Cited By
View all- Kaur TChana I(2016)Energy aware scheduling of deadline-constrained tasks in cloud computingCluster Computing10.1007/s10586-016-0566-919:2(679-698)Online publication date: 1-Jun-2016
Resource Scheduling Techniques in Utility Computing: A Survey
Pages 44 - 65
Abstract
Utility Computing offers on-demand services from a shared pool of resources and can be envisaged to be a benchmark in the IT development. The capability to provide on-demand services involves management of large number of resources that are geographically dispersed and thus poses a number of resource management and scheduling challenges in the domain of resource heterogeneity, dynamic resource locations and load balancing. Proficient resource allocations and efficient scheduling helps in achieving optimal resource utilization and hence enhances the performance of the system. This paper evaluates existing resource management systems, listing their key characteristic features and highlighting the factors that make the existing systems excel upon each other. It also discusses various resource scheduling techniques currently available and characterizes the techniques based on Quality of Service (QoS) parameters supported by them along with the classification on basis of their operating environment and further extends towards load balancing and energy efficiency support if available.
References
[1]
About IBM. (n.d.). Retrieved August, 2013, from http://www-935.ibm.com/services/us/en/it-services/applications-on-demand2.html
[2]
Abramson, D., Buyya, R., & Giddy, J. (2002, October). A computational economy for Grid computing and its implementation in the Nimrod-G resource broker. Future Generation Computer Systems, 18(8), 1061—1074.
[3]
Agarwal, V., Dasgupta, G., Dasgupta, K., Purohit, A., & Viswanathan, B. (2006). DECO: Data replication and execution CO-scheduling for utility grids. In Proceedings of Service-Oriented Computing—ICSOC (pp. 52-65). Springer Berlin Heidelberg.
[4]
Amir, Y., Awerbuch, B., Barak, A., Borgstrom, R. S., & Keren, A. (2000). An opportunity cost approach for job assignment in a scalable computing cluster. IEEE Transactions on Parallel and Distributed Systems, 11(7), 760—768.
[5]
Aron, R., & Chana, I. (2012). Formal QoS policy based grid resource provisioning framework. Journal of Grid Computing, 10(2), 1——16.
[6]
Asaduzzaman, S., & Maheswaran, M. (2006). Utilizing unreliable public resources for higher profit and better SLA compliance in computing utilities. Journal of Parallel and Distributed Computing, 66(6), 796—806.
[7]
Broberg, J., Venugopal, S., & Buyya, R. (2008). Market-oriented Grids and Utility computing: The state-of-the-art and future directions. Journal of Grid Computing, 6(3), 255—276.
[8]
Buyya, R. (2002). Economic-based distributed resource management and scheduling for grid computing. PhD Thesis, Monash University, Melbourne, Australia. Retrieved from http://arxiv.org/ftp/cs/papers/0204/0204048.pdf
[9]
Buyya, R., & Abramson, D. (2009). The Nimrod/G grid resource broker for economic-based scheduling. Market-Oriented Grid and Utility Computing, 75, 371.
[10]
Buyya, R., & Bubendorfer, K. (2009). The nimrod-G grid resource broker and economic scheduling algorithms. Hoboken, NJ: John Wiley & Sons, Inc.
[11]
Buyya, R., & Murshed, M. (2002). A deadline and budget constrained cost-time optimisation algorithm for scheduling task farming applications on global grids. Retrieved from http://www.cloudbus.org/papers/cto-pdpta02.pdf
[12]
Buyya, R., Murshed, M., Abramson, D., & Venugopal, S. (2005). Scheduling parameter sweep applications on global Grids: A deadline and budget constrained cost——time optimization algorithm. Software, Practice & Experience, 35(5), 491——512.
