یک متد با محدودیت ϵ تقریبی برای زمان بندی کار چند هدفه در ابر

• We formulate the multi-objective off-line job scheduling problem in the cloud.• We optimize the makespan, the total average waiting time and the used hosts.• We generate a set of test instances suitable for the problem.• We define an approximate ϵϵ-constraint method.• We compare the proposed solution approach with the weighted sum method.Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform.In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate ϵϵ-constraint method, tested on a set of instances and compared with the weighted sum (WS) method.The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions.

Cloud computing is a revolutionary paradigm suitable to change the way of accessing both hardware and software in order to produce, price, provide and deliver services and computational resources to users. Users can run their applications (jobs) without paying for software licenses, using well equipped machines (hosts) and high performance computational resources. This paper addresses a multi-objective job scheduling problem in a homogeneous cloud infrastructure considering the minimization of the total average waiting time of the jobs, of the total waiting time of the jobs belonging to the longest working schedule (makespan) and the number of used hosts. It takes into account anoff-line job scheduling scenario and, therefore, the number of jobs to run and their resource requirements are known a-priori. The main contributions are as follows: • a multi-objective formulation of the off-line job scheduling problem in a homogeneous cloud computing platform; • an approximate ϵϵ-constraint method for solving the problem; • a detailed experimental analysis for evaluating the quality of the proposed approach. With reference to the last contribution, first we implement an instance generator in order to determine a set of problems considered during the experimental phase. Then, we implement an alternative solution approach based on the weighted sum (WS) method. Finally, we compare the two approaches on the set of generated instances. This paper is organized as follows: Section 2 reviews some significant literary contributions, Section 3 provides a high level description of the problem, Section 4 describes the multi-objective mathematical formulation of the problem. Sections 4.1, 4.2 and 4.3 detail the two solution approaches taken into account, while Section 5 describes the generated scenarios and discusses the computational results. Finally, Section 6 concludes the work and suggests some future developments.

This paper addresses a multi-objective off-line job scheduling problem on a homogeneous cloud computing platform. For that, an approximate ϵϵ-constraint method is designed, implemented and evaluated. Moreover, the numerical results are compared with the WS method traditionally used for solving multi-objective optimization problems. The solution qualities are estimated in terms of the number of no dominated solutions, their distribution and diversification introducing two metrics: View the MathML sourceΣspacing and View the MathML sourceΣspread. Further developments will be carried out on the implementation of alternative heuristic approaches used to populate ΩΩ in order to detect more diverse sets of solutions. Moreover, an on-line job scheduling version of the problem will be examined and studied and also heterogeneous cloud platforms will be taken into consideration. In particular, a redefinition of the mathematical model will be necessary in order to explicitly include the costs for data transfer.