Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

Ferdaus, Hasanul; Murshed, Manzur; Calheiros, Rodrigo N.; Buyya, Rajkumar

doi:10.1007/978-3-319-09873-9_26

Cited by 147 publications

(88 citation statements)

References 16 publications

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“…Furthermore, we evaluate the proposed solution and compare it with a heuristicsbased one proposed in [2,3]. The dynamic placement based on VM consolidation is a strictly NP-hard problem [28]. Figure 2 shows the loop design model of the proposed utility-based solution in terms of monitoring, analysis, planning and execution (MAPE).…”

Section: Optimizing Virtual Machine Placementmentioning

confidence: 99%

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Mosa

Paton

2016

J Cloud Comp

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Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users' demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO 2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.

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Section: Optimizing Virtual Machine Placementmentioning

confidence: 99%

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Mosa

Paton

2016

J Cloud Comp

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“…In [47], the authors proposed a VM consolidation scheme that focused on balanced resource utilization of servers across different computing resources with the goal of minimizing power consumption and resource wastage. This study presented an adaptation and integration of the ACO met heuristic with a balanced usage of computing resources.…”

Section: ) Predicting the Upcoming Number Of The (Vm)mentioning

confidence: 99%

Power management in virtualized data centers: state of the art

et al. 2016

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Cloud computing is an emerging technology in the field of computing that provides access to a wide range of shared resources. The rapid growth of cloud computing has led to establishing numerous data centers around the world. As data centers consume huge amounts of power, enhancing their power efficiency has become a major challenge in cloud computing. This paper surveys previous studies and researches that aimed to improve power efficiency of virtualized data centers. This survey is a valuable guide for researchers in the field of power efficiency in virtualized data centers following the cloud computing model.

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“…Some heuristic approaches like ACO and evolutionary concept of Genetic Algorithm (GA) were suggested in [18] and [19] respectively, for consolidation purposes. ACO-based decentralized scheme for scheduling was proposed in [20]. The genetic algorithm was examined for resource scheduling function in virtualization [21][22][23].…”

Section: Related Workmentioning

confidence: 99%