In this paper, we discuss the issues with the Hadoop task assignment scheme, and present an improved scheme, which is based on an optimal algorithm for minimum makespan scheduling and explicitly strives to shorten the duration of the map phase of MapReduce jobs.
We give an algorithm to construct minimum makespan transmission schedules for data gathering under the following hypotheses: the communication graph G is a tree network, the transmissions in the network can interfere with each other up to distance m, where m≥2, and no buffering is allowed at intermediate nodes.
To the best of our knowledge, this is the first practical parallel approximation algorithm for the minimum makespan scheduling problem that maintains the approximation guarantees of the sequential PTAS and it is specifically designed for execution on shared-memory parallel machines.
An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented.
An effective scheduling algorithm for distributed computing systems is essential for assigning client's tasks to run on a set of processors at a minimum makespan.
Minimum makespan scheduling is one classical combinatorial optimization problem, where given a set of jobs and a cluster of machines, the scheduling is to assign jobs to machines so that the makespan (maximum completion time of all jobs) is minimized.
Second, we present a group-strategyproof cost sharing mechanism for the minimum makespan scheduling problem that is tight with respect to budget balance and efficiency.
Energy-minimum fourier transform algorithm.
The main objective of this part of the scheduling process is to determine the minimum scheduling length (makespan) while satisfying all precedence constraints.
Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem.
