A multi-objective optimization for resource allocation of emergent demands in cloud computing

AbstractCloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

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Multi-Objective Optimization for Resource Allocation in Vehicular Cloud Computing Networks

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3091321 ◽

2021 ◽

pp. 1-10

Author(s):

Wenting Wei ◽

Ruying Yang ◽

Huaxi Gu ◽

Weike Zhao ◽

Chen Chen ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Multi Objective Optimization ◽

Vehicular Cloud Computing ◽

Vehicular Cloud ◽

Multi Objective

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Resource scheduling strategy for uncertain factors in mobile cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189716 ◽

2021 ◽

pp. 1-6

Author(s):

Ying Chen

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Computing Environment ◽

Mobile Terminals ◽

Cloud Computing Environment ◽

Resource Allocation Algorithm ◽

Cloud Resource ◽

Resource Environment

At present, resource configuration of mobile cloud computing has received extensive attention from the outside world. Most of the similar resource scheduling configuration fails to comprehensively consider the dynamics of mobile terminals and the difference in user requested resources. Therefore, considering uncertainty in paging scheduling under mobile cloud resource environment from the perspective of consumers has become the key to solving the problem of resource allocation in the mobile cloud computing environment. This paper proposes an adaptive matching resource allocation algorithm based on uncertain factors under mobile cloud computing environment. Uncertain factors of the mobile terminal are derived via QoS attribute, and then user information and load characteristics of the user requested resources are analyzed through CLIQUE similarity matching. Afterwards, based on the mapping between similarity and resources, resource paging allocation can be carried out based on adaptive matching resource allocation algorithm. From the perspective of consumers, dynamics of mobile terminals and uncertainty of paging scheduling in the mobile cloud resource environment under different user requested resources can be considered to allow minimized delay and optimized paging strategies.

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