A Stochastic Adjustment Strategy for Coordination Process in Distributed Networks

Abstract

Cloud computing has become a popular basis that integrated into amount of large platforms to support applications (e.g., multimedia, vehicle traffic, and IoT). It is critical to focus on coordinating the part of these applications that execute in the cloud to provide reliable, scalable and available services. Nevertheless, the problem of optimally coordinating the applications is rarely addressed. In this paper, we develop a stochastic model to analyze the fundamental characteristics that occur in ZooKeeper during the coordination process. The model primarily addresses two aspects: demands of followers and the load of a leader. Then, we derive the optimal strategy for provision with deployment of coordinated servers to achieve load balancing based on various factors (e.g. server capacity and network load), so that the overall network performance is optimized. We evaluate our algorithm under realistic settings and reveal the trend of factors such as CPU, memory utilization and network bandwidth with the increasing number of requests. We propose the algorithm that considers how many servers should be deployed and when. Our results demonstrate that the strategy guarantees the performance by making suitable deployment adjustment.