Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers

Hsieh, Sun Yuan; Liu, Chengsheng; Buyya, Rajkumar; Zomaya, Albert Y.

doi:10.1016/j.jpdc.2019.12.014

Cited by 98 publications

(32 citation statements)

References 19 publications

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“…Farahnakian et al [26] proposed another technique for predicting the future utilization of CPU according to the linear regression approach. Gray-Markov model has been employed to predict the future CPU utilization [27]. Gradient descent-based regression (Gd) [28] has been proposed to detect overloaded servers based on a machine learning technique.…”

Section: A Detection Of Overloaded and Underloaded Serversmentioning

confidence: 99%

“…If the new fitness function value of a particle is better than its best value, the particle's best value and best position will be replaced (lines [21][22][23][24]. The previous process is repeated to determine the best fitness function value among the swarm and the position that leads to this best value (lines [25][26][27][28]. The next step is to update particle positions (lines [29][30][31][32][33][34][35].…”

Section: Proposed Techniquementioning

confidence: 99%

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PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers. Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers. Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide. Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption. The virtual machine placement problem is a critical issue for good VM consolidation. This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs. A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs. Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA). Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible. Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones. PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD). It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.

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Section: A Detection Of Overloaded and Underloaded Serversmentioning

confidence: 99%

Section: Proposed Techniquementioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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“…This approach has high prediction accuracy in both workstations and grid environment but is not able to predict workload fluctuations. Paper 27 have used the Gray-Markov prediction model to predict future server processor efficiency. This paper reduces energy and virtual machine migrations but uses only one prediction model.…”

Section: Physical Machines Load Prediction 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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“…But, its efficiency was less since it does not consider LB and traffic predictions. Hsieh et al (23) suggested the VM consolidation method which considers the current and future use of resources by the host overload and host under-load identification. The future use of resources was precisely identified by the gray-Markov-based scheme.…”

Section: Literature Surveymentioning

confidence: 99%

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Gowriprakash¹,

Shankar²,

Duraisamy³

2021

IJST

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Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR).

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Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers

Cited by 98 publications

References 19 publications

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

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

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

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