Self-adaptive load-balancing strategy based on a time series pattern for concurrent user access on Web map service

Load-balancing strategies address challenges stemming from intensive access and heavy communication traffic on a Web map service platform (WMSP) by collecting workload-related information from cluster-based servers, and distributing tasks to minimize the consumption of computational and caching resources. However, intensive user access has time series patterns that create temporal periodic variations that can be exploited to improve the performance of the WMSP. In this paper, we propose a variable feedback strategy based on time series variations in the intensity of user access to increase the efficiency and reliability of workload feedback with little resource consumption. A task distribution strategy, based on the expected values of the arrival rate that match real-time conditions in workload feedback periods and the real-time processing capability of each cluster-based server, is devised simply and accurately by an association strategy for workload and service rate that supports services insensitive to massive numbers of concurrent access requests. The results of experiments show that the proposed strategy provides quick responses and high throughput for large-scale user access. It implements efficient load balancing for service resource utilization, and can thus improve the stability and capacity of the WMSP server using heterogeneous back-end cloud cluster-based servers.