[13]
BuyyaR.SulistioA. (2008, April). Service and utility oriented distributed computing systems: challenges and opportunities for modeling and simulation communities. In Proceedings of the 41st Annual Simulation Symposium (ANSS 2008), IEEE (pp.68-81). 10.1109/ANSS-41.2008.35
[14]
BuyyaR.VenugopalS. (2004). The Gridbus toolkit for service oriented Grid and utility computing: An overview and status report. In Proceedings of the 1st IEEE International Workshop on Grid Economics and Business Models (GENCON 2004) (pp. 19-66). 10.1109/GECON.2004.1317583
[15]
Buyya, R., Venugopal, S., Ranjan, R., & Yeo, C. S. (2010). The Gridbus middleware for market-oriented computing. Market-oriented grid and utility computing (pp. 589—622). John Wiley & Sons, Inc.
[16]
Chun, B. N., & Culler, D. E. (2000). Market-based proportional resource sharing for cluster. Berkeley, CA: Computer Science Division, University of California.
[17]
ChunlinL.LayuanL. (2007). Utility based multiple QoS guaranteed resource scheduling optimization in grid computing. In Proceedings of the International Conference on Computing: Theory and Applications (ICCTA'07) IEEE (pp. 165-169). 10.1109/ICCTA.2007.131
[18]
DineshV. (2004). Supporting service level agreements on IP networks. In Proceedings of IEEE/IFIP Network Operations and Management Symposium, 92(9), 1382-1388.
[19]
DongF.LuoJ. (2007). QoS matching offset oriented resource clustering scheduling algorithm in grid environment. In Proceedings of the 11th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2007) (pp. 611-616). 10.1109/CSCWD.2007.4281506
[20]
DubeN.ParizeauM. (2008). Utility computing and market-based scheduling: Shortcomings for Grid resources sharing and the next steps. In Proceedings of the 22nd International Symposium on High Performance Computing Systems and Applications (HPCS 2008), Quebec, Canada (pp. 59-68). 10.1109/HPCS.2008.29
[21]
Entezari-Maleki, R., & Movaghar, A. (2012). A probabilistic task scheduling method for Grid environments. Future Generation Computer Systems, 28(3), 513—524.
[22]
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2011). Environment- conscious scheduling of HPC applications on distributed Cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), 732—749.
[23]
Haque, A., Alhashmi, S. M., & Parthiban, R. (2011). A survey of economic models in Grid computing. Future Generation Computer Systems, 27(8), 1056——1069.
[24]
Hu, J., Gu, J., Sun, G., & Zhao, T. (2010, December). A scheduling strategy on load balancing of virtual machine resources in Cloud computing environment. In Proceedings of the Third International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (pp. 89-96).
[25]
IrwinD. E.GritL. E.ChaseJ. S. (2004). Balancing risk and reward in a market-based task service. In Proceedings of 13th IEEE International Symposium on High performance Distributed Computing (pp. 160-169). 10.1109/HPDC.2004.1323519
[26]
Islam, M., Balaji, P., Sadayappan, P., & Panda, D. K. (2003). QoPS: A QoS based scheme for parallel job scheduling. Job Scheduling Strategies for Parallel Processing (pp. 252—268). Springer Berlin Heidelberg.
[27]
IslamM.BalajiP.SadayappanP.PandaD. K. (2004). Towards provision of quality of service guarantees in job scheduling. In Proceedings of the IEEE International Conference on Cluster Computing (pp. 245-254). 10.1109/CLUSTR.2004.1392622
[28]
Jang, S. H., Kim, T. Y., Kim, J. K., & Lee, J. S. (2012). The study of genetic algorithm-based task scheduling for cloud computing. International Journal of Control and Automation, 5(4).
[29]
LeeY. C.WangC.ZomayaA. Y.ZhouB. B. (2010). Profit-driven service request scheduling in clouds. In Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 15-24).
[30]
Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in Cloud computing systems. The Journal of Supercomputing, 60(2), 268—280.
[31]
LiY.ZhaoD.LiJ. (2007). Scheduling algorithm based on integrated utility of multiple QoS attributes on service grid. In Proceedings of the Sixth International Conference on Grid and Cooperative Computing (GCC 2007) (pp. 288-295). 10.1109/GCC.2007.111
[32]
Lin, C. C., & Shih, C. W. (2008). An efficient scheduling algorithm for Grid computing with periodical resource reallocation. In proceedings of the 8th IEEE International Conference on Computer and Information Technology (CIT 2008) (pp. 295-300).
