Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm
Introduction
Cloud applications are very popular in recent years. Especially, cloud computing has emerged as a promising approach to rent IT infrastructures on a short-term pay-per-usage basis. With cloud computing, companies can scale up to massive capacities in an instant without having to invest in new infrastructure, train new personnel, or license new software. Cloud computing is of particular benefit to small and medium-sized businesses who wish to completely outsource their data-center infrastructure, or large companies who wish to get peak load capacity without incurring the higher cost of building larger data centers internally. In both instances, service consumers use what they need on the Internet and pay only for what they use. Thus, operators of so-called Infrastructure-as-a-Service (IaaS) clouds, like Amazon EC2 [3], let their customers allocate, access, and control a set of virtual machines which run inside their data centers and only charge them for the period of time the machines are allocated. Therefore, workflow management on cloud computing becomes more important, when many tasks are sent to cloud environment at the same time.
Researches on specification and scheduling of workflows have concentrated on temporal and causality constraints, which specify existence and order dependencies among tasks. However, another set of constraints that specify resource allocation is also equally important. Execution of a task has a cost and this may vary depending on the resources allocated. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints provide proper resource allocation to tasks [27], [5], [12], [21], [10], [19], [38], [14], [18]. In cloud computing environment, machines are located in different regions and have disparate processing abilities, characteristics (number of CPU cores, amount of main memory, etc.), and cost. In these situations, the cost and makespan associated with the task schedule and the resources allocated should be taken into account. Therefore, task scheduling and resource allocation should be carefully coordinated and optimized jointly in order to achieve an overall cost and time-effective schedule. That is, to find optimal task schedules by minimizing cost and makespan.
This study developed cost and time models for computing the cost and time in a task schedule. The cost model includes the processing and receiving cost and the time model incorporates receiving, processing, and waiting time. A parallel-machine scheduling involving both task processing and resource allocation was studied by using an improved differential evolution algorithm (IDEA). The proposed IDEA combines Taguchi method [23], [22], [32] and a differential evolution algorithm (DEA) [30], [31], [17]. The DEA had a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method was used to exploit the better individuals on micro-space to be potential offspring. The objective is to find optimal task schedules by minimizing total cost and makespan. Therefore, the multi-objective optimization approach, which was the non-dominated sorting technique [28], [8], [9], not with normalized single-objective method, was applied to find the Pareto front of total cost and makespan. Finally, based on suitable schedules from the Pareto-optimal solution sets, efficiency of the task distribution and the resource utilization were discussed and analyzed.
The paper is organized as follows. Section 2 defines the research issue. The related works are briefly described in Section 3. The proposed approach is illustrated in Section 4. Section 5 shows case study, results, and discussions. Finally, Section 6 concludes the study.
Section snippets
Problem description
To generalize the discussion, the assumption is that there is a set of cloud customer tasks and each task has many subtasks with precedence constraints. Each subtask is allowed to be processed on any given available resource. A cloud resource has a given level of capacity (e.g., CPU, memory, network, storage). A subtask is processed on one resource at a time and the given resources are available continuously. The cloud computing discussion in this study highlighted the fact that either cloud
Related works
This section gives a brief review about differential evolution algorithms, Taguchi method, and a multi-objective approach used in this study.
The proposed approach for task scheduling and resource allocation
The proposed IDEA includes two calculated models for cloud computing and five new ideas for scheduling. They are shown in the following. The cost and time models are proposed for cloud scheduling. The cost model includes rent cost for processing and receiving subtask. The time model incorporates receiving, processing, and waiting time. (1) A subtask-resource-based technique is proposed to encode a schedule. The technique is useful to avoid generating infeasible individual on initialization,
Design examples and comparisons
The performance index for measuring the quality of Pareto-optimal sets and two examples were used for a performance comparison of the IDEA, DEA (i.e., IDEA without the Taguchi method), and NSGA-II.
Conclusions
By introducing the Taguchi method within the DEA framework, the proposed IDEA approach preserves the global solution finding merit of the DEA as a design tool to optimize task scheduling and resource allocation on cloud computing environment. The major contribution of the IDEA is the use of an OA of the Taguchi method as mask mutation operator to account for task-subtask encoding in order to generate improved offspring. Doing so obtains Pareto-optimal solutions within two objectives based on
Acknowledgment
This work was supported in part by the National Science Council, Taiwan, ROC, under Grant Numbers NSC99-2221-E-151-071-MY3, NSC100-2221-E153-001 and NSC101-2221-E153-003.
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