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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 1/2015

01.01.2015

QRSF: QoS-aware resource scheduling framework in cloud computing

verfasst von: Sukhpal Singh, Inderveer Chana

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2015

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Abstract

Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms.
Vorheriger Artikel Task ranking and allocation in list-based workflow scheduling on parallel computing platform
Nächster Artikel A survey of genome sequence assembly techniques and algorithms using high-performance computing

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Literatur
1.
Armando MF, Rean G, Anthony DJ, Randy K, Andy K, Gunho L, David P, Ariel R, Ion S, Matei Z (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRef
2.
Rimal BP, Jukan A, Katsaros D, Goeleven Y (2011) Architectural requirements for cloud computing systems: an enterprise cloud approach. J Grid Comput 9(1):3–26CrossRef
3.
Rimal P, Choi E (2009) A taxonomy and survey of cloud computing systems. In: Fifth international joint conference on INC, IMS and IDC, Seoul, Korea
4.
Buyya R, Yeoa CS, Venugopala S, Broberga J, Brandicc I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616CrossRef
5.
Dillon T, Wu C, Chang E (2010) Cloud computing: issues and challenges. In: 24th IEEE international conference on advanced information networking and applications, Perth, Australia
6.
7.
Singh S, Chana I (2012) Cloud based development issues: a methodical analysis. Int J Cloud Comput Serv Sci 2(1):73–84
8.
Cirne W, Berman F (2001) A comprehensive model of the supercomputer workload. In: Fourth annual IEEE international workshop on workload characterization, WWC-4, Austin, Texas
9.
Gmach D, Rolia J, Cherkasova L, Kemper A (2007) Workload analysis and demand prediction of enterprise data center applications. In: IEEE 10th international symposium on workload characterization, IISWC ’07, Boston, MA, USA
10.
Cherkasova L, Gupta M (2002) Characterizing locality, evolution, and life span of accesses in enterprise media server workloads. In: 12th international workshop on Network and operating systems support for digital audio and video, FL, USA
11.
Rolia J, Cherkasova L, Arlitt M, Andrzejak A (2005) A capacity management service for resource pools. In: 5th international workshop on software and performance, Illes Balears, Spain
12.
Arlitt MF, Williamson CL (1996) Web server workload characterization: the search for invariants. In: ACM SIGMETRICS international conference on measurement and modeling of computer systems, SIGMETRICS ’96, Philadelphia, PA, USA
13.
Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing SLA violations. In: IFIP/IEEE integrated network management, Bombay
14.
Verma A, Dasgupta G, Kumar T, Prad N (2009) Server workload analysis for power minimization using consolidation. In: USENIX annual technical conference, San Jose, CA
15.
Khan A, Yan X, Tao S, Anerousis N (2012) Workload characterization and prediction in the cloud: a multiple time series approach. In: Network operations and management symposium (NOMS), IEEE, Krakow, Poland
16.
Chen SJ, Liang PH, Yang J-M (2010) Workload evaluation and analysis on virtual systems. In: IEEE international conference on E-business engineering, 2010
17.
Bossche RVD, Vanmechelen K, Broeckhove J (2010) Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: IEEE 3rd international conference on cloud computing, Florida, USA
18.
Xiong P, Wang Z, Malkowski S, Wang Q, Jayasinghe D, Pu C (2011) Economical and robust provisioning of n-tier cloud workloads: a multi-level control approach. In: Distributed computing systems (ICDCS), Minneapolis, Minnesota
19.
Tsakalozos K, Roussopoulos M, Floros V, Delis A (2010) Nefeli: Hint-based execution of workloads in clouds. In: International conference on distributed computing systems, Genova, Italy
20.
Bossche RVD, Vanmechelen K, Broeckhove J (2013) Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Gener Comput Syst 29(4):973–985CrossRef
21.
Kousiouris G, Cucinotta T, Varvarigou T (2011) The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks. J Syst Softw 84(8):1270–1291CrossRef
