Integrative Dynamic Reconfiguration in a Parallel Stream Processing Engine

TL;DR

This paper presents an integrated optimization approach for load balancing, operator placement, and scaling in parallel stream processing engines, improving performance and resource utilization.

Contribution

It models the coupled problems as a single optimization problem and introduces ALBIC, an extended solution supporting general jobs, implemented on Apache Storm.

Findings

Outperforms existing approaches in experiments

Effectively balances load and reduces communication costs

Supports a wide range of job types

Abstract

Load balancing, operator instance collocations and horizontal scaling are critical issues in Parallel Stream Processing Engines to achieve low data processing latency, optimized cluster utilization and minimized communication cost respectively. In previous work, these issues are typically tackled separately and independently. We argue that these problems are tightly coupled in the sense that they all need to determine the allocations of workloads and migrate computational states at runtime. Optimizing them independently would result in suboptimal solutions. Therefore, in this paper, we investigate how these three issues can be modeled as one integrated optimization problem. In particular, we first consider jobs where workload allocations have little effect on the communication cost, and model the problem of load balance as a Mixed-Integer Linear Program. Afterwards, we present an extended solution called ALBIC, which support general jobs. We implement the proposed techniques on top of Apache Storm, an open-source Parallel Stream Processing Engine. The extensive experimental results over both synthetic and real datasets show that our techniques clearly outperform existing approaches.