Annals of Operations Research
Algorithmic mechanism design (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Strengthening integrality gaps for capacitated network design and covering problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
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
Online auctions with re-usable goods
Proceedings of the 6th ACM conference on Electronic commerce
Weak monotonicity suffices for truthfulness on convex domains
Proceedings of the 6th ACM conference on Electronic commerce
Truthful mechanism design for multi-dimensional scheduling via cycle monotonicity
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Algorithmic Game Theory
Characterizing truthful mechanisms with convex type spaces
ACM SIGecom Exchanges
The cost of a cloud: research problems in data center networks
ACM SIGCOMM Computer Communication Review
Reining in the outliers in map-reduce clusters using Mantri
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Mechanism design with uncertain inputs: (to err is human, to forgive divine)
Proceedings of the forty-third annual ACM symposium on Theory of computing
On optimal multidimensional mechanism design
ACM SIGecom Exchanges
A truthful mechanism for value-based scheduling in cloud computing
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Efficient online scheduling for deadline-sensitive jobs: extended abstract
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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We consider a market-based resource allocation model for batch jobs in cloud computing clusters. In our model, we incorporate the importance of the due date of a job rather than the number of servers allocated to it at any given time. Each batch job is characterized by the work volume of total computing units (e.g., CPU hours) along with a bound on maximum degree of parallelism. Users specify, along with these job characteristics, their desired due date and a value for finishing the job by its deadline. Given this specification, the primary goal is to determine the scheduling} of cloud computing instances under capacity constraints in order to maximize the social welfare (i.e., sum of values gained by allocated users). Our main result is a new ( C/(C-k) ⋅ s/(s-1))-approximation algorithm for this objective, where C denotes cloud capacity, k is the maximal bound on parallelized execution (in practical settings, k l C) and s is the slackness on the job completion time i.e., the minimal ratio between a specified deadline and the earliest finish time of a job. Our algorithm is based on utilizing dual fitting arguments over a strengthened linear program to the problem. Based on the new approximation algorithm, we construct truthful allocation and pricing mechanisms, in which reporting the job true value and properties (deadline, work volume and the parallelism bound) is a dominant strategy for all users. To that end, we provide a general framework for transforming allocation algorithms into truthful mechanisms in domains of single-value and multi-properties. We then show that the basic mechanism can be extended under proper Bayesian assumptions to the objective of maximizing revenues, which is important for public clouds. We empirically evaluate the benefits of our approach through simulations on data-center job traces, and show that the revenues obtained under our mechanism are comparable with an ideal fixed-price mechanism, which sets an on-demand price using oracle knowledge of users' valuations. Finally, we discuss how our model can be extended to accommodate uncertainties in job work volumes, which is a practical challenge in cloud settings.