22.
Mahambre S, Kulkarni P, Bellur U, Chafle G, Deshpande D (2012) Workload characterization for capacity planning and performance management in IaaS cloud. In: IEEE cloud computing in emerging markets (CCEM), Bangalore, India
23.
Pandey S, Wu L, Guru S, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Advanced information networking and applications (AINA), 24th IEEE international conference, Perth, Australia
24.
Topcuoglu H, Hariri S, Wu M-Y (1999) Task scheduling algorithms for heterogeneous processors. In: Heterogeneous computing workshop, (HCW’99), San Juan, Puerto Rico
25.
El-kenawy E-ST, El-Desoky AI, Al-rahamawy MF (2012) Extended max–min scheduling using petri net and load balancing. Int J Soft Comput Eng (IJSCE) 2(4):198–203
26.
Varalakshmi P, Ramaswamy A, Balasubramanian A, Vijaykumar P (2011) An optimal workflow based scheduling and resource allocation in cloud. In: Advances in computing and communications. Springer, Berlin, Heidelberg, pp 411–420
27.
Li, Kun, Gaochao Xu, Guangyu Zhao, Yushuang Dong, and Dan Wang. “Cloud task scheduling based on load balancing ant colony optimization”. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual, pp. 3–9. IEEE, 2011.
28.
Xu M, Cui L, Wang H, Bi Y (2009) A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing. In: IEEE international symposium on parallel and distributed processing with applications, MA, USA
29.
Ambike S, Bhansali D, Kshirsagar J, Bansiwal J (2012) An optimistic differentiated job scheduling system for cloud computing. Int J Eng Res Appl (IJERA) 2(2):1212–1214
30.
Yu J, Buyya R, Tham CK (2005) Cost-based scheduling of scientific workflow applications on utility grids. In: E-Science and grid computing, IEEE, IL, USA
31.
Sakellariou R, Zhao H, Tsiakkouri E, Dikaiakos MD (2007) Scheduling workflows with budget constraints. In: Integrated research in GRID Comput, pp 189–202
32.
Selvarani S, Sadhasivam GS (2010) Improved cost-based algorithm for task scheduling in cloud computing. In: IEEE, computational intelligence and computing research (ICCIC), Tamilnadu, India
33.
Dakshayini M, Guruprasad HS (2011) An optimal model for priority based service scheduling policy for cloud computing environment. Int J Comput Appl 32(9):0975–8887
34.
S. Ghanbari and. M. Othman, “A Priority based Job Scheduling Algorithm in Cloud Computing”, Procedia Engineering, International Conference on Advances Science and Contemporary Engineering, vol. 50, pp. 778–785, 2012.
35.
Wu Z, Liu X, Ni Z, Yuan D, Yang Y (2013) A market-oriented hierarchical scheduling strategy in cloud workflow systems. J Supercomput 63(1):256–293CrossRef
36.
Delavar AG, Javanmard M, Shabestari MB, Talebi MK (2012) RSDC (reliable scheduling distributed in cloud computing). Int J Comput Sci Eng Appl (IJCSEA) 2(3):1–16CrossRef
37.
Moschakis IA, Karatza HD (2012) Evaluation of gang scheduling performance and cost in a cloud computing system. J Supercomput 59(2):975–992CrossRef
38.
Zhan Z-H, Zhang J, Li Y, Chung HS-H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern-Part B: Cybern 39(6):1362–1381CrossRef
39.
Liu K, Jin H, Chen J, Liu X, Yuan D, Yang Y (2010) A compromised-time-cost scheduling algorithm in swindew-c for instance-intensive cost-constrained workflows on a Cloud computing platform. Int J High Perform Comput Appl 24(4):445–456CrossRef
40.
Verma A, Kaushal S (2012) Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In: IJCA Proceedings on international conference on recent advances and future trends in information technology (iRAFIT 2012)
41.
Calheiros RN, Ranjan R, Rose CAFD, Buyya R (2009) CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services. In: Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia
42.
Singh S, Chana I (2014) Energy based efficient resource scheduling: a step towards green computing. Int J Energy Inf Commun 5(2):35–52
43.
Singh S, Chana I (2014) Metrics based workload analysis technique for IaaS cloud. In: Proceedings of international conference on next generation computing and communication technologies, 23rd and 24th April 2014, Dubai
44.
