Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
In a heterogeneous multi-cluster (HMC) system, processor allocation is responsible for choosing available processors among clusters for job execution. Traditionally, processor allocation in HMC considers only resource fragmentation or processor heterogeneity, which leads to heuristics such as Best-Fit (BF) and Fastest-First (FF). However, those heuristics only favor certain types of workloads and cannot be changed adaptively. In this paper, a temporal look-ahead (TLA) method is proposed, which uses an allocation simulation process to guide the decision of processor allocation. Thus, the allocation decision is made dynamically according to the current workload and system configurations. We evaluate the performance of TLA by simulations, with different workloads and system configurations, in terms of average turnaround time. Simulation results indicate that, with precise runtime information, TLA outperforms traditional processor allocation methods and has up to an 87% performance improvement. © 2013 Elsevier Inc. All rights reserved.
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Giuseppe Romano, Aakrati Jain, et al.
ECTC 2025
Wooseok Choi, Tommaso Stecconi, et al.
Advanced Science
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence