A Strategyproof Auction Mechanism for Grid Scheduling with Selfish Entities (original) (raw)
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Grid computing provides an extremely promising distributed paradigm for executing large-scale resource-intensive applications. Given a distributed pool of resources, a grid user is faced with the problem of selecting an optimal set of resources. We call this problem the resource selection problem and our approach to modelling and solving this problem is through a reverse combinatorial auction. The resource owners submit bids on combinations of resources or tasks in response to the grid user's request for a bundle of resources. The objective of the grid user is to minimise an appropriately defined cost function based on these bids. The resource selection problem therefore becomes the winner determination problem of the reverse combinatorial auction. Two variants of the problem are considered: (1) resource selection with task-level trading and (2) resource selection with resource-level trading. In both the cases, the resource selection problem turns out to be an integer linear programming problem. We have compared the performance of the proposed resource selection protocols against that of a cost optimisation protocol and a time optimisation protocol which are part of the Nimrod-G resource broker. The protocols proposed are found to have superior overall performance in terms of turnaround time and total cost.
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The core goal of resource management is to establish a mutual agreement between a resource producer and a resource consumer by which the provider agrees to supply a capability that can be used to perform some tasks on behalf of the con- sumer. Market-based approaches introduce money and pricing as the technique for coordination be- tween consumers and producers of resources. In this paper, we propose a market-based mechanism to allocate computational resources (CPU time) with a single central Market in a local Grid. In such a network whenever any node can offer idle CPU time to the Grid and whenever a node has some tasks waiting for free CPU, it may request the resource from the Grid. In our approach, con- sumers and producers are autonomous agents that make their own decisions according to their ca- pabilities and their local knowledge. Continuous Double Auction model is used as a technique us- ing which these selfish agents can coordinate their work and make their decision. The performance of this mechanism is evaluated and is compared with the simple FCFS mechanism.
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As users increasingly require better quality of service from Grids, resource management and scheduling mechanisms have to evolve in order to satisfy competing demands on limited resources. Traditional schedulers for Grids are system centric and favour system performance over increasing user's utility. On the other hand market oriented schedulers are price-based systems that favour users but are based solely on user valuations. This paper proposes a novel meta-scheduler that unifies the advantages of both the systems for benefiting both users and resources. In order to do that, we design a valuation metric for user's applications and computational resources based on multi-criteria requirements of users and resource load. The meta-scheduler maps user applications to suitable distributed resources using a Continuous Double Auction (CDA). Through simulation, we compare our scheduling mechanism against other common mechanisms used by current meta-schedulers. The results show that our meta-scheduler mechanism can satisfy more users than the others while still meeting traditional system-centric performance criteria such as average load and deadline of applications
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In resource-limited environment, grid users compete for limited resources, and how to guarantee tasks' victorious probabilities is one of the most primary issues that a resource scheduling model cares. In order to guarantee higher task's victorious probabilities in grid resources scheduling situations, a novel model, namely ESPSA (Extended Second Price Sealed Auction), is proposed. The ESPSA model introduces an analyst entity, and designs analyst's prediction algorithm based on Hidden Markov Model (HMM). In ESPSA model, grid resources are sold through second price sealed auction. Moreover, to achieve high victorious probabilities, the user brokers who are qualified to participate in the auctions will predict other players' bids and then carry out the most beneficial bids. The ESPSA model is simulated based on GridSim toolkit. Simulation results show that the ESPSA model assures a higher victorious probability and superior to other traditional algorithms. Moreover, we analyze the existence of Nash equilibrium based on simulation results, thus, any participant who changes its strategy unilaterally could not make the results better.
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Grid scheduling, that is, the allocation of distributed computational resources to user applications, is one of the most challenging and complex task in Grid computing. The problem of allocating resources in Grid scheduling requires the definition of a model that allows local and external schedulers to communicate in order to achieve an efficient management of the resources themselves. To this aim, some economic/market-based models have been introduced in the literature, where users, external schedulers, and local schedulers negotiate to optimize their objectives. In this paper, we propose a tender/contract-net model for Grid resource allocation, showing the interactions among the involved actors. The performance of the proposed market-based approach is experimentally compared with a round-robin allocation protocol.