TY - GEN
T1 - HALO
T2 - IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2015
AU - Gandhi, Anshul
AU - Zhang, Xi
AU - Mittal, Naman
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/11/16
Y1 - 2015/11/16
N2 - Load Balancers (LBs) play a critical role in managing the performance and resource utilization of distributed systems. However, developing efficient LBs for large, distributed clusters is challenging for several reasons: (i) large clusters require numerous scheduling decisions per second, (ii) such clusters typically consist of heterogeneous servers that widely differ in their computing power, and (iii) such clusters often experience significant changes in load. In this paper we propose HALO, a class of scalable, heterogeneity-aware LBs for cluster systems. HALO LBs are based on simple randomized algorithms that are analytically optimized for heterogeneity. We develop HALO for randomized, Round-Robin, and Power-of-D LBs. We illustrate the benefits of HALO and demonstrate its superiority over other comparable LBs using analytical, simulation, and (Apache-based) implementation results. Our results show that HALO LBs provide significantly lower response times without incurring additional overhead across a wide range of scenarios.
AB - Load Balancers (LBs) play a critical role in managing the performance and resource utilization of distributed systems. However, developing efficient LBs for large, distributed clusters is challenging for several reasons: (i) large clusters require numerous scheduling decisions per second, (ii) such clusters typically consist of heterogeneous servers that widely differ in their computing power, and (iii) such clusters often experience significant changes in load. In this paper we propose HALO, a class of scalable, heterogeneity-aware LBs for cluster systems. HALO LBs are based on simple randomized algorithms that are analytically optimized for heterogeneity. We develop HALO for randomized, Round-Robin, and Power-of-D LBs. We illustrate the benefits of HALO and demonstrate its superiority over other comparable LBs using analytical, simulation, and (Apache-based) implementation results. Our results show that HALO LBs provide significantly lower response times without incurring additional overhead across a wide range of scenarios.
KW - Algorithm design and analysis
KW - Analytical models
KW - Clustering algorithms
KW - Computational modeling
KW - Load modeling
KW - Servers
KW - Time factors
UR - https://www.scopus.com/pages/publications/84962230109
U2 - 10.1109/MASCOTS.2015.14
DO - 10.1109/MASCOTS.2015.14
M3 - Conference contribution
T3 - Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS
SP - 242
EP - 251
BT - Proceedings - IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2015
PB - IEEE Computer Society
Y2 - 5 October 2015 through 7 October 2015
ER -