Linked List Implementation of Discount Pricing in Cloud

Transcription

1 Linked List Implementtion of Discount Pricing in Cloud Mlr.J 1, S.Jesinth Strvin 2 Mster of Engineering in computer Science, Assistnt professor IT deprtment Ponjesly College of Engineering Abstrct: In the cloud computing environment computtionl resources re redily nd elsticlly vilble to the customers. In order to ttrct customers with vrious demnds, most Infrstructure-s--service (IS) cloud service providers offer severl pricing strtegies such s py s you go, py less per unit when you use more (so clled volume discount), nd py even less when you reserve. In order to enjoy these discounts, the customers must be redy to djust the time limits. By strtegiclly scheduling multiple customers resource request, cloud broker tkes the responsibility of distributing the discounts offered by cloud service providers. Here the focus is on how broker cn help group of customers to fully utilize the volume discount pricing strtegy offered by cloud service providers through cost-efficient online resource scheduling. A rndomized online stck-centric scheduling lgorithm (ROSA) is implemented with linked list in order to mintin the sttus of the resource nd to llocte resources without time constrins. Keywords: Broker-Meditor, Computing Opertion of computers, Discount- Deduction, Instnce-Single occurrences, Rndom Informl, Scheduling Arrnge. I. INTRODUCTION The linked list is introduced here to mintin the current sttus of the resource. Doubly linked list is used to mintin the previous nd next stte of the resource. Pricing is done by cloud brokers through cost efficient online resource Scheduling clled ROSA. The min objective of this project is to mximize resource utiliztion, with less pyment mount but without djusting the time limits. The drwbck in this domin is the user hs to djust the time limits in order to enjoy the volume discounts. To overcome this problem here we re implementing the linked list in the Rndomized Online Stck - centric Algorithm. ROSA with linked list comprtively chieves better performnce with flexibility. A. EXISTING SYSTEM: II. SYSTEM ANALYSIS In n Infrstructure-s--Service (IS) cloud, the rel-world chrging scheme hs become bsurdly complicted. For instnce, cloud providers usully dopt n hourly billing scheme, even if the customers do not ctully utilize the llocted resources in the whole billing horizon. In the current cloud mrket, mny cloud providers offer big discount for reserved nd long term request. The cloud provider provides the resources to the users only fter the requested time durtion. Thus it cuses the time wstge. In order to enjoy the discount the users hs to djust to the time limits. Rndomized Online Stck Centric lgorithm is used for resource scheduling. Multiple instnce of resource utiliztion is prohibited nd single instnce resource utiliztion is llowed. Individul user comfort is not considered by the cloud brokers. The resources re not llocted by the end users even if it is not in use. The gol is to mximize resource utiliztion so tht more customers cn be ccommodted nd in return ech cn py less. Pge 65

2 Drwbcks: Customer need to wit for the resource even if it is not in use. Individuls cnnot enjoy the volume discounts without djusting the time limits. Time flexibility fces the mjor problem. Multiple instnce of resource utiliztion is not llowed. B. PROPOSED SYSTEM: In order to ttrct customers with vrious demnds, most Infrstructure-s--service (IS) cloud service providers offer severl pricing strtegies such s py s you go, py less per unit when you use more (so clled volume discount), nd py even less when you reserve. The diverse pricing schemes mong different IS service providers or even in the sme provider nurtures the mrket of cloud brokers. By strtegiclly scheduling multiple customers resource requests, cloud broker cn fully tke responsibility of the discounts offered by cloud service providers. It focus on how broker cn help group of customers to fully utilize the volume discount pricing strtegy offered by cloud service providers through cost-efficient online resource scheduling. Here, the linked list implementtion of ROSA is used. Thus it mintins the current sttus of the resource. So the resources cn be enjoyed by the end users before the requested time if it is free. The current stte of the previous link nd the next stte is mintined to eliminte the witing time of the users. Advntges: Multiple of instnce of resources cn be shred mong individuls. Time flexibility cn be enhnced. End users cn enjoy the resources without strict time durtions. Customers cn utilize the resources if it is free. C. ALGORITHM: Rndomized Online Stck-centric Scheduling Algorithm (ROSA): The online resource scheduling problem ssumes tht, t ny time instnt t, the scheduler only knows the tsks which rrive upon or before t. The scheduler does not rely on ny knowledge of future informtion. The doubly linked list is used for online tsk scheduling to mke decision with informtion vilble so fr. Thus it uses the rndomized online stck centric lgorithm implemented with linked list. Algorithm: Rndomized online stck centric lgorithm with linked list 1 Initiliztion: n ordered list of time instnts I = Ф; 2 while n tsk J i rrives do 3 Insert time instnts t i nd t i d into I; If next instnce > t i d then t i ---- J i+1 If previous instnce < t i then t i J i-1 4 Find ll subintervls [t i, t i d ] ech representing time period in betweentwo djcent time instnts in I, nd mrk them s unprocessed; 5 while wi > 0 do 6 Select the unprocessed subintervl [t i, t i d ] denoted by [t1, t2], tht hs the highest tsk density (rndomly select one if there is tie); Pge 66

