Layered virtual machine migration algorithm for network resource balancing in cloud computing

Xiong, Fei; Chen, Juzhou; Deng, Song; Wang, Junchang; Zhang, Lin

doi:10.1007/s11704-016-6135-9

Cited by 31 publications

(31 citation statements)

References 10 publications

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“…In an intelligent task assignment, a suitable host is selected according to the size of a task and resource consumption of a task. However, it increases the overall operational cost [3]. VM-placement through variable-sized bin packing is an efficient strategy [4].…”

Section: Limitations Of Research and Contributionsmentioning

confidence: 99%

“…The authors in [3] proposed a layered VM-migration algorithm to balance the load of network resources. Two types of the algorithms are presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…The facilities' virtualization technology provides sharing of memory, CPU and storage resources [2]. Multiple applications on VMs perform a task submitted by a user [3]. A bi-directional communication between a user and application needs to use the network resources.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Machine Placement via Bin Packing in Cloud Data Centers

et al. 2018

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With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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Section: Limitations Of Research and Contributionsmentioning

confidence: 99%

“…The authors in [3] proposed a layered VM-migration algorithm to balance the load of network resources. Two types of the algorithms are presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

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Virtual Machine Placement via Bin Packing in Cloud Data Centers

et al. 2018

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“…The number of VMs is increasing dramatically. The dramatic growth of VMs and applications cause unbalanced network resources [3]. A mechanism is required to balance the load of a network.…”

Section: Introductionmentioning

confidence: 99%

“…An intelligent task assignment involves artificial intelligence to balance the load efficiently. However, the cost is increased using intelligent task assignment [3].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

et al. 2019

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Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

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Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

Karthikeyan¹,

Balamurugan²

2021

Concurrency and Computation

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An emerging cloud‐Internet of Things (IoT) is a novel paradigm that brings potential benefits over a variety of applications. The pervasive use of IoT devices often struggle to meet resource requirements in cloud environment. The abundant wastage or inappropriate usage of resources leads to consume larger amount of energy and delayed response. Optimization algorithms are more popular for optimal selection, the algorithms as ant colony optimization, particle swarm optimization, genetic algorithm were majorly concentrated for the purpose of load balancing. However workload is balanced in many previous research works, however it failed to mitigate SLA violations and limitations of energy consumption. In this paper, we address both energy‐aware load balancing and satisfaction of SLA constraints. First, IoT devices submit tasks which are segregated into queues using policy‐based SLA by taking in account of SLA constraints. Second, the tasks are allocated in accordance to dynamic threshold predicting fuzzy followed by Analytical Hierarchical Process. Third, the workload is employed for energy minimization which decides whether to perform swap or migrate virtual machines (VMs). Swapping between VMs is held by novel map and consolidates processes. Then combination of fruit fly and bird swarm hybrid optimization algorithm is enabled to select an optimal VM for workload balancing. Also, the idle physical machines (PMs) are supposed to be in OFF state for diminishing unnecessary energy consumption. The outcomes of this cloud‐IoT system is experimentally evaluated and compared in terms of energy consumption, number of migrations, resource utilization, and execution time.

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Layered virtual machine migration algorithm for network resource balancing in cloud computing

Cited by 31 publications

References 10 publications

Virtual Machine Placement via Bin Packing in Cloud Data Centers

Virtual Machine Placement via Bin Packing in Cloud Data Centers

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

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