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Profit Maximization for Service Providers using Hybrid Pricing in Cloud Computing

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Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising to offer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. While these „clouds‟ are the natural evolution of traditional clusters and data centers, they are distinguished by following a pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit of service providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spot pricing techniques.

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Profit Maximization for Service Providers using Hybrid Pricing in Cloud Computing

  1. 1. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 218Profit Maximization for Service Providers using HybridPricing in Cloud ComputingN.Ani Brown MaryAnna UniversityTirunelveli, IndiaAbstract: Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising tooffer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance anduptime promises for their services. While these „clouds‟ are the natural evolution of traditional clusters and data centers, they aredistinguished by following a pricing model where customers are charged based on their utilization of computational resources, storageand transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed asIaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit ofservice providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spotpricing techniques.Keywords: Service-Level Agreements, Infrastructure as a Service, Platform as a Service, Software as a Service, Hybrid Pricing.1. INTRODUCTIONCloud computing is not a total new concept; it is originatedfrom the earlier large-scale distributed computing technology.However, it will be a subversion technology and cloudcomputing will be the third revolution in the IT industry,which represent the development trend of the IT industry fromhardware to software, software to services, distributed serviceto centralized service. Cloud computing is also a new modelof business computing, it will be widely used in the nearfuture. The core concept of cloud computing is reducing theprocessing burden on the users‟ terminal by constantlyimproving the handling ability of the “cloud”, eventuallysimplify the users‟ terminal to a simple input and output. Allof this is available through a simple Internet connection usinga standard browser or other connection. It manages a varietyof different workloads, including the batch of back-endoperations and user-oriented interactive applications. Itrapidly deploy and increase workload by speedy. It providesphysical machines or virtual machines. It supportsredundancy, self-healing and highly scalable programmingmodel, so that workload can be recover from a variety ofinevitable hardware/software failure.The manufacturing industry is undergoing a majortransformation enabled by IT and related smar ttechnologies.Cloud computing is one of such smar ttechnologies. The mainthrust of Cloud computing is to provide on-demandcomputing services with high reliability, scalability andavailability in a distributed environment. The NationalInstitute of Standards and Technology(NIST) [14] definedcloud computing as„„a model for enabling ubiquitous,convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, servers,storage, applications, and services) that can rapidlyprovisioned and released with minimal management effort orservice provider interaction.‟‟ Cloud computing providesresources such as processing power, bandwidth and storagecapacity. Software as a service providers has to rent resourcesfrom the infrastructure as a service providers and provide it tothe users, so there is no profitable pricing function for theservice providers. So we implement a pricing function calledHybrid pricing to improve the profit of service providers.Here, section II consist of Related Work , section III consistof System Design, section IV,V,VI consists of Fixed, Spotand Hybrid pricing Implementations. At last Section VIIconsist of Results that has been obtained.2. RELATED WORKCloud computing is an emerging technology in the IT world.Some features of cloud, such as low cost, scalability,robustness and availability are attracting large-scale industriesas well as small business towards cloud. Cloud computing is amodel for enabling convenient, on demand network access toa shared pool of configurable computing resources (e.g.,networks, servers, storage, applications, and services) that canbe rapidly provisioned and released with minimalmanagement effort or service provider interaction. At present
