Adaptive Computing Resource Allocation for Mobile Cloud Computing

Liang, Hongbin; Xing, Tianyi; Cai, Lin; Huang, Dijiang; Peng, Daiyuan; Liu, Yan

doi:10.1155/2013/181426

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…In [33], the authors propose a novel MCC adaptive resource allocation model using a semi-Markov decision process (SMDP) to manage resource requests. The model proposed achieves higher system performance and lower service blocking probability compared to classical solutions that are based on greedy resource allocation algorithms.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

Nawrocki

Śnieżyński

2017

J Netw Syst Manage

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Since the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question is how to optimally schedule services/tasks between the device and the cloud. The main objective of this article is to investigate the possibilities for using machine learning on mobile devices in order to manage the execution of services within the framework of Mobile Cloud Computing. In this study, an agent-based architecture with learning possibilities is proposed to solve this problem. Two learning strategies are considered: supervised and reinforcement learning. The solution proposed leverages, among other things, knowledge about mobile device resources, network connection possibilities and device power consumption, as a result of which a decision is made with regard to the place where the task in question is to be executed. By employing machine learning techniques, the agent working on a mobile device gains experience in determining the optimal place for the execution of a given type of task. The research conducted allowed for the verification of the solution proposed in the domain of multimedia file conversion and demonstrated its usefulness in reducing the time required for task execution. Using the experience The research presented in this paper was supported by the Polish Ministry of Science and Higher Education under AGH University of Science and Technology Grant 11.11.230.124. We thank Małgorzata Pła_ zek, Jakub Czy_ zewski and Michał Janiec for assistance with implementation and testing. Neither the entire paper nor any part of its content has been published or has been accepted for publication elsewhere. It has not been submitted to any other journal. gathered as a result of subsequent series of tests, the agent became more efficient in assigning the task of multimedia file conversion to either the mobile device or cloud computing resources.

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Section: Related Workmentioning

confidence: 99%

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

Nawrocki

Śnieżyński

2017

J Netw Syst Manage

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“…(12) represents the balance equations requiring that for any state the long-run average number of transitions out of the state per time unit must be equal to the one into the state per time unit. (13) restricts the optimal basic solution z(s, a) is non-negative, and (14) corresponds to the QoS requirement that the blocking probability of mobile service requests must be less than or equal to a constant P b .…”

Section: Calculation Of Optimal Resource Allocation Policymentioning

confidence: 99%

“…While many researches work on resource allocation and provide various solutions [13]- [17] for efficient cloud resource management in MCC systems, the majority of them fail to provide the security guarantee against possible attacks. The authors of [18] propose a resource allocation for security services in MCC system, in which they consider the cloud services composed of two security categories: Critical Security (CS) service and Normal Security (NS) service at a coarse-grained model.…”

Section: Introductionmentioning

confidence: 99%

Security-Aware Resource Allocation for Mobile Cloud Computing Systems

Liu

Lee

2015

2015 24th International Conference on Computer Communication and Networks (ICCCN)

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In this paper, a novel resource allocation algorithm is proposed for secure mobile cloud computing systems. The mobile request for using cloud resource is classified according to its level of security requirement and the amount of required resource for remote computing. We formulate the resource allocation problem as a semi-Markov decision process under the average reward criterion, where the average reward of states is expected to be optimized. Through maximizing the long-term reward while meeting the system requirements of the blocking probability and the amount of resource requested with a security guarantee, the optimal resource allocation policy is calculated by using the linear programming. Simulation results demonstrate that the system adaptively modifies the resource allocation policy for cloud computing, and determines whether to utilize extra resource for security implementation according to the mobile request type, the current traffic, and the cloud resource availability.In the present paper, an adaptive security-aware resource 978-1-4799-9964-4/15/$31.00 ©2015 IEEE

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“…In such a model, several forces affect the offloading decision process of every mobile device. Liang et al have proposed an adaptive resource allocation strategy, based on a semi-Markov decision process [11], opposed to the traditional greedy approach, where VMs are increasingly allocated, depending on the demand. By considering the benefits and expenses of both Cloud and mobile devices, the proposed strategy is able to dynamically allocate different numbers of VMs to mobile applications, to obtain the maximal system rewards and to achieve various QoS levels for mobile users.…”

Section: Related Workmentioning

confidence: 99%