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2016 IEEE 9th International Conference on Cloud Computing (2016)
San Francisco, California, USA
June 27, 2016 to July 2, 2016
ISSN: 2159-6190
ISBN: 978-1-5090-2619-7
pp: 831-834
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.
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