Abstract
Cloud computing systems often have two conflicting objective, maximizing service performance, and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on the cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis) for cloud tasks DAG scheduling. The experimental results show that CRMO can produce outstanding cloud task scheduling solutions set.
Highlights
In cloud computing system, the service price is generally payas-you-go; cloud users need specify the application’s resource requirements to server provider
It is an NP-HARD problem to execute task scheduling for those applications represented by a directed acyclic graph (DAG) on clusters [2]
In literature [11], a mixed chemical reaction optimization algorithm was proposed for job-shop scheduling problem with fuzzy processing time, which balances its exploitation and exploration ability
Summary
The service price is generally payas-you-go; cloud users need specify the application’s resource requirements to server provider. To get high quality scheduling solutions Pareto frontier is very significant for cloud computing system It is an NP-HARD problem to execute task scheduling for those applications represented by a directed acyclic graph (DAG) on clusters [2]. In literature [11], a mixed chemical reaction optimization algorithm was proposed for job-shop scheduling problem with fuzzy processing time, which balances its exploitation and exploration ability. Li: Chemical Reaction Multi-Objective Optimization for Cloud Task DAG Scheduling high ability to address NP-HARD combinatorial optimization problem. A chemical reaction multi-objective optimization algorithm (CRMO) for maximizing service performance and minimizing computing cost on heterogeneity cloud computation system (HCCS) is proposed. The traditional CRO algorithm is designed for single-objective optimization problems and does not have a multi-objective processing mechanism.
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