On multiprocessor task scheduling using efficient state space search approaches

TL;DR: The results indicate that the proposed parallel greedy algorithm approach is very effective in terms of reduced total completion time or makespan (C"m"a"x) for the attempted problems.

TL;DR: This study considers nine scheduling algorithms which are frequently used to the best of the authors' knowledge: min–min, chaining, A*, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, and DSH with task duplication, and presents a detailed analysis of the scalability, advantages and disadvantages of each algorithm.

TL;DR: The proposed MILP solution uses problem specific knowledge to eliminate the need to linearise the bi-linear equations arising out of communication delays and displays a drastic improvement in performance, which allows to solve larger problems optimally.

TL;DR: An adaptation of the Heterogeneous Earliest-Finish-Time (HEFT) algorithm is proposed, called here P-HEFT, to handle parallel tasks in heterogeneous clusters with good efficiency without compromising the makespan.

TL;DR: Two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time are presented, called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm.

TL;DR: Performance and Scalability of Parallel Systems, General Issues in Mapping Systolic Systems Onto Parallel Computers, and Speedup Anomalies in Parallel Search Algorithms.

TL;DR: A taxonomy that classifies 27 scheduling algorithms and their functionalities into different categories is proposed, with each algorithm explained through an easy-to-understand description followed by an illustrative example to demonstrate its operation.