Latency Aware Elastic Switching-based Stream Processing Over Compressed Data Streams

Abstract

Elastic scaling of event stream processing systems has gained significant attention recently due to the prevalence of cloud computing technologies. We investigate on the complexities associated with elastic scaling of an event processing system in a private/public cloud scenario. We develop an Elastic Switching Mechanism (ESM) which reduces the overall average latency of event processing jobs by significant amount considering the cost of operating the system. ESM is augmented with adaptive compressing of upstream data. The ESM conducts one of the two types of switching where either part of the data is sent to the public cloud (data switching) or a selected query is sent to the public cloud (query switching) based on the characteristics of the query. We model the operation of the ESM as the function of two binary switching functions. We show that our elastic switching mechanism with compression is capable of handling out-of-order events more efficiently compared to techniques which does not involve compression. We used two application benchmarks called EmailProcessor and a Social Networking Benchmark (SNB2016) to conduct multiple experiments to evaluate the effectiveness of our approach. In a single query deployment with EmailProcessor benchmark we observed that our elastic switching mechanism provides 1.24 seconds average latency improvement per processed event which is 16.70% improvement compared to private cloud only deployment. When presented the option of scaling EmailProcessor with four public cloud VMs ESM further reduced the average latency by 37.55% compared to the single public cloud VM. In a multi-query deployment with both EmailProcessor and SNB2016 we obtained a reduction of average latency of both the queries by 39.61 seconds which is a decrease of 7% of overall latency. These performance figures indicate that our elastic switching mechanism with compressed data streams can effectively reduce the average elapsed time of stream processing happening in private/public clouds.