Investigation of Cloud Scheduling Algorithms for Resource Utilization Using CloudSim

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientific HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several efficient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a difficult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Sufferage and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not efficient enough to exploit the full computing capabilities of Cloud virtual machines

Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study

The permutation flowshop scheduling problem has been thoroughly studied in recent decades, both from single objective as well as from multi-objective perspectives. To the best of our knowledge, little has been done regarding the multi-objective flowshop with Pareto approach when sequence dependent setup times are considered. As setup times and multi-criteria problems are important in industry, we must focus on this area. We propose a simple, yet powerful algorithm for the sequence dependent setup times flowshop problem with several criteria. The presented method is referred to as Restarted Iterated Pareto Greedy or RIPG and is compared against the best performing approaches from the relevant literature. Comprehensive computational and statistical analyses are carried out in order to demonstrate that the proposed RIPG method clearly outperforms all other algorithms and, as a consequence, it is a state-of- art method for this important and practical scheduling problemThe authors thank the anonymous referees for their careful and detailed comments which have helped improve this manuscript considerably. This work is partially financed by the Spanish Ministry of Science and Innovation, under the projects "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theorerical Advances" with reference DPI2008-03511/DPI and "RESULT-Realistic Extended Scheduling Using Light Techniques" with reference DPI2012-36243-C02-01 and by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R+D program "Ayudas dirigidas a Institutos Tecnologicos de la Red IMPIVA" during the year 2011, with project numbers IMDEEA/2011/142 and IMDEEA/2012/143.Ciavotta, M.; Minella, GG.; Ruiz García, R. (2013). Multi-objective sequence dependent setup times permutation flowshop: A new algorithm and a comprehensive study. European Journal of Operational Research. 227(2):301-313. https://doi.org/10.1016/j.ejor.2012.12.031S301313227

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn