Type-aware virtual machine management for energy efficient cloud data centers

Al-Dulaimy, Auday; Itani, Wassim; Zantout, Rached; Zekri, Ahmed

doi:10.1016/j.suscom.2018.05.012

Cited by 23 publications

(11 citation statements)

References 17 publications

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“…Paper 39 proposes a new policy based on random selection for virtual machine selection. It also compares the proposed policy with several other policies and analyzes the comparison results.…”

Section: Refmentioning

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: Refmentioning

confidence: 99%

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

2021

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“…22 Another report on carbon emissions published in 2016 revealed that CDCs globally produce nearly 1.5 Mt of CO 2 in 2016, and this will be tripled by 2025. 23 To solve these problems, energy-aware cloud infrastructure systems should be adopted and implemented to reduce total energy consumption and CO 2 emissions in CDCs. 24,25 To minimize global warming effects in Pakistan, it was recommended to use cloud-enabled solutions along with renewable energy sources for power generations across IT infrastructures.…”

Section: Problem Backgroundmentioning

confidence: 99%

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Uddin

Hamdi

Alghamdi

et al. 2021

International Journal of Distributed Sensor Networks

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Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

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“…Their approach selects the destination server according to the reliability and power consumption. In [27], A. Al-Dulaimy et al have proposed a consolidation-based approach that considers the type of jobs assigned to VMs before migrating them. The approach depends on using the Multiple-Choice Knapsack Problem for the initial scheduling of VMs and the placemat of the migrated VMs.…”

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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Type-aware virtual machine management for energy efficient cloud data centers

Cited by 23 publications

References 17 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

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Energy-Efficient Hybrid Framework for Green Cloud Computing

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