Acentric Scheduling Strategy for SLA-Based Multi-Tenant Queries

Abstract

The response time for multi-tenant queries is one of the most important indicators in the service level agreements (SLA). The service provider tries to optimize query scheduling strategy to finish queries of different tenants before the deadline to avoid penalty due to jeopardizing the SLA. With continuous expansion of tenants scale, peer-to-peer (P2P) structure becomes more and more popular in organizing and managing multi-tenant data. In this paper we propose an acentric scheduling approach for SLA-based multi-tenant queries according to the distribution characteristics of multi-tenant data. Our scheduling approach deploys multiple scheduling engines on the computing nodes in the cloud, where the computing node of each engine schedules its assigned queries, estimates whether these queries could be finished before the deadline, and migrates the queries that might jeopardize the SLA to another engine which can respond to it before the deadline. Since the high efficiency of the scheduling process is critical, we improve the balanced binary tree and use it to organize queries on each computing node. Using this structure, the online time complexity of the scheduling strategy is [Formula: see text]. Our extensive experiments demonstrate that our scheduling strategy is sufficient to meet the high scheduling efficiency requirement, while the penalty cost can be reduced up to [Formula: see text] compared with the benchmarking solution and with a better scalability.