A QoS-Latency Aware Event Stream Processing with Elastic-FaaS

Abstract

Stream processing systems need to be elastically scalable to process and respond the unpredictable massive load spike in real-time with high throughput and low latency. Though the modern cloud technologies can help in elastically provisioning the required computing resources on-the-fly, finding out the right point-in-time varies among systems based on their expected QoS characteristics. The latency sensitivity of the stream processing applications varies based on their nature and pre-set requirements. For few applications, even a little latency in the response will have huge impact, whereas for others the little latency will not have that much impact. For the former ones, the processing systems are expected to be highly available, elastically scalable, and fast enough to perform, whenever there is a spike. The time required to elasticity provision the systems under FaaS is very high, comparing to provisioning the Virtual Machines and Containers. However, the current FaaS systems have some limitations that need to be overcome to handle the unexpected spike in real-time. This paper proposes a new algorithm called Elastic-FaaS on top of the existing FaaS to overcome this QoS latency issue. Our proposed algorithm will provision required number of FaaS container instances than any typical FaaS can provision normally, whenever there is a demand to avoid the latency issue. We have experimented our algorithm with an event stream processing system and the result shows that our proposed Elastic-FaaS algorithm performs better than typical FaaS by improving the throughput that meets the high accuracy and low latency requirements.