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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. [ABSTRACT FROM AUTHOR]

Mobile Cloud Computing (MCC)'s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time. [ABSTRACT FROM AUTHOR]