[33]
LiuL.WangY.YangY.TianZ. (2005). Utility-based computing model for grid. In Proceedings of the First International Conference on Semantics, Knowledge and Grid (SKG'05) (pp. 109-109).
[34]
LoadLeveler for AIX 5L and Linux: Using and Administering Version 3 Release 3.1. (n.d.). Available at http://www.serc.iisc.ernet.in/facilities/ComputingFacilities/systems/cluster/loadleveler-3.3.1.0/am2ug303.pdf
[35]
Lorpunmanee, S., Sap, M. P., Abdullah, A. H., & Chompoo-inwai, C. (2007). An ant colony optimization for dynamic job scheduling in grid environment. International Journal of Computer and Information Engineering, 1(4), 207—214.
[36]
Luther, A., Buyya, R., Ranjan, R., & Venugopal, S. (2004). Alchemi: A. net-based grid computing framework and its integration into global grids. Retrieved from http://arxiv.org/ftp/cs/papers/0402/0402017.pdf
[37]
Maui Scheduler Version 3.2 Administrator's guide, supercluster research and development group. (n.d.). Retrieved from http://www.supercluster.org/mauidocs/mauiadmin.shtml
[38]
MilliganC. A. (2006). The data warehouse in a distributed utility environment. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), (Vol. 8, no 8, pp. 170). 10.1109/HICSS.2006.457
[39]
Moore, J., Irwin, D., Grit, L., Sprenkle, S., & Chase, J. (2002). Managing mixed-use clusters with Cluster-on-Demand (Technical Report). Duke University, Department of Computer Science.
[40]
NettoM. A.BuyyaR. (2007). Impact of adaptive resource allocation requests in utility cluster computing environments. In Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGRID 2007), Brazil (pp. 214-224), 10.1109/CCGRID.2007.61
[41]
Philip, T. (2004). Utility computing—identifying the applicability domain and its boundaries. Master's Thesis in Computer Science and Business Administration, University of Zurich, Switzerland. Submitted on 30.12.2004. Retrieved from http://libra.msra.cn/Publication/12753836/utility-computing-identifying-the-applicability-domain-and-its-boundaries
[42]
RamakrishnanA.SinghG.ZhaoH.DeelmanE.SakellariouR.VahiK. (2007). Scheduling data-intensive workflows onto storage-constrained distributed resources. In Proceedings of the International Symposium on Cluster Computing and the Grid, Rio, Brazil. 10.1109/CCGRID.2007.101
[43]
Regev, O., & Nisan, N. (2000). The POPCORN market. Online markets for computational resources. Decision Support Systems, 28(1), 177——189.
[44]
Resource. (n.d.). Retrieved from http://en.wikipedia.org/wiki/Resource
[45]
RoderoI.JaramilloJ.QuirozA.ParasharM.GuimF.PooleS. (2010). Energy-efficient application-aware online provisioning for virtualized Clouds and data centers. In Proceedings of the International Green Computing Conference Chicago, IL (pp. 31-45). 10.1109/GREENCOMP.2010.5598283
[46]
Rolia, J., Andrzejak, A., & Arlitt, M. (2003). Automating enterprise application placement in resource utilities. Self-Managing Distributed Systems., 2867, 118-129. Springer Berlin Heidelberg.
[47]
SharmaR.SoniV. K.MishraM. K. (2010). A new resource scheduling model with bandwidth aware job grouping strategy in grid computing. In Proceedings of the 3rd International Conference on Computer Science and Information Technology (ICCSIT).
[48]
Sherwani, J., Ali, N., Lotia, N., Hayat, Z., & Buyya, R. (2004). Libra: A computational economy-based job scheduling system for clusters. Software, Practice & Experience, 34(6), 573——590.
