On the use of HCM to develop a resource allocation algorithm for heterogeneous clusters

Soares, Thiago Marques; Santos, Rodrigo Weber dos; Lobosco, Marcelo

doi:10.1002/cpe.4899

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…A straightforward solution is provided for distributed infrastructure Cloud taking into consideration several characteristics for Cloud providers such as scalable services, on‐demand usage, and over‐purchase resources. It should be noted, however, that only a few works have been reported for the usage of heterogeneous servers …”

Section: Related Workmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

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Section: Related Workmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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“…The paper “On the Use of HCM to develop a Resource Allocation Algorithm for Heterogeneous Clusters” by Soares et al presents a resource allocation algorithm that considers the characteristics of regular applications to choose the subset of processors, accelerators, and networks that minimize their parallel execution time in a small heterogeneous cluster environment. The resource allocation algorithm uses HCM (Heterogeneous Cluster Model) to estimate the execution time of the parallel applications.…”

Section: Themes Of the Special Issuementioning

confidence: 99%