Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user- and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments. © 2011 Elsevier Inc. All rights reserved.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Zahra Ashktorab, Djallel Bouneffouf, et al.
IJCAI 2025
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021