Annals of Operations Research
Algorithmic mechanism design (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Off-line admission control for general scheduling problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
A unified approach to approximating resource allocation and scheduling
Journal of the ACM (JACM)
Approximating the Throughput of Multiple Machines in Real-Time Scheduling
SIAM Journal on Computing
Truthful Mechanisms for One-Parameter Agents
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Scheduling Algorithms
Truthful and Near-Optimal Mechanism Design via Linear Programming
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Truthful mechanism design for multi-dimensional scheduling via cycle monotonicity
Proceedings of the 8th ACM conference on Electronic commerce
Algorithmic Game Theory
Bayesian algorithmic mechanism design
Proceedings of the forty-second ACM symposium on Theory of computing
Truthful mechanisms with implicit payment computation
Proceedings of the 11th ACM conference on Electronic commerce
Black-Box Randomized Reductions in Algorithmic Mechanism Design
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Bayesian incentive compatibility via fractional assignments
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Bayesian incentive compatibility via matchings
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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We introduce a novel pricing and resource allocation approach for batch jobs on cloud systems. In our economic model, users submit jobs with a value function that specifies willingness to pay as a function of job due dates. The cloud provider in response allocates a subset of these jobs, taking into advantage the flexibility of allocating resources to jobs in the cloud environment. Focusing on social-welfare as the system objective (especially relevant for private or in-house clouds), we construct a resource allocation algorithm which provides a small approximation factor that approaches 2 as the number of servers increases. An appealing property of our scheme is that jobs are allocated non-preemptively, i.e., jobs run in one shot without interruption. This property has practical significance, as it avoids significant network and storage resources for checkpointing. Based on this algorithm, we then design an efficient truthful-in-expectation mechanism, which significantly improves the running complexity of black-box reduction mechanisms that can be applied to the problem, thereby facilitating its implementation in real systems.