Evaluating and Improving Internet Load Balancing with Large-Scale Latency Measurements

Load balancing is used in the Internet to distribute load across resources at different levels, from global load balancing that distributes client requests across servers at the Internet level to path-level load balancing that balances traffic across load-balanced paths. These load balancing algorithms generally work under certain assumptions on performance similarity. Specifically, global load balancing divides the Internet address space into client aggregations and assumes that clients in the same aggregation have similar performance to the same server; load-balanced paths are generally selected for load balancing as if they have similar performance. However, as performance similarity is typically achieved with similarity in path properties, e.g., topology and hop count, which do not necessarily lead to similar performance, performance between clients in the same aggregation and between load-balanced paths could differ significantly. This dissertation evaluates and improves global and path-level load balancing in terms of performance similarity. We achieve this with large-scale latency measurements, which not only allow us to systematically identify and evaluate the performance issues of Internet load balancing at scale, but also enable us to develop data-driven approaches to improve the performance. Specifically, this dissertation consists of three parts. First, we study the issues of existing client aggregations for global load balancing and then design AP-atoms, a data-driven client aggregation learned from passive large-scale latency measurements. Second, we show that the latency imbalance between load-balanced paths, previously deemed insignificant, is now both significant and prevalent. We present Flipr, a network prober that actively collects large-scale latency measurements to characterize the latency imbalance issue. Lastly, we design another network prober, Congi, that can detect congestion at scale and use Congi to study the congestion imbalance problem at scale. For both latency and congestion imbalance, we demonstrate that they could greatly affect the performance of various applications.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168012/1/yibo_1.pd

AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning

In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can redesign the telemetry system according to telemetry requirements and network environments. AdapINT can reduce telemetry latency by 75\% in online games and video conferencing scenarios. For overhead-aware networks, AdapINT can reduce control overheads by 34\% in cloud computing services.Comment: 14 pages, 19 figure