Markov Approximation for Combinatorial Network Optimization

Chen, Minghua; Liew, Soung Chang; Shao, Ziyu; Kai, Caihong

doi:10.1109/infcom.2010.5461998

Cited by 89 publications

(125 citation statements)

References 15 publications

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“…The Markov approximation approach can be used to solve JOP because of its ability to solve multiple sub-problems simultaneously without disjoint step-by-step solutions [10], [11]. First, log-sum-exp approximation is performed, and then, a problem-specific Markov chains is designed to allow distributed implementation.…”

Section: Markov Approximation Frameworkmentioning

confidence: 99%

“…For ease of presentation, U f = U(x, y, P), and JOP be represented by max f ∈F U f . Hence, the equivalent maximum weight independent set (MWIS) problem of JOP is [10]:…”

Section: Markov Approximation Frameworkmentioning

confidence: 99%

“…The log-sum-exp function, g β (U f ), is convex and the closed function [10], [12, p. 93]. Thus, the conjugate of its conjugate g * [10], [12, p. 93].…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

“…Thus, the conjugate of its conjugate g * [10], [12, p. 93]. Following the Markov approximation framework, the logsum-exp approximation of max…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

“…The log-sum-exp approximation in (21) is equivalent to solving the following optimization problem [10], [12, p. 93…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

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SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

Moon

Kazmi

et al. 2017

IEICE Trans. Inf. & Syst.

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Seungil MOON†a) , Thant Zin OO †b) , S. M. Ahsan KAZMI †c) , Bang Ju PARK † †d) , Nonmembers, and Choong Seon HONG †e) , Member SUMMARYThe increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This nonagile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

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Section: Markov Approximation Frameworkmentioning

confidence: 99%

“…For ease of presentation, U f = U(x, y, P), and JOP be represented by max f ∈F U f . Hence, the equivalent maximum weight independent set (MWIS) problem of JOP is [10]:…”

Section: Markov Approximation Frameworkmentioning

confidence: 99%

“…The log-sum-exp function, g β (U f ), is convex and the closed function [10], [12, p. 93]. Thus, the conjugate of its conjugate g * [10], [12, p. 93].…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

“…Thus, the conjugate of its conjugate g * [10], [12, p. 93]. Following the Markov approximation framework, the logsum-exp approximation of max…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

“…The log-sum-exp approximation in (21) is equivalent to solving the following optimization problem [10], [12, p. 93…”

Section: Log-sum-exp Approximationmentioning

confidence: 99%

See 3 more Smart Citations

SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

Moon

Kazmi

et al. 2017

IEICE Trans. Inf. & Syst.

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Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Liao

2013

Int J Communication

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Abstract-Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimisation problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Due to the distinct features of multi hop wireless networks such as time varying channels and dynamic network topology, the feedback information is usually inaccurate, which represent as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback, and how to design effective distributed NUM algorithm for wireless networks. In this paper we firstly use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. Based on that we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm.

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Dynamic switch migration towards a scalable SDN control plane

Cheng

Chen

et al. 2016

Int J Communication

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SUMMARYDistributed control plane is a promising approach to scalable software-defined networking (SDN). Live migration of switches from controllers that are overloaded to those that are underutilized may be a solution to handle peak switch traffic using available control resource. However, such migration has to be performed with a well-designed mechanism to fully utilize available resources in all three resource dimensions: CPU, bandwidth, and memory. In this article, we first provide a resource model for SDN and reduce the switch migration decision to a centralized resource utilization maximization problem under constraints of CPU, bandwidth, and memory. Second, we show that the problem of maximizing resource utilization in an SDN is equivalent to that of maximizing game players' profits in the context of non-cooperative game theory. Taking controllers and switches as game players and commodities respectively, the player policy is how to migrate switches among the control plane. Finally, we implement a proof of concept, called GAME-Switch Migration (GAME-SM). The numerical experiments using Mininet emulator validate nice properties of our game model in enhancing the performance of control plane in SDN.

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Markov Approximation for Combinatorial Network Optimization

Cited by 89 publications

References 15 publications

SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Dynamic switch migration towards a scalable SDN control plane

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