Truthful Online Scheduling with Commitments

Author:

Azar Yossi1,Kalp-Shaltiel Inna1,Lucier Brendan2,Menache Ishai3,Naor Joseph (Seffi)4,Yaniv Jonathan4

Affiliation:

1. Blavatnik School of CS, Tel Aviv University, Tel Aviv, Israel

2. Microsoft Research, Cambridge, MA, USA

3. Microsoft Research, Redmond, WA, USA

4. Technion - Israel Institute of Technology, Haifa, Israel

Funder

Israeli Centers for Research Excellence

Technion-Microsoft Electronic Commerce Research Center

Israel Science Foundation

Publisher

ACM

Reference21 articles.

1. Aaron Archer and Éva Tardos. 2001. Truthful mechanisms for one-parameter agents. In FOCS. 482--491. Aaron Archer and Éva Tardos. 2001. Truthful mechanisms for one-parameter agents. In FOCS. 482--491.

2. Azure. 2015. Azure Machine Learning Pricing. http://azure.microsoft.com/en-us/pricing/details/machine-learning/. (2015). Azure. 2015. Azure Machine Learning Pricing. http://azure.microsoft.com/en-us/pricing/details/machine-learning/. (2015).

3. Bandwidth Allocation with Preemption

4. Deadline-aware scheduling of big-data processing jobs

5. Bounding the Power of Preemption in Randomized Scheduling

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2. Online Throughput Maximization on Unrelated Machines: Commitment is No Burden;ACM Transactions on Algorithms;2023-01-31

3. An online auction-based incentive mechanism for soft-deadline tasks in Collaborative Edge Computing;Future Generation Computer Systems;2022-12

4. Truthful Online Scheduling of Cloud Workloads under Uncertainty;Proceedings of the ACM Web Conference 2022;2022-04-25

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