Abstract
In cloud computing scenario, workflow scheduling algorithms require multiple conflicting goals to be optimized. Optimal makespan, reduced energy consumption and reliability of execution are the most important goals to be optimized. In this paper, we propose a multi-objective workflow scheduling algorithm in cloud computing—ERAWS, which optimizes three conflicting criteria: makespan, reliability of task execution and energy consumption. We validate and analyze the performance of our algorithm by using the CloudSim toolkit. We use randomly generated task graphs and task graphs for Gaussian elimination and fast Fourier transformation to represent workflow applications. The simulation results show that ERAWS algorithm gains significantly in terms of makespan and energy consumption, in real-world scenarios where reliability and energy consumption are important issues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sadiku MN, Musa SM, Momoh OD (2014) Cloud computing: opportunities and challenges. IEEE Potentials 33:34–36
Magkils G, Semeraro G, Albonesi DH, Dropsho SG, Dwarkadas S, Scott ML (2003) Dynamic frequency and voltage scaling for multiple-clock-domain microprocessor. IEEE Micro 23:62–68
Garey MR, Johnson DS (2002) Computers and intractability, vol 29. W.H. Freeman
Garraghan P, Townend P, Xu J (2014) An empirical failure-analysis of a large-scale cloud computing environment. In: 15th IEEE international symposium on high-assurance systems engineering (HASE). IEEE, pp 113–120
Tang X, Li K, Qiu M, Sha EHM (2012) A hierarchical reliability-driven scheduling algorithm in grid systems. J Par Dist Comp 72:525–535
Guo S, Huang HZ, Wang Z, Xie M (2011) Grid service reliability modeling and optimal task scheduling considering fault recovery. IEEE Trans Reliab 60:263–274
Zio E (2013) The Monte Carlo simulation method for system reliability and risk analysis. Springer, London
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw: Pract Exp 41:23–50
Cosnard M, Marrakchi M, Robert Y, Trystram D (1988) Parallel Gaussian elimination on an MIMD computer. Parallel Comput 6:275–296
Chung YC, Ranka S (1992) Applications and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed memory multiprocessors. In: Supercomputing’92, Proceedings. IEEE, pp 512–521
Kim KH, Beloglazov A, Buyya R (2011) Power‐aware provisioning of virtual machines for real‐time cloud services. Concurr Comput: Pract Exp 23:1491–1505
Minas L, Ellison B (2009) Energy efficiency for information technology: how to reduce power consumption in servers and data centers. Intel Press
Topcuoglu H, Hariri S, Wu MY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13:260–274
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rehani, N., Garg, R. (2018). Reliability-Aware Green Scheduling in Cloud Computing. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-3932-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-3932-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3931-7
Online ISBN: 978-981-10-3932-4
eBook Packages: EngineeringEngineering (R0)