Now showing items 1-4 of 4
A learning architecture for scheduling workflow applications in the cloud
The scheduling of workflow applications involves the mapping of individual workflow tasks to computational resources, based on a range of functional and non-functional quality of service requirements. Workflow applications ...
Particle swarm optimisation with gradually increasing directed neighbourhoods
(Association for Computing Machiner, 2011)
Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between the exploration of the solution space in the early stages and the exploitation of the ...
A Comparison of Petri Net and System Dynamics Approaches for Modelling Dynamic Feedback Systems
Petri nets are a valuable tool that can be used to simulate workflow systems. They are based on a state-transition approach, and in common with discrete event simulation, events can be scheduled to fire at different time ...
Applying reinforcement learning towards automating resource allocation and application scalability in the cloud
Public Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines ...