國立虎尾科技大學 |

Spectrum-Aware Cognitive Mobile Cloud Computing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Spectrum-Aware Cognitive Mobile Cloud Computing./
作者:	Mahmoodi, S. Eman.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355095647

Spectrum-Aware Cognitive Mobile Cloud Computing.
Mahmoodi, S. Eman.

Spectrum-Aware Cognitive Mobile Cloud Computing. - 1 online resource (164 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2017.

Includes bibliographical references

Applications have been the main driver for the smartphone market and the smartphones themselves are the source of the bulk of wireless traffic. As the sophistications of these applications increase, so has the interest in offloading computationally intensive applications to a more resource-rich environment like a remote cloud, a smart access point or a fog. Although computation offloading could save energy, it can also cause an increase in the load on the wireless backhaul. In order for computation offloading to be successful, it is essential to look at it holistically and use the recent advances in cognitive networking to develop a new approach to computation offloading.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355095647Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Spectrum-Aware Cognitive Mobile Cloud Computing.
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Advisers: Koduvayur P. Subbalakshmi; Ravanasamudram N. Uma.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2017.
504 $a Includes bibliographical references
520 $a Applications have been the main driver for the smartphone market and the smartphones themselves are the source of the bulk of wireless traffic. As the sophistications of these applications increase, so has the interest in offloading computationally intensive applications to a more resource-rich environment like a remote cloud, a smart access point or a fog. Although computation offloading could save energy, it can also cause an increase in the load on the wireless backhaul. In order for computation offloading to be successful, it is essential to look at it holistically and use the recent advances in cognitive networking to develop a new approach to computation offloading.
520 $a In this dissertation, we develop such a new concept called cognitive cloud offloading. We introduce the concept of wireless network aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components of a sophisticated application need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler predetermined scheduling order for the components. Moreover, our solutions have the ability to use the multiple radio interfaces available in contemporary wireless devices to jointly load balance the data arising from computation offloading.
520 $a In this dissertation we formulate several optimization problems arising in this new holistic approach to end-to-end optimization of computation offloading over an arbitrary number of radio interfaces. Net utility functions are proposed that trade-off several relevant parameters pertaining to the mobile device, the remote infrastructure, the network parameters as well as the applications themselves. Examples of these parameters include bandwidth of the spectrum, device power, CPU cycles, and application deadlines. We present optimal as well as heuristic solutions for these problems.
520 $a The performance of our strategies are evaluated with extensive simulations using real data from HTC smartphones (mobile device), the NSFCloud and Amazon EC2 (remote cloud servers) using two radio interfaces (WiFi and LTE). Data was gathered from indoor as well as outdoor wireless environments. Evaluations show that our schemes can lower energy consumption by 23%-68% and reduce latency by 28%-66% in comparison to the state of the art.
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538 $a Mode of access: World Wide Web
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