Abstract major critical issue in IaaS of cloud

Abstract – With the failure of grid computing cloud computing emerged not only in terms of large upfront investment but also in the area of effective resource allocation. Balancing this cloud computing cloud providers have very large number of computing resources which they make them available on pay per use basis to the cloud users with high resource utilization and maximum profit. Cloud users want to run their application having varieties of resource consumption with lower expenses because of this an imbalance is generated between the two i.e. the provider and the receiver. Resource management in terms of effective allocation becomes most critical issue of cloud computing. In this survey we investigated in AI and strategic based algorithms which will make this work very efficient and definitely balancing the two crucial players of cloud computing.  Keywords— Artificial Intelligence, Strategic Based Resource Allocation, Cloud Computing, Grid Computing.  I.  INTRODUCTION  There are many similarities between cloud computing and grid computing as most of the problem faced are same. According to Foster et al. 1 both have common needs for making a balance between methods by which consumer consumption of resource provided and to implement highly parallel computation to manage these resources where user requests are very less for large amount of data to be used 2.  When it is the matter of managing ideas, grid requires sophisticated policies for resource allocation because of which grid reaches to its saturation level which makes resource management as one of the major critical issue in IaaS of cloud computing 3.  Researches S.H.H et al. 3 elaborated that in a resource allocation problem there are two actors cloud provider and cloud user cloud users who wants to utilize the resources with minimum cost and maximum performance .In the other hand cloud providers want to maximize revenue and get best utilization of the resources.  In cloud computing resource management is accomplished with effective resource allocation. It is done by distributing accessible resources required by cloud application on demand basis 4, 5.  Management and resources provisioning in cloud computing make the resource allocation a challenging issue in IaaS layer. Numerous methodologies have been devised by researchers such as on-demand resource allocation, resource heterogeneity, locality limitations, limited requirement and environmental requirements 6-13.      The provider in the cloud doesn’t include storage facilities alone, but provides hardware and software services too for the general public and the market oriented work. The services provided by the service provider can be anything or everything, i.e. infrastructure, the platform or the software resources. Each service is respectively called Infrastructure as Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). 13  In cloud computing, services can be offered in terms of resources. Resource Allocation (RA) is the process of providing the available resources to the needed cloud applications in the presence of internet 16. If no proper management of resources are held, the application starves for resources. Resource provisioning is the solution to the problem that allows the service providers to manage the resources for each application.  Researcher Madni et al. 3 has classified the resource allocation strategies into two major categories i.e. strategic based resource allocation and parametric based resource allocation. In the next session of this survey strategic based resource allocation will be discussed. Mainly this survey is focused on artificial intelligence strategies based resource allocation in cloud computing.  II.  METHODOLOGY  Before presenting methodology survey, motivation is needed so that we can understand the importance of resource management. According to researchers 3 resource allocation is necessary for cloud computing because it helps to understand the inference of resource allocation, it enhances the benefits for both cloud providers and cloud users in IaaS management.  The contribution and penetration of artificial intelligence not only into every field of computer science but also into inter disciplinary fields.  Use of this power creates immediate effects, increases in productivity and cost reduction 14.In cloud computing AI helps in optimizing and minimizing the make-span of allocation process by creating an intelligent methodology that works like human being 15.  Genetic Algorithm 17,18 as from the name itself is based on the father of biology Darwin’s theory of “survival of the fittest” where in this algorithm helps in analyzing the future based on the past or historical data where scheduling of VMs is done. In this task is scheduled using the fittest parameter of the scheduled process.  The author in 19, Thangaraj et.al defines the predefined policies for allocation of resources for infrastructure as a services (IaaS). He a special tool named Haizea is used for the policy for resource allocation where the tool reduces the rejection of the request .Nebula is the open source manager which uses the tool.  According to Sowmya Koneru et.al 20 the author states   that the efficiency of resource allocation is directly proportional to the scheduling algorithms. Round robin is the algorithm that is used which improvises in the overall turn- around time and also maximizes the efficiency reducing the processing cost as well.  In 21 Ikki Fujiwara et.al is a market based one which allows the participants to have a trade in accomplishing the task of resource allocation by which services is provided in a very effective manner. Workflows and co-allocations are provided to the participant and enables participants to carry future and current services in the forward market as well as on spot market.  