TIMER-Cloud: Time-Sensitive VM Provisioning in Resource-Constrained Clouds

Abstract

Resource management is a vital factor for better performance in cloud systems and many resource allocation algorithms have been studied. In this work, focusing on applications with timing constraints (i.e., deadlines) running on resource-constrained clouds that have multiple heterogeneous nodes of computing resources (e.g., CPU cores and memory), we propose TIMER-Cloud , a time-sensitive resource allocation and virtual machine (VM) provisioning framework. As a key component of the framework, user requests (of running certain applications) are prioritized according to their deadlines and resource demands (in the form of VM and its operation time). Specifically, in addition to the intuitive Earliest Deadline First (EDF) ordering of requests, we propose three prioritization heuristics: a) one based on the Time-Sensitive Resource Factor (TSRF) that incorporates a request's deadline and usage efficiency of all its resources; b) the Dominant Share (DS) extension of TSRF that emphasizes the most demanded resource of a request aiming at obtaining balanced resource usage among the nodes; and c) a unified k-EDF scheme that integrates the ideas of EDF and TSRF/DS to balance the needs of meeting imminent deadlines of requests and improving resource usage efficiency. Then, for the mapping of the prioritized user requests to the heterogeneous nodes, we propose a novel request-to-node mapping algorithm based on the idea of euclidean Distance that finds the node with the best match of its resource requirements for each request. TIMER-Cloud has been implemented and validated on a cloud testbed powered by OpenStack with a few heterogeneous nodes. The proposed VM provisioning schemes are further evaluated through extensive simulations using the execution data of benchmark applications. The results show that the proposed schemes can outperform the state-of-the-art deadline oblivious scheme by serving up to 12 percent more user requests and achieving up to 8 percent more system rewards for the over-loaded scenario with 140 percent system load.