A hierarchical structure for optimal resource allocation in geographically distributed clouds

Ziafat, Hassan; Babamir, Seyed Morteza

doi:10.1016/j.future.2018.08.027

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…In [115 ], a structure is provided for the allocation of resources in data centres that are geographically distributed. Initially, a hierarchical model of data centres and their interfaces are created.…”

Section: Reviewing Existing Ra Mechanismsmentioning

confidence: 99%

Resource allocation mechanisms in cloud computing: a systematic literature review

et al. 2020

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Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random‐access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non‐fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.

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Section: Reviewing Existing Ra Mechanismsmentioning

confidence: 99%

Resource allocation mechanisms in cloud computing: a systematic literature review

et al. 2020

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“…Liu, X. et al considered VM allocation problem in [30]. The basic purpose of the proposed work is to maximize the availability and reliability of resources provided by the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Efficient VM placement, VM consolidation, VM migration and optimization problem are solved in [22,[24][25][26]. The authors of [30,31] proposed GreA and ACO to solve VM allocation and VM placement problem. However, VM placement problem is an NP-hard problem.…”

Section: Related Workmentioning

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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“…The authors in [26] proposed a simplex linear programming (SLP) method. In distributed cloud data centers, the hierarchical structure is also presented to show connections between them.…”

Section: Related Workmentioning

confidence: 99%

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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A hierarchical structure for optimal resource allocation in geographically distributed clouds

Cited by 14 publications

References 20 publications

Resource allocation mechanisms in cloud computing: a systematic literature review

Resource allocation mechanisms in cloud computing: a systematic literature review

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

Virtual Machine Placement via Bin Packing in Cloud Data Centers

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