Scheduling in Computer and Manufacturing Systems
Scheduling in Computer and Manufacturing Systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The ANL/IBM SP Scheduling System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Epsilon-constraint with an efficient cultured differential evolution
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Towards a general model of the multi-criteria workflow scheduling on the grid
Future Generation Computer Systems
Multi-cost job routing and scheduling in Grid networks
Future Generation Computer Systems
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Rescheduling for reliable job completion with the support of clouds
Future Generation Computer Systems
Improving job scheduling algorithms in a grid environment
Future Generation Computer Systems
Secondary Job Scheduling in the Cloud with Deadlines
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
An optimization-based heuristic for the Multi-objective Undirected Capacitated Arc Routing Problem
Computers and Operations Research
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Future Generation Computer Systems
Scheduling strategies for optimal service deployment across multiple clouds
Future Generation Computer Systems
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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 @e-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.