Under a Creative Commons license Open access AbstractCloud
computing is an attractive computing model since it allows for the provision of resources on-demand. Cloud computing has emerged as a new technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. Companies are able to rent resources from cloud for storage and other computational purposes so that their infrastructure cost can be reduced significantly. Hence there is no need for getting licenses for
individual products. Cloud Computing offers an interesting solution for software development and access of content with transparency of the underlying infrastructure locality. The Cloud infrastructure is usually composed of several data centers and consumers have access to only a slice of the computational power over a scalable network. The provision of these computational resources is controlled by a provider, and resources are allocated in an elastic way, according to consumers’ needs. However
one of the major pitfalls in cloud computing is related to optimizing the resources being allocated. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper, modified round robin resource allocation algorithm is proposed to satisfy customer demands by reducing the waiting time. KeywordsCloud Cloud Computing Cloud
Users Cloud Services Resource Allocation Customer Demand Infrastructure Resource Allocation Algorithm © 2016 The Author(s). Published by Elsevier B.V. ReferencesAssunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R (2015) Big data computing and clouds: trends and future directions. J Parall Distrib Comput 79:3–15 Google
Scholar On line. Cloud computing statistics 2019. https://techjury.net/stats-about/cloud-computing/. Accessed on 12 July 2019 Buyya R, Yeo CS, Venugopal S (2008). Market-oriented cloud computing: vision, hype, and reality for delivering it services as computing utilities. IEEE, pp 5–13 -
Belgacem A, Beghdad-Bey K, Nacer H (2018) Task scheduling optimization in cloud based on electromagnetism metaheuristic algorithm. In: 2018 3rd International conference on pattern analysis and intelligent systems (PAIS). IEEE, pp 1–7 Alkhanak EN, Lee SP, Rezaei R, Parizi RM (2016) Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J Syst Softw
113:1–26
Google Scholar Challita S, Paraiso F, Merle P (2017) A study of virtual machine placement optimization in data centers. April Porto, Portugal Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egy Inform J 16(3):275–295 Google Scholar Madni
SHH, Latiff MSA, Coulibaly Y et al (2016) Resource scheduling for infrastructure as a service (IAAS) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200
Google Scholar Zhan ZH, Liu XF, Gong YJ, Zhang J, Chung HSH, Li Y (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surv (CSUR) 47(4):63
Google Scholar Masdari M, ValiKardan S, Shahi Z, Azar SI (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82 Google Scholar
Salot P (2013) A survey of various scheduling algorithm in cloud computing environment. Int J Res Eng Technol 2(2):131–135 Google Scholar Alkhanak
EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Fut Gen Comput Syst 50:3–21
Google Scholar Haji LM, Zeebaree SR, Ahmed OM, Sallow AB, Jacksi K, Zeabri RR (2020) Dynamic resource allocation for distributed systems and cloud computing. TEST Eng Manag 83:22417–22426
Google Scholar Dieste O, Grimán A, Juristo N (2009) Developing search strategies for detecting relevant experiments. Empir Softw Eng 14(5):513–539 Google Scholar -
Kino T (2011) Infrastructure technology for cloud services. Fujitsu Sci Tech J 47(4):434–442
Google Scholar Rochwerger B, Breitgand D, Levy E, Galis A, Nagin K, Llorente IM, Montero R, Wolfsthal Y, Elmroth E, Caceres J et al (2009) The reservoir model and architecture for open federated cloud computing. IBM J Res Develop 53(4):4–1
Google Scholar Peng J, Zhang X, Lei Z, Zhang B, Zhang W, Li Q (2009) Comparison of several cloud computing platforms. In: Proceedings of the 2009 second international symposium on information science and engineering, pp. 23–27. IEEE Online. Gestion des ressources vsphere. http://www.vmware.com/fr/support/pubs. Accessed on
16 June 2020 Li J, Qiu M, Ming Z, Quan G, Qin X, Zonghua G (2012) Online optimization for scheduling preemptable tasks on IAAS cloud systems. J Parall Distrib Comput 72(5):666–677
Google Scholar Jin Y, Branke J et al (2005) Evolutionary optimization in uncertain environments: a survey. IEEE Trans Evol Comput 9(3):303–317 Google Scholar Talbi
