Applying Probability Model to The Genetic Algorithm Based Cloud Rendering Task Scheduling

Abstract

Abstract-There are huge amount of tasks and data to be processed in cloud rendering environment. How to effectively schedule them is the key to ensure the overall performance of the cloud rendering environment. In this paper, an improved task scheduling algorithm based on genetic algorithm (PMGA) and probability model is proposed, which aims to minimize the total time and cost of task scheduling . First, the fitness function relating to the total time and task cost is improved under the consideration of the user's satisfaction to the rendering services. Then, a probability model is constructed for the scheduling algorithm, which is used to achieve the non-linear adaptive adjustment of the crossover rate function and the mutation rate function. As a result, the evolutionary ability of poor individuals in the population can be enhanced, avoiding the stagnation in the early stages. Finally, experiments are performed to demonstrate that PMGA has a better ability of optimization than that of traditional adaptive genetic algorithm (AGA). Our approach contributes to reduce the total time and the scheduling cost of the cloud rendering tasks.

Constraint-Based Schedulability Analysis in Multiprocessor Real-Time Systems

“…There are two approaches to schedulability analysis in multiprocessor systems: utilization bound tests and simulation [3]- [13]. Schedulability analysis using utilization bound tests provides safe results but lacks flexibility, whereas simulation-based analysis is flexible but not safe because it cannot be applied to all possible state traces.…”