A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

Shao, Yifan; Yang, Qiangqiang; Gu, Yanfeng; Pan, Yong; Yi, Zhou; Zhou, Ziao

doi:10.1109/access.2020.3046318

Cited by 16 publications

(16 citation statements)

References 46 publications

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“…Liu and Wang 22 applied different heuristic algorithms including GA and PSO to perform load‐balancing in cloud. Exploring in depth, authors designed GA. Shao et al 24 developed gray model‐based discrete PSO (GM‐DPSO) algorithm for resource allocation in dynamic VMs. Shao et al 24 applied gray prediction model to identify overloaded VM.…”

Section: Resultsmentioning

confidence: 99%

“…In the proposed model, since we assume that a VM encompasses multiple operating tasks together, we require migrating VM rather than single task from one host to another, so as to retain QoS and SLA of the operating users or allied tasks. Although there are a few efforts applying network checklists and cache information to perform task migration, the probability of SLAV or QoS degradation cannot be ignored 24 . Unlike aforesaid or above‐discussed approaches, in this paper, virtualization, often called VM migration, is taken into consideration that not only avoids SLA risks but also reduces detrimental impact of SLAV during random search‐based migration.…”

Section: Proposed Systemmentioning

confidence: 99%

“…Furthermore, for SLA‐centric cloud service provision, there is the need of network‐sensitive allocation even under constrained SLA conditions 5,12,17,18 . Unlike classical bin packing‐oriented VM consolidation concepts, to ensure QoS demands, the migration controller requires considering other performance aspects as well, especially under dynamic cloud condition 19–24 . Migrating a VM while guaranteeing QoS or SLA constructs (no SLAV, higher availability or no or negligible SLAV per active host, 3,7,8,10,16 low power consumption, 14,15,25 high resource utilization, 26 etc.)…”

Section: Introductionmentioning

confidence: 99%

“…In our targeted VM migration of load‐balancing task, heuristic method(s) can be applied to identify the best suitable host or PM to attach the VM or task from the overloaded host 17,18,27–31 . To be noted that in the classical bin‐packing methods, algorithms 32 often use random host selection and therefore do not care the ongoing tasks and its continuity issues, resource demands, and so on 19–24,27 . Majority of the classical load‐balancing methods consider VM migration as a bin‐packing problem 32 that often results into increased SLAV and iterative hotspot creation across cloud infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…It can be a severe problem in case of mega cloud infrastructure 3,5,11,14,15 . To cope up with SLA‐sensitive load balancing, there is the need of a network‐adaptive (say, proactive) scheduling system which could ensure SLA‐sensitive resource allocation while guaranteeing that it does not impose iterative hotspot in the network and energy exhaustion 24 . It has been considered as the key driving force behind this research.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

Madireddy

Ravindranath

2022

Concurrency and Computation

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Summary Cloud services can be expanded and offered in a flexible manner depending on the needs of the users, and cloud computing has become more popular. The energy consumption to provide users with service quality is the biggest concern in virtualization technology. Therefore, reducing energy consumption is a key step in reducing the operating costs of various organizations' data centers. In this article, we propose a dynamic VM relocation (DVMR) system used inside data centers to study virtualization technologies for reducing energy consumption in cloud computing environments. The DVMR system provides a method for predicting PM load by using and selecting loaded and unloaded PM, thereby effectively managing virtualization and energy saving. Experimental evaluation is done by implementing load threshold‐based relocation. The results show that the proposed system can efficiently balance the load between different PMs and efficiently decrease the energy consumption of the entire system.

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A Comparative Analysis on Resource Allocation in Load Balancing Optimization Algorithms

Sridhar

Voleti

Miriyala

et al. 2021

Smart Technologies in Data Science and Communication

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A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

Cited by 16 publications

References 46 publications

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

A Comparative Analysis on Resource Allocation in Load Balancing Optimization Algorithms

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