Resource Allocation in Mobile Cloud Computing

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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Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200799 ◽

2021 ◽

Vol 40 (1) ◽

pp. 787-797

Author(s):

G. Saravanan ◽

N. Yuvaraj

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Mobile Applications ◽

Mobile Cloud Computing ◽

Service Providers ◽

Mobile User ◽

Mobile Users ◽

Mobile Cloud ◽

Queuing Model ◽

Gradient Learning

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

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Improved Cost-Effective Technique for Resource Allocation in Mobile Cloud Computing

Advances in Intelligent Systems and Computing - Progress in Computing, Analytics and Networking ◽

10.1007/978-981-10-7871-2_53 ◽

2018 ◽

pp. 551-558

Author(s):

Enakshmi Nandi ◽

Ranjan Kumar Mondal ◽

Payel Ray ◽

Biswajit Biswas ◽

Manas Kumar Sanyal ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Mobile Cloud Computing ◽

Cost Effective ◽

Mobile Cloud ◽

Effective Technique ◽

Cost Effective Technique

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