Cost-aware & Fault-tolerant Geo-distributed Edge Computing for Low-latency Stream Processing

Abstract

The number of Internet-of-Things (IoT) devices is rapidly increasing with the growth of IoT applications in various domains. As IoT applications have a strong demand for low latency and high throughput computing, stream processing using edge computing resources is a promising approach to support low latency processing of large-scale data. Edge-based stream processing extends the capability of cloud-based stream processing by processing the data streams near the edge of the network. In this vision paper, we discuss a distributed stream processing framework that optimizes the performance of stream processing applications through a careful allocation of geo-distributed computing and network resources available in edge computing environments. The framework includes key optimizations in both the platform layer and the infrastructure layer. While the platform layer is responsible for converting the user program into a stream processing physical plan and optimizing the physical plan and operator placement, the infrastructure layer is responsible for provisioning geo-distributed resources to the platform layer. The framework optimizes the performance of stream query processing at the platform layer through its careful consideration of data locality and resource constraints during physical plan generation and operator placement and by incorporating resilience to deal with failures. The framework also includes techniques to dynamically determine the level of parallelism to adapt to changing workload conditions. At the infrastructure layer, the framework includes a novel model for allocating computing resources in edge and geo-distributed cloud computing environments by carefully considering latency and cost. End users benefit from the platform through reduced cost and improved user experience in terms of response time and latency.