Complementary oriented allocation algorithm for cloud computing

• Autorzy: Orzechowski P.

Identyfikatory

DOI

10.17466/tq2017/21.4/u

• Języki publikacji: EN

Abstrakty

EN

Nowadays cloud computing is one of the most popular processing models. More and more different kinds of workloads have been migrated to clouds. This trend obliges the community to design algorithms which could optimize the usage of cloud resources and be more efficient and effective. The paper proposes a new model of workload allocation which bases on the complementarity relation and analyzes it. An example of a case of use is shown and an increase in the workload execution is presented.

Słowa kluczowe

EN

cloud computing; complementary workloads; workloads allocation; IaaS cloud; resource provisioning

PL

przetwarzanie w chmurze; komplementarność obciążeń; alokacja obciążeń; chmura IaaS; przydzielanie zasobów

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2017
• Tom: Vol. 21, No 4
• Strony: 395--403
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Orzechowski P.

• Academic Computer Centre in Gdansk ( CI TASK ), Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland

Bibliografia

• [1] Zhang Q, Cheng L and Boutaba R 2010 Journal of Internet Services and Applications 1 (1) 7 doi: 10.1007/s13174-010-0007-6
• [2] Mustafa S, Nazir B, Hayat A, Khan A u R and Madani S A 2015 Computers and Electrical Engineering 47 186
• [3] Zhan Z-H, Liu X-F, Gong Y-J, Zhang J, Chung H S-H and Li Y 2015 ACM Comput. Surv.,ACM,47(4) 63:1 doi: 10.1145/2788397
• [4] Madni S H H, Latiff M S A, Coulibaly Y and Abdulhamid S M 2016 Journal of Network and Computer Applications 68 (Supplement C) 173 doi: 10.1016/j.jnca.2016.04.016
• [5] Zhao Ch, Zhang S, Liu Q, Xie J and Hu J 2009 Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing, Procs Wi COM ’09, IEEE Press 5548
• [6] Verma A and Kumar P 2012 International Journal of Advanced Research in Computer Science and Software Engineering 2 111
• [7] Chimakurthi L and Kumar S D M 2011 CoRR [Online] available at: http://arxiv.org/abs/1102.2608 [Accessed: 7-Jun-2017]
• [8] Gao Y, Guan H, Qi Z, Hou Y and Liu L 2013 J. Comput. Syst. Sci., Academic Press, Inc., 79 (8) 1230 doi: 10.1016/j.jcss.2013.02.004
• [9] Chen W N and Zhang J 2012 A set-based discrete PSO for cloud workflow scheduling with user-defined QoS constraints, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 773 doi: 10.1109/ICSMC.2012.6377821
• [10] Li H-H, Chen Z-G, Zhan Z-H, Du K-J and Zhang J 2015 Renumber Coevolutionary Multiswarm Particle Swarm Optimization for Multi-objective Workflow Scheduling on Cloud Computing Environment, Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM 1419 doi: 10.1145/2739482.2764632
• [11] OpenStack 2017 [Online] available at: https://www.openstack.org/ [Accessed: 7-Nov-2017]
• [12] Krawczyk H, Proficz J and Daca B 2013 Prediction of Processor Utilization for Real-Time Multimedia Stream Processing Tasks, Proceedings of Distributed Computing and Internet Technology Hota Ch, Srimani P K (ed.), Springer 278 doi: 10.1007/978-3-642-36071-8 22
• [13] Orzechowski P, Proficz J, Krawczyk H and Szymanski J 2016 Categorization of Cloud Workload Types with Clustering, Proceedings of the International Conference on Signal, Networks, Computing, and Systems, Springer 303

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).