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Scheduling and congestion control fo...

Jiang, Libin.

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Scheduling and congestion control for wireless and processing networks /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Scheduling and congestion control for wireless and processing networks // Libin Jiang and Jean Walrand.
作者:	Jiang, Libin.
其他作者:	Walrand, Jean.
出版者:	[San Rafael, Calif.] :Morgan & Claypool publishers, : c2010.,
面頁冊數:	x, 144 p. :ill. ;24 cm.
標題:	Wireless communication systems. -
電子資源:	http://www.morganclaypool.com/doi/pdf/10.2200/S00270ED1V01Y201008CNT006
電子資源:	http://proxy.library.carleton.ca/login?url=http://dx.doi.org/10.2200/S00270ED1V01Y201008CNT006Synthesis digital library of engineering and computer science
ISBN:	9781608454624 (ebook)

Scheduling and congestion control for wireless and processing networks /
Jiang, Libin.

Scheduling and congestion control for wireless and processing networks /Libin Jiang and Jean Walrand. - [San Rafael, Calif.] :Morgan & Claypool publishers,c2010. - x, 144 p. :ill. ;24 cm. - Synthesis lectures on communication networks ;#6. - Synthesis lectures on communication networks ;#6..

Includes bibliographical references (p. 133-139) and index.

In this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs.

ISBN: 9781608454624 (ebook)Subjects--Topical Terms:

567106
Wireless communication systems.

LC Class. No.: T57.77 / .J53 2010

Dewey Class. No.: 004.6

Scheduling and congestion control for wireless and processing networks /
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520 3 $a In this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs.
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