Cloud providers provision their various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that consider several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.

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We address the problem of physical machine resource management in clouds considering multiple types of physical machines and resources. We formulate this problem in an auction-based setting and design optimal and approximate strategy-proof mechanisms that solve it. Our proposed mechanisms consist of a winner determination algorithm that selects the users, provisions the virtual machines (VMs) to physical machines (PM), and allocates them to the selected users; and a payment function that determines the amount that each selected user needs to pay to the cloud provider. We prove that our proposed approximate winner determination algorithm satisfies the loser-independent property, making the approximate mechanism robust against strategic users who try to manipulate the system by changing other users' allocations. We show that our proposed mechanisms are strategy-proof, that is, the users do not have incentives to lie about their requested bundles of VM instances and their valuations. In addition, our proposed mechanisms are in alignment with green cloud computing strategies in which physical machines can be powered on or off to save energy. Our theoretical analysis shows that the proposed approximation mechanism has an approximation ratio of 3. We perform extensive experiments in order to investigate the performance of our proposed approximation mechanism compared to that of the optimal mechanism. Index Terms-cloud computing, mechanism design, energy efficient resource management. ✦ • L. Mashayekhy, M. M. Nejad, and D. Grosu are with the . His research interests include paralleland distributed systems, cloud computing, parallel algorithms, resource allocation, computer security, and topics at the border of computer science, game theory and economics. He has published more than ninety peer-reviewed papers in the above areas. He has served on the program and steering committees of several international meetings in parallel and distributed computing. He is a senior member of the ACM, the IEEE, and the IEEE Computer Society.