Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT

Mekala, M. S.; Viswanathan, Pragasam

doi:10.1016/j.compeleceng.2018.11.021

Cited by 58 publications

(26 citation statements)

References 16 publications

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“…Using content similarity, paper 33 solves the two problems of virtual machine selection and virtual machine placement in the form of one problem to minimize the memory data transferred in the virtual machine migration. The paper 34 proposes an energy-efficient virtual machine selection method that ranks resources and is a factor-based utilization method. The proposed method includes resource requirement rates for task grouping, comprehensive balanced resource ranking, processing costs, and a square resource efficiency model for migration.…”

Section: Virtual Machine Selection 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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Section: Virtual Machine Selection Methodsmentioning

confidence: 99%

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

2021

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“…Energy reduction is achieved by executing the processors in minimum frequency level and the quality of service is maintained by executing the tasks within the deadline. The unbalanced resource usage, high energy usage and the number of migrations are handled by task categorization and a resource utilization square model [36]. The weight function is applied to each task based on its resource requirement for task categorization.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

et al. 2020

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Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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“…Authors in [61], [74], [82] used the CPU and Memory utilization threshold as an objective function to balance the load or initiate migration among the PMs. They had success in attaining an efficient utilization level for the resources and reducing the energy consumption while keeping to the service level agreement.…”

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

confidence: 99%

“…The migration overhead might be stand-alone or combined with other parameters, because the consolidation depends on it. As in [61] and [63], their work is based on hardware threshold and the migration overhead. [61] classified the server resources based on resource ranking, while [63] scheduled the VMs based on the availability of the server resources where they achieved an optimal number of migrations.…”

Section: ) Migration Overheadmentioning

confidence: 99%

“…As in [61] and [63], their work is based on hardware threshold and the migration overhead. [61] classified the server resources based on resource ranking, while [63] scheduled the VMs based on the availability of the server resources where they achieved an optimal number of migrations. On the reliability side, the VM overhead has optimized as in [67], [73], [78], [79].…”

Section: ) Migration Overheadmentioning

confidence: 99%

See 1 more Smart Citation

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT

Cited by 58 publications

References 16 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

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

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