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Abstract
High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed resources. However, earlier clustering methods ignore the effect of failures on the system, despite their significant impact on large-scale distributed resources, such as Clouds. In this paper, we present a new fault-tolerant task clustering method called FT-HCC that is designed by including the workflow execution time (makespan) and execution cost constraints, which are used to increase workflow performance. The proposed method is implemented and evaluated in a simulation-based approach, using a real-time workflow execution to analyze performance improvement. The results consolidate that the proposed strategies and techniques work efficiently in terms of fault tolerance and improve both workflow makespan and execution cost when compared to existing approaches.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Miloud Khaldi
Miloud Khaldi obtained his MSC in computer science from the University of Mascara, Algeria. He is a PhD student in the Computer Science Department at the University of Mascara, Algeria. His research interests include grid computing, cloud computing and fault tolerance.
Mohammed Rebbah
Mohammed Rebbah obtained his MSC in computer science from the University of Sciences and Technology of Oran (USTO), Algeria. After that he received his PhD in computer science in 2015, in USTO, Algeria. Actually, he is an Assistant Professor in Computer Sciences Department at the University of Mascara, Algeria. His research interests are in grid computing, cloud computing and distributed data mining.
Boudjelal Meftah
Boudjelal Meftah is an Associate Professor at the Computer Science Department of Mascara University, Algeria. He obtained his engineering diploma in computer science in 1997 from Sidi Belabes University, his MSC in computer science option pattern recognition and artificial intelligence in 2005 from The University of USTO, Oran, Algeria, and the PhD degree in computer science from the University of USTO, Oran, Algeria in 2011. From September 2009 to August 2011 he joined GREYC Laboratory of the University of Caen Basse Normandie in France as a member in Image Group. Actually, he is the head of research team (TVIM) in University of Mascara. His research interests include pattern recognition application, neural network, spiking neural networks and also image processing. He has publications in many journals and participated in many international conferences. He served as reviewer of known journals and joined some international conferences as an organizing, a scientific program committee’s member.
Omar Smail
Omar Smail received his Engineer Degree in Computer Science in 1994 from the University of Es-Senia of Oran, and Master’s degree in communication from Mascara University in 2004. He obtained his PhD degree in the area of engineering ad hoc networks protocols from USTO in 2015. At present, he is working as Assistant professor in Department of Computer Science, University of Mascara, Algeria. His teaching and research interests include Ad hoc and sensors network, Qos, Distributed system, power management in wireless communication and traffic engineering. Is head and Member in national research projects.
