SigLM: Signature-driven load management for cloud computing infrastructures

Abstract

Cloud computing has emerged as a promising platform that grants users with direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. Unlike traditional batch service model, cloud service model adopts a pay-as-you-go form, which demands explicit and precise resource control. In this paper, we present SigLM, a novel Signature-driven Load Management system to achieve quality-aware service delivery in shared cloud computing infrastructures. SigLM dynamically captures fine-grained signatures of different application tasks and cloud nodes using time series patterns, and performs precise resource metering and allocation based on the extracted signatures. SigLM employs dynamic time warping algorithm and multi-dimensional time series indexing to achieve efficient signature pattern matching. Our experiments using real load traces collected on the PlanetLab show that SigLM can improve resource provisioning performance by 30–80% compared to existing approaches. SigLM is scalable and efficient, which imposes less than 1% overhead to the system and can perform signature matching within tens of milliseconds.