In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate strategy should both maximize the user?s utility and determine the best bid for him/her. In this paper, we aim to solve the problem of joint minimizing the cost of processing tasks and maximizing user satisfaction. Our proposed method, which we call bi-level Markov decision-making process (BLMDP), works on two levels. At the top level, it selects the most appropriate user bids and adjusts the spot price to minimize the cost of VMs on the cloud provider side. At a low level, it decides to admit tasks in such a way as to maximize the user side satisfaction. Performance evaluation based on real data collected from Amazon web services shows that BLMDP manages to minimize cloud provider's costs and maximize user gain more effectively compared to heuristic methods.
NAGHDEHFOROUSHHA, MONA; FOOLADI, MEHDI DEHGHAN TAKHT; REZVANI, MOHAMMAD HOSSEIN; and SADEGHI, MOHAMMAD MEHDI GILANIAN
"BLMDP: A new bi-level Markov decision process approach to joint bidding andtask-scheduling in cloud spot market,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
4, Article 17.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss4/17