Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Economic models for allocating resources in computer systems
Market-based control
Workload Modeling for Performance Evaluation
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
The dawning of the autonomic computing era
IBM Systems Journal
Reducing Power with Performance Constraints for Parallel Sparse Applications
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 11 - Volume 12
Energy conservation in heterogeneous server clusters
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Autonomic Self-Optimization According to Business Objectives
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Analyzing Market-Based Resource Allocation Strategies for the Computational Grid
International Journal of High Performance Computing Applications
Static allocation of resources to communicating subtasks in a heterogeneous ad hoc grid environment
Journal of Parallel and Distributed Computing - Special issue: Algorithms for wireless and ad-hoc networks
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications
IEEE Transactions on Parallel and Distributed Systems
Temperature aware task scheduling in MPSoCs
Proceedings of the conference on Design, automation and test in Europe
On building next generation data centers: energy flow in the information technology stack
COMPUTE '08 Proceedings of the 1st Bangalore Annual Compute Conference
A Measurement-Based Method for Improving Data Center Energy Efficiency
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
Optimal algorithms and inapproximability results for every CSP?
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Communications of the ACM
Web Customer Modeling for Automated Session Prioritization on High Traffic Sites
UM '07 Proceedings of the 11th international conference on User Modeling
GreedEx--a scalable clearing mechanism for utility computing
Electronic Commerce Research
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Eliciting honest value information in a batch-queue environment
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
A Market Design for Grid Computing
INFORMS Journal on Computing
Cost-aware scheduling for heterogeneous enterprise machines (CASH'EM)
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
Economically enhanced resource management for internet service utilities
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
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The cost of electricity for datacenters is a substantial operational cost that can and should be managed, not only for saving energy, but also due to the ecologic commitment inherent to power consumption. Often, pursuing this goal results in chronic underutilization of resources, a luxury most resource providers do not have in light of their corporate commitments. This work proposes, formalizes and numerically evaluates DEEP-Sam, for clearing provisioning markets, based on the maximization of welfare, subject to utility-level dependant energy costs and customer satisfaction levels. We focus specifically on linear power models, and the implications of the inherent fixed costs related to energy consumption of modern datacenters and cloud environments. We rigorously test the model by running multiple simulation scenarios and evaluate the results critically. We conclude with positive results and implications for long-term sustainable management of modern datacenters.