  2. 2. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 219some major cloud providers are Amazon Web Services [1],Microsoft Azure [2] and Google AppEngine [3]. These cloudproviders offer many type of services for monitoring,managing and provisioning resources and applicationservices. A cloud provider can support more number of userswith same number of resources [4]. During the busy hoursservice provider needs more resources and at other periods oftime load on the service providers are very less, so there is aneed of continuous scale up and scale down the serviceproviders infrastructure of resources. These scale up and scaledown operations require dynamic provision [6].Yi et al. give an approach to minimize the costs ofcomputations using Amazon EC2s spot instances for resourceprovisioning. This paper also considers the application ofmarket oriented mechanisms in [7]. Fabien Hermenier et al.proposed a new approach, Entropy, in a homogeneous clusterenvironment, which takes into account both the problem ofallocating the virtual machines to available nodes and theproblem of how to migrate the virtual machines to thesenodes. The performance overhead is determined by the timerequired to choose a new configuration and the time requiredto migrate virtual machines according to the configuration.The Entropy resource manager can choose migrations that canbe implemented efficiently, incurring a low performanceoverhead in [10]. Wei et al. used game theory to handle theresource allocation in cloud computing. In their approach, aBinary Integer Programming method is proposed to solve theparallel tasks allocation problem on unrelated machinesconnected across the Internet. Their algorithms take bothoptimization and fairness into account and provide a relativelygood compromise resource allocation. But these methods canonly be used for seeking optimal allocation solution for thecomplex and dynamic problems that can be divided intomultiple cooperative subtasks in [13].3. SYSTEM DESIGNWhen two or more pricing joined together it is called Hybridpricing. Here fixed and spot pricing are combined to form theHybrid pricing technique. IaaS providers maintain the virtualmachine with the help of the cloud storage. SaaS providersrent resources from the IaaS providers and provide it to theusers. Figure 1 Shows clearly the cloud environment consistof virtual machines and it all maintained in the cloud storageand that was controlled by the controller storage. A SaaSprovider rents resources from IaaS providers and leasessoftware as services to users. SaaS providers aim atminimizing their operational cost by efficiently usingresources from IaaS providers, and improving CustomerSatisfaction Level (CSL) by satisfying SLAs, which are usedto guarantee QoS requirements of accepted users. From SaaSprovider‟s point of view, there are two layers of SLA withboth users and resource providers.. It is important to establishtwo SLA layers, because SLA with user can help the SaaSprovider to improve the customer satisfaction level by gainingusers‟ trust of the quality of service; SLA with resourceproviders can enforce resource providers to deliver thesatisfied service. If any party in the contract violates its terms,the defaulter has to pay for the penalty according to theclauses defined in the SLA.An IaaS provider, offers VMs to SaaS providers and isresponsible for dispatching VM images to run on theirphysical resources. The platform layer of SaaS provider usesVM images to create instances. It is important to establishSLA with a resource provider, because it enforces theresource provider to guarantee service quality. Furthermore, itprovides a risk transfer for SaaS providers, when the terms areviolated by resource provider. In this work, we do notconsider the compensation given by the resource providerbecause 85% resource providers do not really provide penaltyenforcement for SLA violation currently [22].Hybrid PricingCloudEnvironmentFixed andSpot PricingTechniques UsersIaasProvidersSaas ProvidersVMInstancesCloudStorageControllerStorageFigure 1: Architecture for Hybrid PricingUsers and the providers negotiate for the services, that is theyhave an agreement between them. After having theiragreement, resources will be provided to users with the helpof the Service Level Agreement contract. If the request can beaccepted, a formal agreement (SLA) is signed between bothparties to guarantee the QoS requirements such as response