E-G (2009) Metaheuristics: from design to implementation, vol 74. Wiley, Hoboken MATH
Google Scholar Branke J (2012) Evolutionary optimization in dynamic environments, vol 3. Springer, New York MATH
Google Scholar Mell P, Grance T, et al (2011) The nist definition of cloud computing Ali B, Kadda BB, Hassina N (2018) Task scheduling in cloud computing environment: a comprehensive analysis. In: International conference on computer science and its applications, pp. 14–26, 24–25 April, in Algiers, Algeria. Springer, New York Zhang L, Zhou L, Salah A (2020) Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments. Inf Sci 531:31–46 MathSciNet MATH
Google Scholar
Yuan H, Bi J, Zhou MC (2019) Profit-sensitive spatial scheduling of multi-application tasks in distributed green clouds. IEEE Trans Autom Sci Eng Swain CK, Saini N, Sahu A (2020) Reliability aware scheduling of bag of real time tasks in cloud environment. Computing 102(2):451–475 MathSciNet MATH
Google Scholar Alworafi MA, Mallappa S (2020) A collaboration of deadline and budget constraints for task scheduling in cloud computing. Clust Comput 23(2):1073–1083 Google Scholar Chen Z, Junqin H, Chen X, Jia H, Zheng X, Min G (2020) Computation offloading and task scheduling for dnn-based applications in cloud-edge computing. IEEE Access 8:115537–115547
Google Scholar Rashida SY, Sabaei M, Ebadzadeh MM, Rahmani AM (2019) A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment. Cluster Comput 1–40 More NS, Ingle RB (2020) Optimizing the topology and energy-aware vm migration in cloud computing. Int J Ambient Comput Intell (IJACI) 11(3):42–65 Google Scholar Gholipour N, Arianyan E, Buyya R (2020) A novel energy-aware resource management technique using joint vm and container consolidation approach for green computing in cloud data centers. Simul Model Pract Theory, pp. 102127 Mandal R, Mondal MK, Banerjee S, Biswas U (2020) An approach toward design and development of an energy-aware vm selection policy with improved sla violation in the domain of green cloud
computing. J Supercomput 1–20 Singh BP, Ananda KS, Gao XZ, Kohli M, Katiyar S (2020) A study on energy consumption of dvfs and simple vm consolidation policies in cloud computing data centers using cloudsim toolkit. Wireless Pers Commun 1–13 Kholidy HA (2020) An intelligent swarm based prediction approach for predicting cloud computing user resource needs. Comput Commun 151:133–144 Google Scholar Chen X, Wang H, Ma Y, Zheng X, Guo L (2020) Self-adaptive resource allocation for cloud-based software services based on iterative qos prediction model. Futur Gener Comput Syst 105:287–296
Google Scholar Qiu C, Shen H (2019) Dynamic demand prediction and allocation in cloud service brokerage. IEEE Trans Cloud Comput Chen J, Wang Y (2019) A hybrid method for short-term host utilization prediction in cloud computing. J Elect Comput Eng 2019 Hai Y (2014) Improved ant colony algorithm based on pso and its application on cloud
computing resource scheduling. In: Advanced materials research vol 989, pp. 2192–2195. Trans Tech Publ Chaima G, Makhlouf H, Djamal Z (2013) Energy efficient vm scheduling for cloud data centers: exact allocation and migration algorithms. In: Proceedings of the 13th IEEE/ACM international symposium on Cluster, cloud and grid computing (CCGrid), 2013, pp. 671–678, Delft, Netherlands, 13–16 May 2013. IEEE Suraj P, Linlin W, Siddeswara MG, Rajkumar B (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Proceedings of the 24th IEEE international conference on advanced information networking and applications (AINA), 2010, pp. 400–407, Perth, Western Australia, 20–23 April 2010. IEEE Zhangjun W, Zhiwei N, Lichuan G, Xiao L (2010) A revised discrete particle swarm
optimization for cloud workflow scheduling. In: International conference on computational intelligence and security (CIS), 2010, pp. 184–188, Nanning, Guangxi, China, 11–14 December 2010. IEEE Ritu K (2015) A cost effective approach for resource scheduling in cloud computing. In: International conference on computer, communication and control (IC4), 2015, pp. 1–6, Medi-Caps Group of Institutions A.B. Road Pigdamber Rau, Indore Indore, India, 10
Sep–12 Sep 2015. IEEE Mohammed Abdullahi Md, Ngadi A et al (2016) Symbiotic organism search optimization based task scheduling in cloud computing environment. Futur Gener Comput Syst 56:640–650 Google Scholar
