Reducing Network Latency for Web Applications in a Datacenter
Abstract:
With the rapid development of web applications in datacenters, network latency becomes more important to user experience. The network latency will be greatly increased by incast congestion, in which a huge number of requests arrive at the front-end server simultaneously. Previous congestion problem solutions usually handle the data transmission between the data servers and the front-end server directly, and they are not sufficiently effective in proactively avoiding incast congestion. Generally, the proposals to solve this problem have focused either on remaining existing window-based congestion control like in TCP or on introducing a central controller to make congestion control decisions. In this research, we introduce a Swarm-based incast Congestion Control (SICC) system and a Proactive incast Congestion Control system (PICC), and a Neighbor-aware Congestion Control algorithm based on Reinforcement Learning (NCC). We conclude that these congestion control systems in a datacenter will help reduce network latency, avoid congestion and improve the quality of service of clients. This dissertation provides an overview of the scope of congestion control and network optimization within a datacenter, some of the key challenges in building congestion control systems, hypothesized contributions. The proposed systems achieve better congestion avoidance than several end-to-end and centralized mechanisms in prior work.
Committee:
- Yangfeng Ji, Chair, (CS/SEAS/UVA)
- Haiying Shen, Advisor, (CS/SEAS/UVA)
- Lu Feng (CS/SEAS/UVA)
- Charles Reiss (CS/SEAS/UVA)
- Cong Shen (ECE/SEAS/UVA)