called Enhanced Optimization (EO) that offers an allembracing outlook on the resource management procedure.…”

Section: Previous Researchmentioning

confidence: 99%

“…Research has shown that although the power consumed by hardware equipment is idle, it is almost equal to that at the peak of consumption. Thus, failure to utilize them in a perfect manner will result in a huge energy loss [6]. In this respect, Forrester research team observed that when a server is idle for 70% of the time, it consumes a power of almost 30% of the consumption peak power [7]; therefore, what mainly accounts for energy loss in cloud computing data centres is the use of equipment while their utilization is at low levels [6].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, failure to utilize them in a perfect manner will result in a huge energy loss [6]. In this respect, Forrester research team observed that when a server is idle for 70% of the time, it consumes a power of almost 30% of the consumption peak power [7]; therefore, what mainly accounts for energy loss in cloud computing data centres is the use of equipment while their utilization is at low levels [6]. Virtualization, is the key feature and main basis of cloud computing, making possible the establishment of several VMs on a host as well as the migration of VMs [8].…”

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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Optimization of checkpointing/recovery strategy in cloud computing with adaptive storage management

Meroufel

Belalem

2018

Concurrency and Computation

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Summary Cloud Computing is a type of distributed system that is usually based on the services offered to the user based on SLA contract. In this case, the implementation of a fault‐tolerant system that ensures the reliability and the services continuity becomes a major requirement. In this paper, we propose a fault tolerance strategy based on checkpointing and replication. Our approach uses a smart checkpoint infrastructure for cloud computing tasks. The checkpoints are stored in alternative already paid VMs. This allows resuming a task execution faster and cheaper after a node crash. Since checkpoints are distributed and replicated, our approach increases also the system reliability. The experimental results show the effectiveness of the proposed strategy in term of energy consumption, SLA (System Level Aggregation) violation, and reliability.

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Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers

Cited by 104 publications

References 19 publications

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

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

Optimization of checkpointing/recovery strategy in cloud computing with adaptive storage management

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