Chen WN, Zhang J (2012) A set-based discrete pso for cloud workflow scheduling with user-defined qos constraints. In: IEEE international conference on systems, man, and cybernetics (SMC), 2012, pp. 773–778, COEX Seoul, Korea (South), 14 Oct–17 Oct 2012. IEEE Belgacem A, Kadda BB, Hassina N (2020) Dynamic resource allocation method based on symbiotic organism search algorithm in cloud computing. IEEE Trans
Cloud Comput Calheiros RN, Buyya R (2014) Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans Parallel Distrib Syst 25(7):1787–1796 Google Scholar
Zuo L, Shu L, Dong S, Zhu C, Hara T (2015) A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3:2687–2699
Google Scholar Belgacem A, Beghdad-Bey K (2021) Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost. Cluster Comput 1–17 Octavio Gutierrez-Garcia J, Sim KM (2013) A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Futur Gener Comput Syst 29(7):1682–1699 Google
Scholar Oprescu AM, Kielmann T (2010) Bag-of-tasks scheduling under budget constraints. In: Proceedings of the 2010 IEEE second international conference on cloud computing technology and science, pp 351–359. IEEE Zhang F, Cao J, Tan W, Khan SU, Li K, Zomaya AY (2014) Evolutionary scheduling of dynamic multitasking workloads for big-data analytics in elastic cloud. IEEE Trans Emerg Top
Comput 2(3):338–351
Google Scholar Bey KB, Benhammadi F, El Yazid Boudaren M, Khamadja S (2017) Load balancing heuristic for tasks scheduling in cloud environment. In: Proceedings of the 19th international conference on enterprise information systems Vol 1: ICEIS, pp. 489–495, April 26–29, in Porto, Portugal, 2017. INSTICC, SciTePress Nan X, He Y, Guan L (2013) Optimization of workload scheduling for
multimedia cloud computing. In: Proceedings of the 2013 IEEE international symposium on circuits and systems (ISCAS), pp. 2872–2875. IEEE Gupta A, Garg R (2017) Load balancing based task scheduling with aco in cloud computing. In: Proceedings of the 2017 international conference on computer and applications (ICCA), pp. 174–179, 6–7 Sept, Doha, United Arab Emirates, 2017. IEEE Li K, Gaochao X, Zhao G,
Dong Y, Wang D (2011) Cloud task scheduling based on load balancing ant colony optimization. In: Chinagrid Conference (ChinaGrid), 2011 Sixth Annual, pp. 3–9, August, Dalian, Liaoning, China, 2011. IEEE Kumar D, Raza Z (2015) A pso based vm resource scheduling model for cloud computing. In: Proceedings of the 2015 IEEE international conference on computational intelligence and communication technology (CICT), pp. 213–219, October Liverpool, UK,
2015. IEEE Tsai C-W, Huang W-C, Chiang M-H, Chiang M-C, Yang C-S (2014) A hyper-heuristic scheduling algorithm for cloud. IEEE Trans Cloud Comput 2(2):236–250 Google Scholar
Sandhu R, Sood SK (2015) Scheduling of big data applications on distributed cloud based on qos parameters. Clust Comput 18(2):817–828 Google Scholar Zhao H, Wang J, Wang Q, Liu F (2019) Queue-based and learning-based dynamic resources allocation for virtual streaming media server cluster of multi-version vod system. Multimedia Tools Appl 78(15):21827–21852
Google Scholar Zhang J, Xie N, Zhang X, Yue K, Li W, Kumar D (2018) Machine learning based resource allocation of cloud computing in auction. Comput Mater Continua 56(1):123–135
Google Scholar Thein T, Myo MM, Parvin S, Gawanmeh A (2020) Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers. J King Saud Univ Comput Inform Sci 32(10):1127–1139
Google Scholar Vadivel R, SudalaiMuthu TP (2020) An effective hpso-mga optimization algorithm for dynamic resource allocation in cloud environment. Clust Comput 23(3):1711–1724 Google Scholar
Chen Z, Yang L, Huang Y, Chen X, Zheng X, Rong C (2020) Pso-ga-based resource allocation strategy for cloud-based software services with workload-time windows. IEEE Access 8:151500–151510
Google Scholar Gao X, Liu R, Kaushik A (2020) Hierarchical multi-agent optimization for resource allocation in cloud computing. IEEE Trans Parallel Distrib Syst 32(3):692–707 Google Scholar
Bajo J, De la Prieta F, Corchado JM, Rodríguez S (2016) A low-level resource allocation in an agent-based cloud computing platform. Appl Soft Comput 48:716–728 Google Scholar
Achar R, Thilagam PS, Shwetha D, Pooja H, et al (2012) Optimal scheduling of computational task in cloud using virtual machine tree. In: Third international conference on emerging applications of information technology (EAIT), 2012, pp. 143–146, 30 Nov–01 Dec, Kolkata, India, 2012. IEEE Gao ZW, Zhang K (2012) The research on cloud computing resource scheduling method based on time-cost-trust model.
