A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

Sayadnavard, Monireh H.; Haghighat, Abolfazl Toroghi; Rahmani, Amir Masoud

doi:10.1007/s11227-018-2709-7

Cited by 45 publications

(27 citation statements)

References 31 publications

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“…This paper also combined an ant colony system with new heuristics to present a solution for energy optimization problem but did not consider SLA violation in evaluation metrics. Paper 41 presented a failure-aware method for placing virtual machines. This paper used two criteria of energy and reliability to score servers.…”

Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE-k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi-heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well-known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

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Section: Physical Machine Selection (Vm Placement) Methodsmentioning

confidence: 99%

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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“…There are several works in the literature about scalability [ 5 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, they tend to focus on mechanisms that contribute to infrastructure robustness [ 14 ] since carrying out economic management of resources directly impacts the service’s quality.…”

Section: Related Workmentioning

confidence: 99%

MoHRiPA—An Architecture for Hybrid Resources Management of Private Cloud Environments

Andreazi

Estrella

Bruschi

et al. 2021

Sensors

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The high demand for data processing in web applications has grown in recent years due to the increased computing infrastructure supply as a service in a cloud computing ecosystem. This ecosystem offers benefits such as broad network access, elasticity, and resource sharing, among others. However, properly exploiting these benefits requires optimized provisioning of computational resources in the target infrastructure. Several studies in the literature improve the quality of this management, which involves enhancing the scalability of the infrastructure, either through cost management policies or strategies aimed at resource scaling. However, few studies adequately explore performance evaluation mechanisms. In this context, we present the MoHRiPA—Management of Hybrid Resources in Private cloud Architecture. MoHRiPA has a modular design encompassing scheduling algorithms, virtualization tools, and monitoring tools. The proposed architecture solution allows assessing the overall system’s performance by using complete factorial planning to identify the general behavior of architecture under high demand of requests. It also evaluates workload behavior, the number of virtualized resources, and provides an elastic resource manager. A composite metric is also proposed and adopted as a criterion for resource scaling. This work presents a performance evaluation by using formal techniques, which analyses the scheduling algorithms of architecture and the experiment bottlenecks analysis, average response time, and latency. In summary, the proposed MoHRiPA mapping resources algorithm (HashRefresh) showed significant improvement results than the analyzed competitor, decreasing about 7% percent in the uniform average compared to ListSheduling (LS).

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“…The authors in [26] have considered the reliability of physical servers when taking decisions of consolidation. They have employed a model of Markov chain to define the server reliability in the cloud.…”

Section: B Power-efficiency Based On Consolidationmentioning

confidence: 99%

Energy-Efficient Hybrid Framework for Green Cloud Computing

et al. 2020

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The increasing growth in the demand for cloud computing services, due to the increasingly digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the requests' scheduling and servers' consolidation approaches rather than depending on only one approach as in existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework over approaches that depend on using only one approach to reduce power consumption in terms of Power Usage Effectiveness (PUE), Data Center Energy Productivity (DCEP), average execution time, throughput and cost-saving. Index terms-green computing, scheduling, consolidation, power consumption.

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A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers

Cited by 45 publications

References 31 publications

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

MoHRiPA—An Architecture for Hybrid Resources Management of Private Cloud Environments

Energy-Efficient Hybrid Framework for Green Cloud Computing

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