OSPN: Optimal Service Provisioning with Negotiation for Bag-of-Tasks Applications

  • SCI-E
  • EI
作者: Wang, Xiaogang;Cao, Jian;Xiang, Yang
作者机构: Shanghai Institute for Advanced Communication and Data Science, Shanghai Jiao Tong University, Minghang District, Shanghai, P.R. China
Shanghai Dianji University, Minghang District, Shanghai, P.R. China
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Minghang District, Shanghai, P.R. China
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia
语种: 英文
关键词: Cloud computing;Pricing;Economics;Nash equilibrium;Computational modeling;Parallel pricing negotiation;service provisioning strategy profile;multi-cloud service selection;spot Instance;utility nash equilibrium;kuhn-tucher condition
期刊: IEEE Transactions on Services Computing
ISSN: 1939-1374
年: 2021
摘要: Cloud service selection is becoming more complex with the arrival of a large number of cloud providers offering various service packages on the market. These cloud service packages are generally provisioned by Spot, On-demand and Reserved Instances. Typically, a user's service requirements contain many independent sub-tasks (Bag-of-Tasks), and have budget limitations and additional constraints. To select reasonable cloud instances to run the user's sub-tasks, we propose a strategy, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OSPN</italic> (Optimal Service Provisioning with Negotiation), to support the allocation of tasks to services offered by multi-cloud providers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OSPN</italic> consists of two phases: in the first phase, a one-to-many parallel Spot Instance pricing negotiation is applied; in the second phase, service provisioning strategy profiles on the three types of cloud instances are calculated. Specifically, the first phase employs an improved double auction in which the price and availability of providers’ instances are taken into account; then the second phase gives the utility Nash equilibrium model and derives the optimal provisioning strategy profiles. The experimental results show that our service provisioning strategy is more cost-effective, namely, the most gains of both the user and providers in the changing scenes, and the least payments of the user than the existing relevant strategies.

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OSPN: Optimal Service Provisioning with Negotiation for Bag-of-Tasks Applications
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