In: Proceedings of the 2012 2nd international conference on computer science and network technology (ICCSNT), pp. 939–942, Dec Changchun, China, 2012. IEEE Bessai K, Youcef S, Oulamara A, Godart C, Nurcan S. Bi-criteria work ow tasks allocation and scheduling in cloud computing environments. In: Proceedings of the 2012 IEEE 5th international conference on cloud computing (CLOUD), pp. 638–645, Nov, Chicago, IL, USA, 2012. IEEE Arash GD, Yalda A (2014) Hsga: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust Comput 17(1):129–137 Google Scholar Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21
Google Scholar Portaluri G, Giordano S (2016) Multi objective virtual machine allocation in cloud data centers. In: Proceedings of the 2016 5th IEEE international conference on cloud networking (Cloudnet), pp 107–112. IEEE Yousri M, Foued J, Jie T, Jiaqi Z, Joanna K, Achim S (2013) Load and thermal-aware vm scheduling on the cloud. In: International conference on algorithms and
architectures for parallel processing, pp 101–114, October Liverpool, UK, 2013. Springer Wang W, Zeng G, Tang D, Yao J (2012) Cloud-dls: Dynamic trusted scheduling for cloud computing. Exp Syst Appl 39(3):2321–2329 Google Scholar -
Gawali MB, Shinde SK (2018) Task scheduling and resource allocation in cloud computing using a heuristic approach. J Cloud Comput 7(1):4 Google Scholar Guo-ning G, Ting-lei H, Shuai G (2010) Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: Proceedings of the 2010 international conference on intelligent computing and integrated systems, pp. 60–63, 22–24 October, Guilin, China, 2010. IEEE Peng Y, Kang D-K, Al-Hazemi F, Youn C-H (2017) Energy and qos aware resource allocation for heterogeneous sustainable cloud datacenters. Opt Switch
Netw 23:225–240 Google
Scholar Meng X, Lizhen C, Haiyang W, Yanbing B (2009) A multiple qos constrained scheduling strategy of multiple workflows for cloud computing. In: Proceedings of the 2009 IEEE international symposium on parallel and distributed processing with applications, pp. 629–634, 10–12 Aug., in Chengdu, China, 2009. IEEE Joseph CT, Chandrasekaran K (2020) Intma: dynamic interaction-aware resource
allocation for containerized microservices in cloud environments. J Syst Arch 111:101785 Google Scholar Pradeep SR, Priti D, Soumen K, Gyanendra PS (2020) Optimize task allocation in cloud environment based on big-bang big-crunch. Wireless Pers Commun 115(2):1711–1754 Google
Scholar Chang Z, Liu L, Guo X, Sheng Q (2020) Dynamic resource allocation and computation offloading for iot fog computing system. IEEE Trans Ind Inform Naha RK, Garg S, Chan A, Battula SK (2020) Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Futur Gener Comput Syst 104:131–141 Google
Scholar Zhang P, Zhou MC, Wang X (2020) An intelligent optimization method for optimal virtual machine allocation in cloud data centers. IEEE Trans Autom Sci Eng 17(4):1725–1735 Google
Scholar Belgacem A, Beghdad-Bey K, Nacer H (2018) Enhancing cost performance using symbiotic organism search based algorithm in cloud. In: Proceedings of the 2018 international conference on smart communications in network technologies (SaCoNeT), pp. 306–311. IEEE Gong S, Yin B, Zheng Z, Cai K-Y (2019) Adaptive multivariable control for multiple resource allocation of service-based systems
in cloud computing. IEEE Access 7:13817–13831 Google
Scholar Feng L, Zhou F, Peng Yu, Li W (2018) Benders decomposition-based video bandwidth allocation in mobile media cloud network. Multimedia Tools Appl 77(1):877–895 Google
Scholar Narman HS, Hossain MS, Atiquzzaman M, Shen H (2017) Scheduling internet of things applications in cloud computing. Ann Telecommun 72(1–2):79–93 Google Scholar On line. The state of the cloud 2019. https://www.brightred.com/wp-content/uploads/2019/02/The-State-of-Cloud-22022019.pdf. Accessed on 23 July 2019 Tan CB, Hijazi MHA, Lim Y, Gani A (2018) A survey on proof of retrievability for cloud data integrity and availability: Cloud storage
state-of-the-art, issues, solutions and future trends. J Netw Comput Appl 110:75–86
Google Scholar
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How are resources allocated?
Resource allocation is the process of assigning and managing assets in a manner that supports an organization's strategic planning goals. Resource allocation includes managing tangible assets such as hardware to make the best use of softer assets such as human capital.
When a cloud app allocate and release resources?
The user now utilizes the services of assigned resources to perform any specific task or application. When no more service is required then the user releases the resource, pay for the resource & closes the connection. The provider now schedule and allocate the resource to other requesting clients. [5, 6].
What are the types of allocation of resources?
There are mainly two types of resource allocation that are focused on: continuous and and one-time.
How resources are shared in cloud computing?
Resource sharing enables cloud providers and customers to reduce their capital expenditure, operational expenditure, and total cost of ownership. Multiple cloud service providers connect to form a federation to share their resources among themselves to meet the dynamic demand of their customers.
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