2016 IEEE 9th International Conference on Cloud Computing (2016)
San Francisco, California, USA
June 27, 2016 to July 2, 2016
Cloud computing is revolutionizing the information technology field. However, clouds today have not yet addressed real-time applications. Current work has shown that real-time hypervisors are capable of allowing virtual machines to meet real-time requirements. Other work has looked at statically allocating resources to virtual guests, which allow those guests to meet deadlines. In this paper, we present DART-C (Demand-based Allocation for Real-Time Clouds), a dynamic real-time cloud framework that allows automated adaptation to these types of workloads. Many applications follow dynamic multi-modal execution patterns, with varying computational requirements over time. DART-C provides demand-based elasticity to support changing real-time requirements by enabling applications to report mode changes to a resource manager, which reallocates resources based on need. We also describe a prototype and demonstrate up to 62% in system resource utilization savings compared to a static allocation, when running a synthetic application set.
Real-time systems, Cloud computing, Resource management, Dynamic scheduling, Virtual machine monitors, Kernel, Elasticity
G. P. Tran, Y. Chen, D. Kang, J. P. Walters and S. P. Crago, "Automated Demand-Based Vertical Elasticity for Heterogeneous Real-Time Workloads," 2016 IEEE 9th International Conference on Cloud Computing(CLOUD), San Francisco, California, USA, 2016, pp. 831-834.