A refined performance characterization of longest-queue-first policy in wireless networks

One of the major challenges in wireless networking is how to optimize the link scheduling decisions under interference constraints. Recently, a few algorithms have been introduced to address the problem. However, solving the problem to optimality for general wireless interference models is known to be NP-hard. The research community is currently focusing on finding simpler suboptimal scheduling algorithms and on characterizing the algorithm performance. In this paper, we address the performance of a specific scheduling policy called Longest Queue First (LQF), which has gained significant recognition lately due to its simplicity and high efficiency in empirical studies. There has been a sequence of studies characterizing the guaranteed performance of the LQF schedule, culminating at the construction of the σ-local pooling concept by Joo et al. In this paper, we refine the notion of σ-local pooling and use the refinement to capture a larger region of guaranteed performance.

[1]  Changhee Joo,et al.  A local greedy scheduling scheme with provable performance guarantee , 2008, MobiHoc '08.

[2]  Ness B. Shroff,et al.  Performance of Random Access Scheduling Schemes in Multi-Hop Wireless Networks , 2006, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[3]  Ness B. Shroff,et al.  On the Complexity of Scheduling in Wireless Networks , 2006, MobiCom '06.

[4]  Prasanna Chaporkar,et al.  Throughput Guarantees Through Maximal Scheduling in Wireless Networks , 2008 .

[5]  Abhinav Gupta,et al.  Low-complexity distributed scheduling algorithms for wireless networks , 2009 .

[6]  J. Walrand,et al.  Sufficient conditions for stability of longest-queue-first scheduling: second-order properties using fluid limits , 2006, Advances in Applied Probability.

[7]  J. Dai On Positive Harris Recurrence of Multiclass Queueing Networks: A Unified Approach Via Fluid Limit Models , 1995 .

[8]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[9]  Ness B. Shroff,et al.  Joint Congestion Control and Distributed Scheduling for Throughput Guarantees in Wireless Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[10]  Ness B. Shroff,et al.  The impact of imperfect scheduling on cross-layer rate control in wireless networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[11]  Ness B. Shroff,et al.  Understanding the capacity region of the Greedy maximal scheduling algorithm in multihop wireless networks , 2009, TNET.

[12]  E. Modiano,et al.  Local pooling conditions for joint routing and scheduling , 2008, 2008 Information Theory and Applications Workshop.

[13]  Eytan Modiano,et al.  Enabling distributed throughput maximization in wireless mesh networks: a partitioning approach , 2006, MobiCom '06.

[14]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[15]  D. West Introduction to Graph Theory , 1995 .

[16]  Ness B. Shroff,et al.  Joint Congestion Control and Distributed Scheduling for Throughput Guarantees in Wireless Networks , 2007, INFOCOM.

[17]  Xiaojun Lin,et al.  Constant-Time Distributed Scheduling Policies for Ad Hoc Wireless Networks , 2006, CDC.

[18]  Jian Ni,et al.  Improved Bounds on the Throughput Efficiency of Greedy Maximal Scheduling in Wireless Networks , 2011, IEEE/ACM Transactions on Networking.

[19]  Martin Grötschel,et al.  The ellipsoid method and its consequences in combinatorial optimization , 1981, Comb..

[20]  Koushik Kar,et al.  Throughput and Fairness Guarantees Through Maximal Scheduling in Wireless Networks , 2008, IEEE Transactions on Information Theory.

[21]  Quentin F. Stout,et al.  PERFECT DOMINATING SETS , 1990 .

[22]  Ness B. Shroff,et al.  Maximum weighted matching with interference constraints , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06).

[23]  R. Srikant,et al.  Scheduling Efficiency of Distributed Greedy Scheduling Algorithms in Wireless Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[24]  R. Srikant,et al.  Scheduling Efficiency of Distributed Greedy Scheduling Algorithms in Wireless Networks , 2007, IEEE Trans. Mob. Comput..

[25]  Paul D. Seymour,et al.  Analyzing the performance of greedy maximal scheduling via local pooling and graph theory , 2010, S3 '10.

[26]  Madhav V. Marathe,et al.  The distance-2 matching problem and its relationship to the MAC-Layer capacity of ad hoc wireless networks , 2004, IEEE Journal on Selected Areas in Communications.

[27]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[28]  Xiaojun Lin,et al.  The impact of imperfect scheduling on cross-Layer congestion control in wireless networks , 2006, IEEE/ACM Transactions on Networking.

[29]  E. Modiano,et al.  Distributed Throughput Maximization in Wireless Mesh Networks via Pre-Partitioning , 2008, IEEE/ACM Transactions on Networking.

[30]  Ness B. Shroff,et al.  Understanding the Capacity Region of the Greedy Maximal Scheduling Algorithm in Multihop Wireless Networks , 2008, IEEE/ACM Transactions on Networking.