Omer K, Maljevic I, Anthony R, Petridis M, Parrott K, Schulz M (2011) Dynamic scheduling of virtual machines running HPC workloads in scientific grids. In: 3rd international IEEE conference on new technologies, mobility and security (NTMS)
45.
46.
Qureshi MRJ, Qureshi WA (2012) Evaluating requirement specification document to improve the quality of software. AWERProc Inf Technol Comput Sci 1:596–600
47.
Elghany MA, Khalifa N (2012) Quantifying software reliability attribute through the adoption of weighting approach to functional requirements. In: International conference on software and computer applications (ICSCA 2012), Singapore
48.
Saeid M, Ghani AAA, Selamat H (2011) Rank-Order Weighting of Web Attributes for Website Evaluation. The International Arab Journal of Information Technology 8(1):30–38
49.
Dromey RG (1995) A model for software product quality. IEEE Trans Softw Eng 21(2):146–462CrossRef
50.
Malik SU (2012) Customer satisfaction, perceived service quality and mediating role of perceived value. Int J Mark Stud 4(1):68–76MathSciNet
51.
Nallur V, Bahsoon R (2010) Design of a market-based mechanism for quality attribute tradeoff of services in the cloud. In: ACM symposium on applied computing
52.
Stefani A, Xenos M (2008) E-commerce system quality assessment using a model based on ISO 9126 and belief networks. Softw Qual Control 16(1):107–129CrossRef
53.
Davoudi M, Aliee FS (2009) A new AHP-based approach towards enterprise architecture quality attribute analysis. In: Research challenges in information science, RCIS
54.
Garofalakis J, Stefani A, Stefanis V, Xenos M (2008) Quality attributes of consumer-based M-commerce systems, White Paper, University of Patras
55.
Otieno C, Mwangi W, Kimani S (2012) Framework to assess software quality in ERP systems. In: Scientific conference proceedings
56.
Clements P, Kazman R, Klein M (2002) Evaluating software architectures: methods and case studies. Addison-Wesley Longman, Boston, MA, USA
57.
Meiappane A, Venkatesan VP, Roshini N, Nivedha S, Maheswar R (2013) Evaluation of software architecture quality attribute for an internet banking system. Int J Sci Eng Res 4(4):1704–1708
58.
Stefani A, Xenos MN (2009) Meta-metric evaluation of E-commerce-related metrics. Electr Notes Theor Comput Sci (ENTCS) 233:59–72CrossRef
59.
Bhattacharjee PK (2010) Service quality measurement with minimum attributes (SERVQUAL-MA) technique upgrade by human resource development. Int J Innov Manage Technol 1(3):322–327MathSciNet
60.
Barbacci MR (2003) Software Quality Attributes and Architecture Tradeoffs. Carnegie Mellon University, Pittsburgh, Software Engineering Institute
61.
Berander P, Damm L-O, Eriksson J, Gorschek T, Henningssonv, Jönsson P, Kågström S, Milicic D, Mårtensson F, Rönkkö K, Tomaszewski P (2005) Software quality attributes and trade-offs. Blekinge Institute of Technology
62.
Brooke J (1996) SUS-A quick and dirty usability scale. Redhatch Consulting, UK
63.
Singh RV, Bhatia MP (2011) Data clustering with modified K-means algorithm. In: IEEE-international conference on recent trends in information technology, ICRTIT, Chennai
64.
Gupta GK (2006) Introduction to data mining with case studies. PHI Learning Pvt. Ltd., New Delhi
65.
Su M-C, Chou C-H (2001) A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans Pattern Anal Mach Intell 23(6):674–680CrossRef
66.
Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892CrossRef
67.
Mahendiran A, Saravanan N, Subramanian NV, Sairam N (2012) Implementation of K-means clustering in cloud computing environment. Res J Appl Sci Eng Technol 4(10):1391–1394
68.
Barseghyan A, Kupriyanov M, Kholod I, Yelizarov S, Thess M (2014) Analysis of data and processes: from standard to realtime data mining. Re Di Roma-Verlag, Remscheid, Germany
69.
V’azquez C, Huedo E, Montero RS, Llorente IM (2009) Dynamic provision of computing resources from grid infrastructures and cloud providers. In: Workshops at the grid and pervasive computing conference
Metadaten
Titel
QRSF: QoS-aware resource scheduling framework in cloud computing
verfasst von
Sukhpal Singh
Inderveer Chana
Publikationsdatum
01.01.2015
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 1/2015
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-014-1295-6

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