Heavy traffic optimal resource allocation algorithms for cloud computing clusters

Authors:
Siva Theja Maguluri;R. Srikant;Lei Ying
Affiliations:
University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;Arizona State University
Venue:
Proceedings of the 24th International Teletraffic Congress
Year:
2012

Citing 7
Cited 0

Diffusion approximations for open multiclass queueing networks: sufficient conditions involving state space collapse

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State space collapse with application to heavy traffic limits for multiclass queueing networks

Queueing Systems: Theory and Applications
Heavy traffic resource pooling in parallel-server systems

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The power of two choices in randomized load balancing

The power of two choices in randomized load balancing
Limited choice and locality considerations for load balancing

Performance Evaluation
Randomized load balancing with general service time distributions

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Asymptotically tight steady-state queue length bounds implied by drift conditions

Queueing Systems: Theory and Applications

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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 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.