CRAA/FA (Cloud Resource Allocating Algorithm via Fitness-enabled Auction) is introduced in 22, by Zuling et. al, is a novice idea which is very prudent if considered in a business point of view. Where price bargaining is basically done between the resource agents and service agents for cloud resources.  Apart from that the researchers have devised many AI based methods or algorithms such as Genetic Algorithm (GA), Simulated annealing (SA), Tabu Search (TS), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), Bacterial Foraging Algorithm (BF), Fish Swarm Optimization Algorithm (FS), Cat Swarm Optimization Algorithm (CS), Firefly Algorithm (FF), Cuckoo Search Algorithm (CS), Artificial Bee Colony (ABC), Bat Algorithm (BA) stating the various means by which effective allocation of resources can be carried out.  CONCLUSIONS  In this brief survey on artificial intelligence strategy based resource allocations are given so that the better approach can be selected for the new research work. There are many challenges that the cloud computing is facing. This survey paper leads to the resource allocation which can be a path finder for many researchers. It also acts as a key factor in balancing in a better way for service providing to the end users.  REFERENCES  1 Ian Foster et al., “Cloud Computing and Grid Computing 360-Degree Compared,” in Grid Computing Environments Workshop, Austin, TX, USA, 2008.   2 Jennifer Schiff. (Jan 21, 2010). Grid Computing and the Future of Cloud Computing Online, Available: http://www.enterprisestorageforum.com /outsourcing/features/article.php/3859956/Grid-Computing-and-theFuture-of-Cloud-Computing.htm   3 Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y. et al., “Recent advancements in resource allocation techniques for cloud computing environment: a systematic review,” in Cluster Comput (2017) 20: 2489. https://doi.org/10.1007/s10586-016-0684-4   4 Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-ofthe-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)  5 Madni,S.H.H.,Latiff,M.S.A.,Coulibaly,Y.,Abdulhamid,S.I.M.: An appraisal of meta-heuristic resource allocation techniques for IaaS Cloud. Indian J. Sci. Technol. 9(4), 1–14 (2016)   6 Mustafa,S.,Nazir,B.,Hayat,A.,Madani,S.A.:Resourcemanagement in cloud computing: taxonomy, prospects, and challenges. Comput. Electr. Eng. 47, 186–203 (2015)   7 Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu,   A.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98, 751–774 (2014)   8 Elghoneimy, E., Bouhali, O., Alnuweiri, H.: Resource allocation andschedulingincloudcomputing.In:2012InternationalConference on Computing, Networking and Communications (ICNC), pp. 309–314. IEEE (2012)   9 Mohan, N., Raj, E.B.: Resource Allocation Techniques in Cloud Computing–Research Challenges for Applications. In: 2012 Fourth International Conference on Computational Intelligence and Communication Networks (CICN), pp. 556–560. IEEE (2012)   10 Ma, T., Chu, Y., Zhao, L., Ankhbayar, O.: Resource allocation and scheduling in cloud computing: policy and algorithm. IETE Tech. Rev. 31(1), 4–16 (2014)   11 Parikh, S.M.: A survey on cloud computing resource allocation techniques. In: 2013 Nirma University International Conference on Engineering (NUiCONE), pp. 1–5. IEEE (2013)   12 Manvi, S.S., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)   13 Chana, I., Singh, S.: Quality of service and service level agreements for cloud environments: issues and challenges. In: Mahmood,Z.(ed.)CloudComputing,pp.51–72.Springer,NewYork (2014)   14 J.I. Lopez-Sanchez and L.E. Carretero-Diaz. ” The importance of artificial intelligence-expert systems in computer integrated manufacturing,” in Engineering and Technology Management, 1998. Pioneering New Technologies: Management Issues and Challenges in the Third Millennium. IEMC ’98 Proceedings, pp. 11-13 Oct. 1998.   15 Naveen Kumar Gondhi and Ayushi Gupta. “Survey on Machine Learning based scheduling in Cloud Computing,” in ISMSI ’17 Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, Pages 57-61. Hong Kong — March 25 – 27, 2017   16 V.Vinothina, Dr.R.Sridaran, Dr.Padmavathi Ganapathi, “Resource Allocation Strategies in Cloud Computing”, International Journal of Advanced Computer Science and Applications ,Vol. 3, No.6, 2012.   17 Ipsita Kar, R.N. Ramakant Parida, Himansu Das 2016. “Energy Aware Scheduling using Genetic Algorithm in Cloud Data Centers” International   Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)   18 Aleena Xavier T, Rejimoan R 2016 “Survey on Various Resource Allocation Strategies in Cloud” International Conference on Circuit,   Power and Computing Technologies ICCPCT   19 Thangaraj P, Soundarrajan S, Mythili A, “Resource allocation policy for IaaS in Cloud computing”, International Journal of Computer Science and  Management Research 2013, Vol 2, Issue 2, pp. 1645 – 1649.   20 Sowmya Koneru, V N Rajesh Uddandi, Satheesh Kavuri, “Resource  Allocation  Method  using  Scheduling  methods  for  Parallel  Data  Processing in Cloud”, International Journal of Computer Science and  Information Technologies, Vol. 3, 2012, pp. 4625 – 4628.   21 Ikki Fujiwara, Kento Aida, “Applying Double-sided Combinational Auctions to Resource Allocation in Cloud Computing”, 10th Annual   International Symposium on Applications and the Internet, 2010.   22 Zuling Kang, Hongbing Wang, “A Novel Approach to Allocate Cloud Resource with Different Performance Traits”, IEEE 10th International Conference on Services Computing, 2013, pp. 8-13.  


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