TY - GEN
T1 - Distributed dispatching in the parallel server model
AU - Goren, Guy
AU - Vargaftik, Shay
AU - Moses, Yoram
N1 - Publisher Copyright: © Guy Goren, Shay Vargaftik, and Yoram Moses; licensed under Creative Commons License CC-BY 34th International Symposium on Distributed Computing (DISC 2020).
PY - 2020/10/1
Y1 - 2020/10/1
N2 - With the rapid increase in the size and volume of cloud services and data centers, architectures with multiple job dispatchers are quickly becoming the norm. Load balancing is a key element of such systems. Nevertheless, current solutions to load balancing in such systems admit a paradoxical behavior in which more accurate information regarding server queue lengths degrades performance due to herding and detrimental incast effects. Indeed, both in theory and in practice, there is a common doubt regarding the value of information in the context of multi-dispatcher load balancing. As a result, both researchers and system designers resort to more straightforward solutions, such as the power-of-two-choices to avoid worst-case scenarios, potentially sacrificing overall resource utilization and system performance. A principal focus of our investigation concerns the value of information about queue lengths in the multi-dispatcher setting. We argue that, at its core, load balancing with multiple dispatchers is a distributed computing task. In that light, we propose a new job dispatching approach, called Tidal Water Filling, which addresses the distributed nature of the system. Specifically, by incorporating the existence of other dispatchers into the decision-making process, our protocols outperform previous solutions in many scenarios. In particular, when the dispatchers have complete and accurate information regarding the server queue lengths, our policies significantly outperform all existing solutions.
AB - With the rapid increase in the size and volume of cloud services and data centers, architectures with multiple job dispatchers are quickly becoming the norm. Load balancing is a key element of such systems. Nevertheless, current solutions to load balancing in such systems admit a paradoxical behavior in which more accurate information regarding server queue lengths degrades performance due to herding and detrimental incast effects. Indeed, both in theory and in practice, there is a common doubt regarding the value of information in the context of multi-dispatcher load balancing. As a result, both researchers and system designers resort to more straightforward solutions, such as the power-of-two-choices to avoid worst-case scenarios, potentially sacrificing overall resource utilization and system performance. A principal focus of our investigation concerns the value of information about queue lengths in the multi-dispatcher setting. We argue that, at its core, load balancing with multiple dispatchers is a distributed computing task. In that light, we propose a new job dispatching approach, called Tidal Water Filling, which addresses the distributed nature of the system. Specifically, by incorporating the existence of other dispatchers into the decision-making process, our protocols outperform previous solutions in many scenarios. In particular, when the dispatchers have complete and accurate information regarding the server queue lengths, our policies significantly outperform all existing solutions.
KW - Distributed load balancing
KW - Join the shortest queue
KW - Parallel server model
KW - Tidal water filling
UR - http://www.scopus.com/inward/record.url?scp=85107934698&partnerID=8YFLogxK
U2 - https://doi.org/10.4230/LIPIcs.DISC.2020.14
DO - https://doi.org/10.4230/LIPIcs.DISC.2020.14
M3 - منشور من مؤتمر
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 34th International Symposium on Distributed Computing, DISC 2020
A2 - Attiya, Hagit
T2 - 34th International Symposium on Distributed Computing, DISC 2020
Y2 - 12 October 2020 through 16 October 2020
ER -