Least expected cost query optimization: an exercise in utility
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Strengthening integrality gaps for capacitated network design and covering problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
On computing functions with uncertainty
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Least expected cost query optimization: what can we expect?
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Allocating Bandwidth for Bursty Connections
SIAM Journal on Computing
Computing the Median with Uncertainty
SIAM Journal on Computing
Query strategies for priced information
Journal of Computer and System Sciences - Special issue on STOC 2000
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A measurement-based analysis of multihoming
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Tight Approximation Results for General Covering Integer Programs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Approximate replication
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Boosted sampling: approximation algorithms for stochastic optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
An Edge in Time Saves Nine: LP Rounding Approximation Algorithms for Stochastic Network Design
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Asking the right questions: model-driven optimization using probes
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Improving the reliability of internet paths with one-hop source routing
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Exceeding expectations and clustering uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Adaptive Uncertainty Resolution in Bayesian Combinatorial Optimization Problems
ACM Transactions on Algorithms (TALG)
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In several systems applications, parameters such as load are known only with some associated uncertainty, which is specified, or modeled, as a distribution over values. The performance of the system optimization and monitoring schemes can be improved by spending resources such as time or bandwidth in observing or resolving the values of these parameters. In a resource-constrained situation, deciding which parameters to observe in order to best optimize the expected system performance (or in general, optimize the expected value of a certain objective function) itself becomes an interesting optimization problem. In this article, we initiate the study of such problems that we term “model-driven optimization”. In particular, we study the problem of optimizing the minimum value in the presence of observable distributions. We show that this problem is NP-Hard, and present greedy algorithms with good performance bounds. The proof of the performance bounds are via novel sub-modularity arguments and connections to covering integer programs.