A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

Tang, Maolin; Pan, Shenchen

doi:10.1007/s11063-014-9339-8

Cited by 159 publications

(59 citation statements)

References 12 publications

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“…Therefore, their GA approach was not only used to find an optimized state to map the VMs efficiently to the PMs, but also was extended to reconstruct the system state at a low transition overhead when the environment did change. Tang and Pan [2014] used a hybrid GA with a local search procedure to reduce the energy consumption by considering not only the PMs, but also the communications among VMs.…”

Section: Scheduling For Energy Conservationmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Section: Scheduling For Energy Conservationmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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“…Second, evolutionary computation methods are used to perform approximating optimal solutions. The examples are simulated annealing algorithm [41], hybrid genetic algorithm [32], frog-leaping algorithm [23], machine learning algorithm [3] and artificial bee colony algorithm [14]. These approaches yield better results, but require much longer runtimes than greedy heuristics.…”

Section: Related Workmentioning

confidence: 99%

Energy efficiency of VM consolidation in IaaS clouds

Teng

et al. 2016

J Supercomput

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The energy efficiency of cloud computing has recently attracted a great deal of attention. As a result of raised expectations, cloud providers such as Amazon and Microsoft have started to deploy a new IaaS service, a MapReduce-style virtual cluster, to process data-intensive workloads. Considering that the IaaS provider supports multiple pricing options, we study batch-oriented consolidation and online 123 F. Teng et al. placement for reserved virtual machines (VMs) and on-demand VMs, respectively. For batch cases, we propose a DVFS-based heuristic TRP-FS to consolidate virtual clusters on physical servers to save energy while guarantee job SLAs. We prove the most efficient frequency that minimizes the energy consumption, and the upper bound of energy saving through DVFS techniques. More interestingly, this frequency only depends on the type of processor. FS can also be used in combination with other consolidation algorithms. For online cases, a time-balancing heuristic OTB is designed for on-demand placement, which can reduce the mode switching by means of balancing server duration and utilization. The experimental results both in simulation and using the Hadoop testbed show that our approach achieves greater energy savings than existing algorithms.

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“…In [16], Tang and Pan proposed an Hybrid Genetic Algorithm (HGA) to solve the consolidation problem. To rapidly improve solutions, they adopted a local optimization procedure.…”

Section: Previous Researchmentioning

confidence: 99%

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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A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

Cited by 159 publications

References 12 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Energy efficiency of VM consolidation in IaaS clouds

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

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