  3. 3. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 220time. That contract has been explained clearly in figure 2. TheService Level Agreement includes the following constraintsService Initiation Time that gives how long it takes to deploya VM. Then Price shows how much a SaaS provider has topay per hour for using a VM from a resource provider. ThenInput Data Transfer Price shows how much a SaaS providerhas to pay for data transfer from local machine to resourceprovider‟s VM. Then Output Data Transfer Price shows howmuch a SaaS provider has to pay for data transfer fromresource provider‟s VM to local machine. Then ProcessingSpeed shows how fast the VM can process. Then DataTransfer Speed shows how fast the data is transferred and itdepends on the location distance and also the networkperformance.Figure 2: SLA contractAlgorithm for Accepting or Rejecting SLAContract1. When a task finishes or anew job is received:1.1. Updates user constraintssuch as deadline, budget,filesize, penalty rate.1.2. If file size > deadline1.2.1. Reject the RequestElse1.2.2. Accept the RequestDeadlineBudgetPenalty RateFile SizeRequested LengthUser ConstraintsThis algorithm depends on user accept or reject the offerprovided by IaaS providers. Here cost and profit arecalculated. If there is profit for SaaS providers, request will beaccepted or rejected. For one second only thousand millioninstruction per second can be calculated. So users deadline iscompared with the file size, if file size is more than thedeadline then request will be rejected or it will be accepted.Thus the SLA contract will be either accepted or rejected withthe help of the constraints.4. FIXED PRICINGIMPLEMENTATIONIf the Investment Return is greater than the ExpectedInvestment Return then the resources will be provided. Onlyfixed price will be offered for users. Here we get users fiveconstraints they are deadline, budget, penalty rate ratio, inputfile size and requested length. Deadline first shows themaximum time user would like to wait for the result. Then theBudget shows how much user is willing to pay for therequested services. Then Penalty Rate Ratio shows ratio forconsumers‟ compensation if the SaaS provider misses thedeadline. Then Input File Size asks the size of input fileprovided by users. Then Requested Length shows how manyMillions of Instructions (MI) are required to be executed toserve the request.In fixed pricing, we calculate the cost with help of processingcost + data transfer cost + virtual machine initiation cost +penalty delay cost. Then profit for providers is calculated byreducing the cost from the budget that is obtained from users.
  4. 4. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 221Algorithm for Fixed Pricing1. When a task finishes or anew job is received:1.1. Updates user constraintssuch as deadline, budget, filesize, penalty rate, requestedlength;1.2 Profit = Budget - Cost1.2.1. Cost = Processing cost +Data transfer Cost + VMinitiation cost + Penalty DelayCostDeadlineBudgetPenalty RateFile SizeRequested LengthUser Constraints5. SPOT PRICINGIMPLEMENTATIONWhen more than one users request for the same resource atthe same time , depending on the profit of SaaS providers theresource will be provided to the user. This algorithm is basedon users given inputs such as Deadline, Budget, File Size.Here Investment Return and Expected Investment Return arecalculated. In this technique, response time is calculated byjust calculating the processing time, virtual machine initiationtime and penalty delay time. Return Investment is calculatedby the profit by the response time. Expected ReturnInvestment is calculated by the cost by the response time. Thecondition is if the Return Investment Return is more than theExpected Investment Return then there is profit for providersso the resources are provided.Algorithm for Spot Pricing1. When a task finishes or a new job isreceived:1.1. Updates user constraints;1.2 Response Time = Processing Time +VM Initiation Time + Penalty DelayTime1.2.1. Return Investment = Profit/Response Time1.2.2. Expected Return Investment = Cost/Response Time1.3 if Return Investment > ExpectedReturn Investment1.3.1. Accept the RequestElse1.3.2. Reject the RequestDeadlineBudgetPenalty RateFile SizeRequested LengthUser Constraints6. HYBRID PRICINGIMPLEMENTATIONHybrid pricing is a complete combination of fixed and spotpricing. This