Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Economic models for allocating resources in computer systems
Market-based control
Mariposa: a wide-area distributed database system
The VLDB Journal — The International Journal on Very Large Data Bases
A computational economy for grid computing and its implementation in the Nimrod-G resource broker
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
Market-based cluster resource management
Market-based cluster resource management
Group Strategyproof Mechanisms via Primal-Dual Algorithms
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Free-riding and whitewashing in peer-to-peer systems
Proceedings of the ACM SIGCOMM workshop on Practice and theory of incentives in networked systems
Contract-based load management in federated distributed systems
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Algorithmic Game Theory
Managing virtual money for satisfaction and scale up in P2P systems
DaMaP '08 Proceedings of the 2008 international workshop on Data management in peer-to-peer systems
An Economic Model for Self-Tuned Cloud Caching
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Google fusion tables: data management, integration and collaboration in the cloud
Proceedings of the 1st ACM symposium on Cloud computing
CloudCmp: shopping for a cloud made easy
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Distributed systems meet economics: pricing in the cloud
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
An implementation of the contract net protocol based on marginal cost calculations
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Scalable clustering algorithm for N-body simulations in a shared-nothing cluster
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Predicting cost amortization for query services
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Flexible use of cloud resources through profit maximization and price discrimination
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Cost exploration of data sharings in the cloud
Proceedings of the 16th International Conference on Extending Database Technology
COCCUS: self-configured cost-based query services in the cloud
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Toward practical query pricing with QueryMarket
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Optimized data management for e-learning in the clouds towards Cloodle
Proceedings of the Fourth Symposium on Information and Communication Technology
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Data-management-as-a-service systems are increasingly being used in collaborative settings, where multiple users access common datasets. Cloud providers have the choice to implement various optimizations, such as indexing or materialized views, to accelerate queries over these datasets. Each optimization carries a cost and may benefit multiple users. This creates a major challenge: how to select which optimizations to perform and how to share their cost among users. The problem is especially challenging when users are selfish and will only report their true values for different optimizations if doing so maximizes their utility. In this paper, we present a new approach for selecting and pricing shared optimizations by using Mechanism Design. We first show how to apply the Shapley Value Mechanism to the simple case of selecting and pricing additive optimizations, assuming an offline game where all users access the service for the same time-period. Second, we extend the approach to online scenarios where users come and go. Finally, we consider the case of substitutive optimizations. We show analytically that our mechanisms induce truthfulness and recover the optimization costs. We also show experimentally that our mechanisms yield higher utility than the state-of-the-art approach based on regret accumulation.