TY - GEN
T1 - Analyzing the performance of greedy maximal scheduling via local pooling and graph theory
AU - Birand, Berk
AU - Chudnovsky, Maria
AU - Ries, Bernard
AU - Seymour, Paul
AU - Zussman, Gil
AU - Zwols, Yori
PY - 2010
Y1 - 2010
N2 - Efficient operation of wireless networks and switches requires using simple scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling - GMS) are guaranteed to achieve only a fraction of the maximum possible throughput. It was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ% throughput. In this extended abstract, we characterize all the network graphs in which Local Pooling holds under primary interference constraints. We then show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies its performance could be very bad. Overall, we demonstrate that using graph theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms. The proofs of the results have been omitted for brevity and can be found in [1].
AB - Efficient operation of wireless networks and switches requires using simple scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling - GMS) are guaranteed to achieve only a fraction of the maximum possible throughput. It was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ% throughput. In this extended abstract, we characterize all the network graphs in which Local Pooling holds under primary interference constraints. We then show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies its performance could be very bad. Overall, we demonstrate that using graph theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms. The proofs of the results have been omitted for brevity and can be found in [1].
KW - Algorithms
KW - Design
KW - Performance
UR - http://www.scopus.com/inward/record.url?scp=78649267449&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649267449&partnerID=8YFLogxK
U2 - 10.1145/1860039.1860045
DO - 10.1145/1860039.1860045
M3 - Conference contribution
AN - SCOPUS:78649267449
SN - 9781450301442
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 17
EP - 19
BT - Proc. 2010 ACM Workshop on Wireless of the Students, by the Students, for the Students, S3 '10, Co-located with MobiCom'10 and 11th ACM Int. Symp. on Mobile Ad Hoc Networking and Computing,MobiHoc'10
PB - Association for Computing Machinery
T2 - 2010 ACM Workshop on Wireless of the Students, by the Students, for the Students, S3 '10
Y2 - 20 September 2010 through 24 September 2010
ER -