Data Allocation in Scalable Distributed Database Systems Based on Time Series Forecasting

Abstract

In cloud computing environments, database systems have to serve a large number of tenants instantaneously and handle applications with different load characteristics. To provide a high quality of services, scalable distributed database systems with self-provisioning are required. The number of working nodes is adjusted dynamically based on user demand. Data fragments are reallocated frequently for node number adjustment and load balancing. The problem of data allocation is different from that in traditional distributed database systems, and therefore existing algorithms may not be applicable. In this paper, we first formally define the problem of data allocation in scalable distributed database systems. Then, we propose an algorithm for the problem. The algorithm makes use of time series models to perform short-term load forecasting such that node number adjustment and fragment reallocation can be performed in advance to avoid node over loadings and performance degradation due to fragment migrations. In addition, excessive working nodes can be minimized for resource-saving.