TY - GEN
T1 - Accurate traffic splitting on commodity switches
AU - Rottenstreich, Ori
AU - Kaplan, Haim
AU - Kanizo, Yossi
AU - Rexford, Jennifer L.
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/11
Y1 - 2018/7/11
N2 - Traffic splitting is essential for load balancing over multiple servers, middleboxes, and paths. Often the target traffic distribution is not uniform (e.g., due to heterogeneous servers or path capacities). A natural approach is to implement traffic split in existing rule matching tables in commodity switches. In this paper we suggest an analytical study of such an approach. To do that, we relate the description of distributions in switches to signed representations of positive integers. We suggest an optimal algorithm that minimizes the number of rules needed to represent a weighted traffic distribution. Since switches often have limited rule-table space, the target distribution cannot always be exactly achieved. Accordingly, we also develop a solution that, given a restricted number of rules, finds a distribution that can be implemented within the limited space. To select among different solutions, we describe metrics for quantifying the accuracy of an approximation. We demonstrate the efficiency of the solutions through extensive experiments.
AB - Traffic splitting is essential for load balancing over multiple servers, middleboxes, and paths. Often the target traffic distribution is not uniform (e.g., due to heterogeneous servers or path capacities). A natural approach is to implement traffic split in existing rule matching tables in commodity switches. In this paper we suggest an analytical study of such an approach. To do that, we relate the description of distributions in switches to signed representations of positive integers. We suggest an optimal algorithm that minimizes the number of rules needed to represent a weighted traffic distribution. Since switches often have limited rule-table space, the target distribution cannot always be exactly achieved. Accordingly, we also develop a solution that, given a restricted number of rules, finds a distribution that can be implemented within the limited space. To select among different solutions, we describe metrics for quantifying the accuracy of an approximation. We demonstrate the efficiency of the solutions through extensive experiments.
UR - http://www.scopus.com/inward/record.url?scp=85051140025&partnerID=8YFLogxK
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U2 - 10.1145/3210377.3210412
DO - 10.1145/3210377.3210412
M3 - Conference contribution
AN - SCOPUS:85051140025
T3 - Annual ACM Symposium on Parallelism in Algorithms and Architectures
SP - 311
EP - 320
BT - SPAA 2018 - Proceedings of the 30th ACM Symposium on Parallelism in Algorithms and Architectures
PB - Association for Computing Machinery
T2 - 30th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2018
Y2 - 16 July 2018 through 18 July 2018
ER -