Particle swarm optimization (PSO) is a heuristic global optimization method, proposed
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …

On parallel and distributed heterogeneous computing systems, a heuristic-based task
scheduling algorithm typically consists of two phases: task prioritization and processor …

Cloud computing has attracted great attentions in research community because of its
ubiquitous, unlimited computing resources, low cost, and flexibility owing to virtualization …

Heterogeneous cloud datacenters are well-suited and cost-efficient platforms for execution
of scientific workflows requested from academics. Workflow scheduling algorithms have …

This work presents a novel hybrid meta-heuristic that combines particle swarm optimization
and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) …

Task scheduling is one of the major issues to achieve high performance in distributed
systems such as Grid, Peer-to-Peer and cloud environment. Generally, there are two phases …

Resource-constrained project scheduling problem (RCPSP) is an important, but
computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly …

This paper proposes a genetic algorithm (GA) based energy-efficient design-time task
scheduling algorithm, AGATS, for an asymmetric multiprocessor system-on-chip. Unlike …

To solve the problem of task scheduling in the cloud computing system, this paper proposes
a scheduling algorithm for cloud computing based on the driver of dynamic essential path …

In today's world, cloud computing plays a significant role in the development of an effective
computing paradigm that adds more benefits to the modern Internet of Things (IoT) …