QoS- and Contention- Aware Resource Provisioning in a Stream Processing Engine

This paper addresses the shared resource contention problem associated with the auto-parallelization of running queries in distributed stream processing engines. In such platforms, analyzing a large amount of data often requires to execute user-defined queries over continues raw-inputs in a parallel fashion at each single host. However, previous studies showed that the collocated applications can fiercely compete for shared resources, resulting in a severe performance degradation among applications. This paper presents an advanced resource allocation strategy for handling scenarios in which the target applications have different quality of service (QoS) requirements while shared-resource interference is considered as a key performance-limiting parameter.To properly allocate the best possible resource to each query, the proposed controller predicts the performance degradation of the running pane-level as well as the window-level queries when co-running with other queries. This is addressed as an optimization problem where a set of cost functions is defined to achieve the following goals: a) reduce the sum of QoS violation incidents over all machines; b) keep the CPU utilization level within an accepted range; and c) avoid fierce shared resource interference among collocated applications. Particle swarm optimization is used to find an acceptable solution at each round of the controlling period. The performance of the proposed solution is benchmarked with Round-Robin and best-effort strategies, and the experimental results clearly demonstrate that the proposed controller has the following advantages over its opponents: it increases the overall resource utilization by 15% on average while can reduce the average tuple latencies by 14%. It also achieves an average 123% improvement in preventing QoS violation incidents