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Double auction-based pricing model is an efficient pricing model to balance users’ and providers’ benefits. Existing double auction mechanisms usually require both users and providers to bid with the unit price and the number of VMs. However, in practice users seldom know the exact number of VMs that meets their task requirements, which leads to users’ task requirements inconsistent with providers’ resource. In this paper, we propose a truthful double auction mechanism, including a matching process as well as a pricing and VM allocation scheme, to bridge users’ task requirements and providers’ resources in two-sided cloud markets. In the matching process, we design a cost-aware resource algorithm based on Lyapunov optimization techniques to precisely obtain the number of VMs that meets users’ task requirements. In the pricing and VM allocation scheme, we apply the idea of second-price auction to determine the final price and the number of provisioned VMs in the double auction. We theoretically prove our proposed mechanism is individual-rational, truthful and budget-balanced, and analyze the optimality of proposed algorithm. Through simulation experiments, the results show that the individual profits achieved by our algorithm are 12.35 and 11.02 percent larger than that of scale-out and greedy scale-up algorithms respectively for 90 percent of users, and the social welfare of our mechanism is only 7.01 percent smaller than that of the optimum mechanism in the worst case. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Bodyguard Allocation Problem (BAP) game, that illustrates the behavior of processes with contradictory individual goals in distributed systems. In particular, the game deals with the conflict of interest between two classes of processes that maximize/minimize their distance to a special process called the root. A solution of the BAP game represents a rooted spanning tree in which there exists a condition of equilibrium with maximum social welfare. We analyze the inefficiency of equilibria of the game based on both a completely cooperative and noncooperative approach. Additionally, we design two algorithms, CBAP and DBAP, that provide approximated solutions for the BAP game. We prove that both algorithms always terminate in a configuration with equilibrium and we analyze their running time based on the approach of cooperation used. We perform experimental simulations to compare the overall quality of equilibria obtained by the proposed algorithms. [ABSTRACT FROM AUTHOR]

Increasingly, many bandwidth-intensive applications have been ported to the cloud platform. In practice, however, some disadvantages including equipment failures, bandwidth overload and long-distance transmission often damage the QoS about data availability, bandwidth provision and access locality respectively. While some recent solutions have been proposed to cope with one or two of disadvantages, but not all. Moreover, as the number of data objects scales, most of the current offline algorithms solving a constraint optimization problem suffer from low computational efficiency. To overcome these problems, in this paper we propose an approach that aims to make fully efficient use of the cloud resources to enable bandwidth-intensive applications to achieve the desirable level of SLA-specified QoS mentioned above cost-effectively and timely. First we devise a constraint-based model that describes the relationship among data object placement, user cells bandwidth allocation, operating costs and QoS constraints. Second, we use the distributed heuristic algorithm, called DREAM-L, that solves the model and produces a budget solution to meet SLA-specified QoS. Third, we propose an object-grouping technique that is integrated into DREAM-L , called DREAM-LG, to significantly improve the computational efficiency of our algorithm. The results of hundreds of thousands of simulation-based experiments demonstrate that DREAM-LG provides much better data availability, bandwidth provision and access locality than the state-of-the-art solutions at modest cloud operating costs and within a small and acceptable range of time. [ABSTRACT FROM PUBLISHER]