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Resource allocation in mobile cloud ...

Liu, Yanchen.

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Resource allocation in mobile cloud computing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Resource allocation in mobile cloud computing./
Author:	Liu, Yanchen.
Description:	128 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
Contained By:	Dissertation Abstracts International77-03B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3732019
ISBN:	9781339186702

Resource allocation in mobile cloud computing.
Liu, Yanchen.

Resource allocation in mobile cloud computing. - 128 p.

Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.

Thesis (Ph.D.)--The City College of New York, 2015.

Nowadays, mobile applications are providing increasingly richer functionalities, which inevitably result in high computational complexity and thus high energy consumption of mobile devices. In Mobile Cloud Computing (MCC) system, some tasks of mobile applications can execute remotely in the cloud by utilizing its resource, which alleviates the energy consumptions of the mobile devices, and improves the performance of mobile applications.

ISBN: 9781339186702Subjects--Topical Terms:

649834
Electrical engineering.

Resource allocation in mobile cloud computing.
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245 1 0 $a Resource allocation in mobile cloud computing.
300 $a 128 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Adviser: Myung J. Lee.
502 $a Thesis (Ph.D.)--The City College of New York, 2015.
520 $a Nowadays, mobile applications are providing increasingly richer functionalities, which inevitably result in high computational complexity and thus high energy consumption of mobile devices. In Mobile Cloud Computing (MCC) system, some tasks of mobile applications can execute remotely in the cloud by utilizing its resource, which alleviates the energy consumptions of the mobile devices, and improves the performance of mobile applications.
520 $a To further release the computational burden of mobile devices, a Dynamic Programming based Offloading Algorithm (DPOA) is proposed to quickly find the optimal partitioning between executing subcomponents of a mobile application at the mobile device and offloading them to the cloud server for remote computing. The DPOA takes into account the CPU speed of mobile device, network performance, the characteristics of an application program, and the efficiency of cloud server. Besides significantly reducing the execution time of mobile application, the DPOA solves the offloading optimization problem with much lower complexity than the Branch and Bound, which is utilized in offloading decision calculation of many other MCC systems.
520 $a In order to better utilize the computing resource of the cloud server, a novel resource allocation algorithm is proposed in this thesis with the consideration of application partition offloading sequence while maintaining the required qualities of services (QoS's) of mobile users. The resource allocation problem is modeled as a semi-Markov decision process (SMDP). Through maximizing the long-term discounted system reward, an optimal resource allocation policy is calculated for partitioned mobile applications using the policy iteration approach, in which the system adaptively allocates computing resource to those partitions that can optimize the system throughput (the number of completed mobile applications).
520 $a Security aspect of the resource allocation is studied for mobile cloud computing systems. The mobile request for using cloud resource is classified according to its risk degree of being an attack to the cloud system and the amount of required resource for remote computing. Through maximizing the long-term reward while meeting the system requirements of the request blocking probability and the user's requirement of resource amount and security guarantee, the optimal resource allocation policy is calculated based on SMDP. From simulation results, it is indicated that the system adaptively updates the resource allocation policy for cloud computing as to whether to utilize extra resource for security implementation according to the mobile request type, the current traffic, and the cloud resource availability.
520 $a The mobile cloud computing utilizing cloudlet is an emerging technology to improve the quality of mobile services. To overcome the main bottleneck of the computation capability of a cloudlet and the wireless bandwidth between mobile devices and the cloudlet, a multi-resource allocation problem of cloudlet-based MCC systems is studied for resource-intensive and latency-sensitive mobile applications. The proposed SMDP-based multi-resource allocation strategy significantly enhances the quality of mobile cloud services in terms of the system throughput and the service latency. From the simulation result, it is indicated that the system dynamically updates the allocation policy as to how much resource to allocate and whether to utilize the distant cloud according to the arrival rate of mobile service requests and the availability of the resource in the system. The proposed algorithm outperforms the greedy admission control approach over a broad range of environments.
590 $a School code: 1606.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
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690 $a 0984
710 2 $a The City College of New York. $b Electrical Engineering. $3 2095374
773 0 $t Dissertation Abstracts International $g 77-03B(E).
790 $a 1606
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3732019