The layer-4 load balancer (LB) is one of the key building blocks of online services. In this paper, we empower such LBs to adapt to different and dynamic performance of backend instances (DIPs). Our system, KnapsackLB, is generic (can work with variety of LBs), does not require agents on DIPs, LBs or clients, and scales to large numbers of DIPs. KnapsackLB uses judicious active probes to learn a mapping from LB weights to the response latency of each DIP, and then applies Integer Linear Programming (ILP) to calculate LB weights that optimize latency, using an iterative method to scale the computation to large numbers of DIPs. Using testbed experiments and simulations, we show that KnapsackLB load balances traffic according to DIP performance and cuts average latency by up to 45% compared to existing designs.<\/p>\n","protected":false},"excerpt":{"rendered":"
The layer-4 load balancer (LB) is one of the key building blocks of online services. In this paper, we empower such LBs to adapt to different and dynamic performance of backend instances (DIPs). Our system, KnapsackLB, is generic (can work with variety of LBs), does not require agents on DIPs, LBs or clients, and scales 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