algorithm is used when more than one userrequest for the same resources at the same time. Deadline ,Budget and File size are obtained by more than one user. Theuser who provides more profit for SaaS providers will beselected and resources will be provided.From a SaaS provider‟s point of view, there is a legalcontract-SLA with any customer and if any party violatesSLA terms, the defaulter has to pay for the penalty accordingto the clauses defined in the SLA. The SLA properties includeSaaS provider pre-defined parameters and the customerspecified QoS parameters. The properties defined in the SLAare as follows, Request Type defines the customer requesttype, which is „fist time rent‟ or „upgrade service‟. „First timerent‟ means the customer is the customer who is renting a newservice from this SaaS provider. „Upgrade service‟ includestwo types of upgrade, which are „add account‟ and „upgradeproduct‟. Then Product Type shows the software productoffered to customers. Then Account Type constrains themaximum number of accounts a customer can create. ContractLength shows how long the software service is legallyavailable for a customer to use. Number of Accounts showsthe actual number of accounts that a customer wants to create.Then Number of Records shows the maximum number ofrecords a customer is able to create for each account duringthe transaction and this will impact the data transfer timeduring the service upgrade. Response Time represents theelapsed time between the end of a demand on a softwareservice and the beginning of a service.Algorithm for Hybrid PricingUser1User 2User 3User 4User 51. Selection of User on thebasis of Maximum Profitattained by each users.2. Profit is calculated forall users.3. Resources are providedto user, who provides moreprofit for SaaS Providerswithin minimumdeadline.DeadlineBudgetPenalty RateFile SizeRequestedLength
  5. 5. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 222Here the concept of both fixed and spot pricing are combinedtogether to form the hybrid pricing. Since fixed and spotpricing are totally new concept they both are combinedtogether to improve the profit of service providers.7. RESULTSThe results shows the comparison between fixed, spot andhybrid pricing. On comparing with deadline and budget wecan see each results clearly shows the profit maximizationusing hybrid pricing. Totally there are five constraints , herewe took two constraints they are deadline and budget. Bothare compared with the three pricing techniques.Table 1: Deadline Vs ProfitDeadline(secs)/Profit(Rupees)FixedPricingSpotPricingHybridPricingTight 1000 2200 3800Medium 1800 3400 4600Relax 2100 3600 4900Very Relax 2900 4400 5800This table shows clearly that Hybrid pricing increases theprofit compared with the fixed and spot pricing. Tight showsthat seconds range between one to six seconds and mediumshows the range between six to twelve seconds. Relax showsthat the process is performed in very relaxed way and veryrelax shows that user is waiting for a long time.Figure 3: Deadline Vs ProfitThis figure shows the profit maximization using hybridpricing to a value of 6000 compared to the fixed and spotpricing. When compared to tight, very relax provides moreprofit for service providers. This shows Hybrid pricingprovides more profit compared to other pricing techniques.Table 2: Budget Vs ProfitBudget /Profit(Rupees)FixedPricingSpotPricingHybridPricingSmall 1245 2300 3756Medium 2600 3400 4200Large 4600 5235 6344This table shows that the budget that is provided by users inthree ways that is small budget users, medium budget usersand large budget users. Here the profit is obtained more inhybrid pricing technique. When compared with fixed pricing ,spot pricing gives more profit. When compared with hybridpricing , this provides more profit.Figure 4: Budget Vs ProfitThis graph clearly shows that the profit is maximized in spotpricing compared to the fixed pricing and then profit is morein hybrid pricing compared to the spot pricing techniques.8. CONCLUSIONThus the main goal to improve the profit of service providershas been satisfied. The three pricing techniques has beenexplained and implemented and their results are shown. Fixedand Spot pricing both are the best techniques but when theyare combined and used it provides more profit when are usedsingle-handedly.ACKNOWLEDGEMENTNone of this work would have been possible without theselfless assistance of a great number of people. I would like to