3 Where, t i - rrivl time of the job request J i - current resource stte J i+1 -next resource stte J i-1 - previous resource stte t i d - specified dedline of the job request Multiple customers my submit job requests t rndom instnts with rndom worklod tht should be fulfilled before specified dedline to broker. By ssuming tht the inter-rrivl times for job requests re rbitrry. The processing time for ech job is deterministic nd known to the broker given the resource llocted to the job. The broker is responsible for purchsing computtionl resource from IS clouds, llocting resource to nd executing jobs, s well s meeting job dedlines. The dedlines specified by the customers re flexible. Different from PS cloud, where the customers directly submit job requests to cloud service providers, brokers medite the process by orgnizing the job requests in mnner which benefits the most from the volume discounts provided by the cloud provider. Both the cloud provider nd the customers benefit from this medition. FIG.1 OPTIMAL SCHEDULING USING CLOUD BROKERS Figure 1 illustrtes the four functionlities such s 1) Job request 2) scheduling 3) volume discounts 4) sttus identifiction.initilly multiple customers sends the job request to the cloud broker thus the scheduler performs scheduling.the cloud broker finds the inter rrivl time nd llocte the unit time slots. Thus the job scheduling is done efficiently.broker performs low cost function opertion to return cost with volume discount. Pge 67

4 Multiple customers: Multiple customers involve the individul users. They cn send resource request to both the cloud brokers or cloud providers. Bsed on their request the resources re llocted to them. Cloud provider s offers vrious discount schemes for bulk resources but it is beyond the need of individul cloud users. Such discount schemes re beneficil to orgniztions. Cloud broker: The cloud brokers emerge s the meditors between the cloud providers nd the customers. Here the cloud brokers tkes the responsibility of the providers in providing discounts bsed on the utility. Brokers purchse the bulk mount of resources nd distribute the discount bsed on the independent purchsing of resources. Arrivl time: The rrivl time is the time t which the user log on to the system. It is lso recorded in the stck for the future verifiction. Dedline: It is the time requested for the resources to be obtined. The dedline is provided t the time of user logon into the system. Thus the resources re vilble to the user only fter the dedline timing. But by using the doubly linked list implementtion the strict dedline cn be removed. It mintins the list of user requirements nd bsed on tht the resources will be llocted to the end users if it is free. Job schedule: The job is scheduled to the user t the unique time slots. Thus it cn ssigned bsed on the rrivl time nd dedline. Volume discount: Bsed on the utility discount the volume discounts cn be enjoyed, The more you use the resources the lrge your discount will be. Thus the mthemticl formultion is used for the low cost evlution Cloud is n emerging computing mrket where cloud providers, brokers, nd users shre, medite, nd consume computing resource. With the evolution of cloud computing, Py-s-you-go pricing model hs been diversified with volume discounts to stimulte the users doption of cloud computing. MODULES: There re four modules in the proposed system they re involved in efficient resource mngement in the cloud environments.so the resources cn be mnged efficiently with low cost. User registrtion Job scheduling Volume discount Sttus identifiction With the help of these modules the resources cn be retrieved efficiently by the users nd ech nd every individul users cn enjoy the volume discounts bsed on their utility threshold without time limits. III. CONCLUSION Cloud is n emerging computing mrket where cloud providers, brokers, nd users shre, medite, nd consume computing resource. With the evolution of cloud computing, Py-s-you-go pricing model hs been diversified with volume discounts to stimulte the users doption of cloud computing. It shows how broker cn schedule the jobs to users with volume discounts so tht the mximum cost sving cn be chieved for its customers. For tht n online scheduling lgorithm is developed nd its competitive rtio is derived. Simultion results hve shown tht the proposed online scheduling lgorithm outperforms other conventionl scheduling lgorithms. The sptil nd temporl resolution is involved in reshping the incoming VM request. Mthemticl evlution is used for reducing the cost with volume discounts. Rndomized online stck centric lgorithm implemented with doubly linked list is efficiently used for the resource lloction by mpping the time instnces s processed nd unprocessed. As the multiple instnces of resources re scheduled to the individul users without ny time limits nd the end users re pying only for their used resources, it increses the demnd for the cloud resources. As the multiple instnce of resource utiliztion is llowed in the cloud brokerge it is efficient for the online customers. Thus the cloud users cn utilize the resources without ny time dely with the volume discounts. Pge 68

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