EdgeCloudBenchmark: A Benchmark Driven by Real Trace to Generate Cloud-Edge Workloads

With the rapid development of 5G and IoT technology, edge computing, as an extension of the cloud computing paradigm, has been widely used to handle some latency-sensitive tasks. Due to insufficient and limited resource of edge devices, when the edge handles some complex tasks, it is often necessary to cooperate with the cloud, which forms the cloud-edge collaboration scenarios. In real cloud-edge collaboration cluster, different scheduling algorithms will greatly affect the resource allocation and workload completion time. Therefore, how to measure the quality of a scheduling algorithm has become critical. However, there is no existing benchmark test sets for such scenarios at present. Based on this problem, this paper proposes EdgeCloudBenchmark, which is a benchmark generation system driven by real Alibaba cluster trace. In this system, we can generate two different benchmark test sets for CPU cluster and GPU cluster, respectively. The experimental results show that these workloads generated from the proposed system can maintain the consistency with the characteristics of the real cluster workloads, and are highly available. Therefore, our proposed system has high concurrency, availability and fault tolerance.