  6. 6. International Journal of Computer Applications Technology and ResearchVolume 2– Issue 3, 218 - 223, 2013www.ijcat.com 223gratefully thank all those members for their valued guidance,time, helpful discussion and contribution to this work.REFERENCES[1] Amazon Elastic Compute Cloud,"http://aws.amazon.comec2 .[2] Windows Azure Platform,http://www.microsoft.com.azure/ (March 17,2010).[3] Google App Engine, http://appengine.google.com(March17, 2010).[4] R. Buyya, R. Ranjan, and R. N. Calheiros, "InterCloud:Utilityoriented federation of Cloud computingenvironments for scaling of application services," inProceedings of the 10th International Conference onAlgorithrns and Architectures for Parallel Processing(ICA3PPIO), ser. Lecture Notes in Computer Science,vol. 6081. Busan: Springer, May 2010, pp. 13-3.[5] N.A. Vouk, "Cloud computing-issues, research andimplementation", published in Proceedings of 30thInternational Conference on Information TechnologyInterfaces(ITI 2008), Dubrovnik, Croatia, 2008.[6] http://www.vmware.com.virtualization.[7] Inigo Goiri, Jordi Guitart, Jordi Torres, "Economic modelof a Cloud provider operating in a federated Cloud ",Springer Science+Business Media, LLC 2011 Inf SystFront DOL 10. I007/sI0796-011-9325-x.[8] P.B. Chun, D.E. Culler, "User-centric performanceanalysis of market-based cluster batch schedulers",published in Proceedings of the 2nd IEEE/ACMInternational Symposium on Cluster and Grid Computing(CCGrid 2002), Berlin, Germany, 2002.[9] Y.C. Lee, C. Wang, A.Y. Zomaya, B.B. Zhou, "Profit-driven service request scheduling in clouds", published inProceedings of the International Symposium on Clusterand Grid Computing (CCGrid 2010), Melbourne,Australia, 2010.[10] F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller and J.Lawall. Entropy: a Consolidation Manager for Cluster. Inproc. of the 2009 International Conferenceon VirtualExecution Environments (VEE‟09), Mar.2009.[11] C.S. Yeo, R. Buyya, "Service level agreement basedallocation of cluster resources: Handling penalty toenhance utility", published in the Proceedings of the 7thIEEE International Conference on Cluster Computing(Cluster 2005), Boston, MA, USA, 2005.[12] Y.F. Rana, M. Warnier, T.B. Quillinan, F. Brazier, D.Cojocarasu, "Managing violations in service levelagreements", published in the Proceedings of the 5thInternational Workshop on Grid Economics and BusinessModels (GenCon 2008), Gran Canaria, Spain, 2008.[13] Guiyi Wei, Athanasios V. Vasilakos, Yao Zheng andNaixue Xiong. A game-theoretic method of fair resourceallocation for cloud computing services. The Journal ofSupercomputing,Volume 54, Number 2, 252-269.[14] Mell P, Grance T. Perspectives on cloud computing andstandards. National Institute of Standards andTechnology (NIST). Information TechnologyLaboratory; 2009.[15] Mario Mac´ıas, J. Oriol Fit´o and Jordi Guitart ,"Rule-based SLA Management for Revenue Maximisation inCloud Computing Markets", published in theProceedings of the 12th IEEE International Conferenceon Cluster Computing (Cluster 2009), Boston, MA,USA, 2009.[16] Hadi Goudarzi and Massoud Pedram ,"Multi-dimensionalSLA-based Resource Allocation for Multi-tier CloudComputing Systems", published in the Proceedings ofthe International Symposium on Cluster and GridComputing (CCGrid 2011), Melbourne, Australia, 2011.[17] Dimitrios Zissis , Dimitrios Lekkas ,"Addressing cloudcomputing security issue" published in ELESIVERPublications of Future Generation Computer Systems28(2012) 583–592.[18] Nir Kshetri ,"Privacy and security issues in cloudcomputing: The role of institutions and institutionalevolution", published in the Proceedings of IEEEInternational Conference on Service Oriented Computingand Applications (SOCA 2011), Newport Beach,California, USA, 2011.[19] Dan Svantesson, Roger Clarke ,"Privacy and consumerrisks in cloud computing", published in ELESIVERpublications of computer law & security review 26(2010)391 - 397.[20] Gaofeng Zhang , Yun Yanga, Jinjun Chen, "A historicalprobability based noise generation strategy for privacyprotection in cloud computing", published in ELESIVERPublications in the Journal of Computer and SystemSciences 78 (2012) 1374–1381.[21] Brototi Mondal , Kousik Dasgupta , Paramartha Dutta ,"Load Balancing in Cloud Computing using StochasticHill Climbing-A Soft Computing Approach" , publishedin ELESIVER publications Procedia Technology 4 (2012 ) 783 – 789.[22] CIO, retrieved on 10 Sep. 2010, http://www.cio.com.au.

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