Heavy traffic optimal resource allocation algorithms for cloud computing clusters |
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| Affiliation: | 1. Department of ECE and CSL, University of Illinois at Urbana–Champaign, 1308 W Main Street, Urbana, IL 61801, USA;2. School of ECEE, 436 Goldwater Center, Arizona State University, Tempe, AZ 85287, USA;1. Department of Mathematics, Henan University, Kaifeng, Henan 475004, PR China;2. Institute of Mathematics, Academia Sinica, Beijing 100190, PR China;3. Hua Loo-Keng Key Laboratory of Mathematics, Chinese Academy of Sciences, Beijing 100190, PR China;4. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, PR China;5. Steklov Mathematical Institute of Russian Academy of Sciences, Gubkina Str., 8, 119991, Moscow, Russia;1. Modeling & Simulation Division, CSIR-CECRI, Karaikudi 630 006, India;2. CSIRO Materials Science and Engineering, Private Bag 33, Clayton South, Victoria 3169, Australia;1. Mathematics, University of British Columbia, Kelowna, B.C. V1V 1V7, Canada;2. Department of Mathematics and Informatics, Hanoi National University of Education, 136 Xuan Thuy, Hanoi, Viet Nam;3. Mansoura University, Faculty of Science, Mathematics Department, Mansoura 35516, Egypt |
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| Abstract: | Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with the MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit. |
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| Keywords: | Scheduling Load balancing Cloud computing Resource allocation |
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