Performance Analysis of Large-Scale Load Balancing Systems
Author(s)
Zhao, Zhisheng
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Advanced cloud computing platforms, such as AWS, Azure, and Google Cloud, handle millions of requests per second. Efficiently assigning tasks across servers using a load balancing algorithm is critical for the seamless functioning of these systems. This dissertation focuses on performance analysis of large-scale load balancing systems. First, we analyze the JSQ policy in the super-Halfin-Whitt scaling window. We have shown that the centered and scaled process of total number of tasks in the system converges to a certain Bessel process. Also, its stationary distribution converges to a Gamma distribution. Then, we consider a heterogeneous model with the constraint of data locality and implement JSQ(d) policy. In this work, we investigate the graph structure with which the vanilla JSQ(d) policy can achieve the throughput optimality and improve the system performance greatly compared with the random assignment. In the third project, we extend our analysis to a more general heterogeneous system where the service rate depends on both types of servers and dispatchers. We proposed a new framework for analyzing the fully heterogeneous systems and designed two simple scalable delay-optimal routing policies. In the end, we discussed an application of the load balancing concept in the EV charging networks.
Sponsor
Date
2024-12-11
Extent
Resource Type
Text
Resource Subtype
Dissertation