Research on dynamic resource allocation with cooperation strategy in cloud computing

Abstract

Cloud computing is featured by powerful computing capabilities. However, the resource allocation puts forward new challenges. A highly efficient resource allocation scheme is of great significance in parallel processing of cloud computing. In this paper, we propose a novel dynamic resource allocation algorithm with cooperation strategy. We first model and analyze the resource allocation problem theoretically. We introduce a heuristic information-based algorithm with the cooperation of all the computing nodes. We describe the algorithm in details; introduce the related parameters and algorithm steps. We analyze the algorithm and also evaluate its performance by simulation experiments. The experiment results indicate that our algorithm conducts resource allocation fast and effectively, achieving superior performance as well.

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

“…In fact, the term of skewness is used to calculate the irregular utilization of the servers familiarized by Xiao et al [47], also develop a load prediction algorithm that is used for sensed load the upcoming resource usages of applications precisely, deprived of VMs consideration. Further, Dai et al [48] offer an inventive dynamic resource allocation algorithm for the VM, with assistance policy. Firstly, plan the model that is used for estimated the resource allocation problem hypothetically and further presented a heuristic information based algorithm with the collaboration of all the processing nodes.…”

“…It is frequently generated to execute certain tasks by software competition ways or hardware virtualization techniques that are different than tasks are executed in a host environment. An-ping and Chun-xiang [31] Li and Li [34] Liang et al [33] Panda and Jana [29] Radhakrishnan and Kavitha [32] Shyam and Manvi [30] Vernekar and Game [35] Wang et al [36] Ali et al [44] Dai et al [48] Hu et al [45] Hadji and Zeghlache [49] Oddi et al [46] Saraswathi et al [39] Teng and MagoulFs [54] Wang and Liu [50] Wang and Su [40] Wolke and Ziegler [41] Wuhib et al [51] Wuhib et al [55] Xie and Liu [42] Xiao et al [47] Yin et al [52] Zhang et al [43] Zhang et al [ Ali et al [44] Dai et al [48] Hu et al [45] Hadji and Zeghlache [49] Oddi et al [46] Saraswathi et al [39] Teng and MagoulFs [54] Wang and Liu [50] Wang and Su [40] Wuhib et al [51] Wuhib et al [55] Xie and Liu [42] Xiao et al [47] Yin et al [52] Zhang et al [43] Zhang et al [53] Gu et al [65] Mashayekhy et al …”

A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

“…Optimization problem for the cloud in resource allocation will aim to optimize the use of different resources like Job completion time, topology of the cloud, minimum execution time, Resource availability, Request satisfaction are considered. [5] The task allocator is using knowledge base of all the VM types that the user can request and the host machine can configure. This information is used in the computation of the optimization matrix.…”

Resource Allocation in Cloud computing: A Comparative study

“…The cost here may refer in terms of infrastructure cost, computing cost etc. It provides virtual business to organizations so that different resources can be managed efficiently [3]. The most critical feature provided by cloud is the virtualization.…”