[49]
SinghalS.ArlittM.BeyerD.GraupnerS.MachirajuV.PruyneJ.ZhuX. (2005). Quartermaster-a resource utility system. In Proceedings of the 9th IFIP/IEEE International Symposium on Integrated Network Management (IM 2005), Nice, France (pp. 265-278).
[50]
SoniV. K.SharmaR.MishraM. K.DasS. (2010). Constraint-based job and resource scheduling in grid computing. In Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT) (Vol. 4, pp. 334-337).
[51]
Thain, D., Tannenbaum, T., & Livny, M. (2005). Distributed computing in practice: The Condor experience. Concurrency and Computation, 17(2——4), 323—356.
[52]
Vanderster, D. C., Dimopoulos, N. J., Parra-Hernandez, R., & Sobie, R. J. (2009). Resource allocation on computational grids using a utility model and the knapsack problem. Future Generation Computer Systems, 25(1), 35—50.
[53]
Vengerov, D., Mastroleon, L., Murphy, D., & Bambos, N. (2010). Adaptive data-aware utility-based scheduling in resource-constrained systems. Journal of Parallel and Distributed Computing, 70(9), 871—879.
[54]
WangX.WangY. (2011). Energy-efficient multi task scheduling based on MapReduce for cloud computing. In Proceedings of the Seventh International Conference on Computational Intelligence and Security (CIS). IEEE. 10.1109/CIS.2011.21
[55]
WeiyiQ.JunweiW.MingluL.ChuliangW. (2008). A resource scheduling model supporting differentiated service for grid computing. ChinaGrid. In Proceedings of the Third Annual Conference (ChinaGrid'08) (pp. 47-54). IEEE. 10.1109/ChinaGrid.2008.17
[56]
Wu, L., & Buyya, R. (2010). Service level agreement (SLA) in utility computing systems. Retrieved from http://arxiv.org/ftp/arxiv/papers/1010/1010.2881.pdf
[57]
YangB.XuX.TanF.ParkD. H. (2011). An utility-based job scheduling algorithm for cloud computing considering reliability factor. In Proceedings of the International Conference on Cloud and Service Computing. 10.1109/CSC.2011.6138559
[58]
YeoC. S.BuyyaR. (2005). Service level agreement based allocation of cluster resources: Handling penalty to enhance utility. In Proceedings of the IEEE International Conference in Cluster Computing (pp. 1-10), 10.1109/CLUSTR.2005.347075
[59]
Yeo, C. S., & Buyya, R. (2006). A taxonomy of market-based resource management systems for utility-driven cluster computing. Software, Practice & Experience, 36(13), 1381——1419.
[60]
Yeo, C. S., & Buyya, R. (2007). Pricing for utility-driven resource management and allocation in clusters. International Journal of High Performance Computing Applications, 21(4), 405—418.
[61]
Yeo, C. S., de Assunção, M. D., Yu, J., Sulistio, A., Venugopal, S., Placek, M., & Buyya, R. (2006). Utility computing on global grids. Retrieved from http://arxiv.org/ftp/cs/papers/0605/0605056.pdf
- Resource Scheduling Techniques in Utility Computing: A Survey
Recommendations
Stochastic resource allocation
VEE 2019: Proceedings of the 15th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution EnvironmentsSuboptimal resource utilization among public and private cloud providers prevents them from maximizing their economic potential. Long-term allocated resources are often idle when they might have been subleased for a short period. Alternatively, ...
Position paper: resource management for complex distributed systems
WORDS '96: Proceedings of the 2nd Workshop on Object-Oriented Real-Time Dependable Systems (WORDS '96)Complex distributed systems require resource management capabilities that can allocate resources to applications, monitor and control the use of resources, and reallocate resources in response to anomalies. To utilize resources efficiently and meet ...
Resource provisioning and scheduling in clouds: QoS perspective
Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
IGI Global
United States
Publication History
Published: 01 April 2014
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
Cited By
View all- Kaur TChana I(2016)Energy aware scheduling of deadline-constrained tasks in cloud computingCluster Computing10.1007/s10586-016-0566-919:2(679-698)Online publication date: 1-Jun-2016