Obtaining Profit Maximization in Optimal Multi Server Configuration through Customer Satisfaction in Cloud Computing

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1 Obtaining Profit Maximization in Optimal Multi Server Configuration through Customer Satisfaction in Cloud Computing Gurrala Manasa Reddy 1, M.Swami Das 2 1 M Tech Student, Dept. of CSE, MREC (A), Telangana State, India. 2 Supervisor and Associate Professor, Dept. of CSE, MREC (A), Telangana State, India. Abstract - Today s world cloud computing becoming so popular because of an effective and efficient way to provide computing resources and services to customers on demand. A cloud service provider s view point profit is one of the most important considerations and it is mainly determined by the configuration of a cloud service platform under given market demand. But traditional single resource renting scheme cannot guarantee the quality of all requests and also wastes a large amount of resources. To overcome that weakness use Double -Quality-Guaranteed (DQG) resource renting scheme this combines long-term renting with short-term renting. An M/M/m+D queuing model and the performance indicators plays important role for profit maximization. For security purpose we are using attribute based encryption scheme. The result shows guaranteed the service quality of all requests, security also obtain more profit. Keywords: Cloud Computing, Guaranteed Service Quality, Multi server System, Profit Maximization, Queuing Model, Service-Level Agreement, Waiting Time. I. INTRODUCTION Cloud computing is that the delivery of resources and computing as a service instead of a product over the web, specified accesses to shared hardware, software, databases, data, and every one resources area unit provided to shoppers on-demand. Customers use and pay money for services on-demand while not considering the direct infrastructure prices and therefore the consequent maintenance price. Thanks to such benefits, cloud computing is changing into a lot of and a lot of in style and has received respectable attention recently. Nowadays, there are several cloud service suppliers, like Amazon EC2, Microsoft Azure, Saleforce.com, and then forth. As a form of recent IT industrial model, profit is a crucial concern of cloud service suppliers. The cloud service suppliers rent resources from infrastructure suppliers to set up the service platforms and supply paid services to customers to create profits.for cloud service providers, step by step instructions to design their cloud benefit stages to acquire the maximal profit becomes progressively the concentration that they focus on. The ideal setup issue with profit augmentation of cloud specialist co-ops has been examined in our past looks into which accepted that the cloud benefit request is known ahead of time and not influenced by outer components. Be that as it may, the demand landing rate of a specialist co-op is influenced by numerous components in real, and customer satisfaction is the most critical factor. II. EXISTING SYSTEM Cloud computing is the conveyance of assets and computing as an administration as opposed to an item finished the Internet, with the end goal that gets to shared equipment, programming, databases, data, and all assets are given to purchasers on-request. Clients utilize and pay for administrations on-request without considering the forthright framework costs and the resulting upkeep cost. Because of such points of interest, cloud computing is becoming increasingly mainstream and has gotten significant consideration as of late. These days, there have been numerous cloud specialist organizations, for example, AmazonEC2, Microsoft Azure, Saleforce.com, et cetera. As a sort of new IT commercial model, profit is a critical worry of cloud specialist organizations as shown in the Figure 2. The cloud specialist organizations lease assets from foundation suppliers to arrange the administration stages and give paid administrations to clients to make profits. For cloud specialist co-ops, how to design their cloud benefit stages to get the maximal profit becomes progressively the concentration that they focus on. The ideal design issue with profit expansion of cloud specialist co-ops has been inquired about in our Malla Reddy Engineering College (Autonomous), Vol. 4, Issue 1, March,

2 past explores which accepted that the cloud benefit request is known ahead of time and not influenced by outside components. A. Disadvantages The waiting time of the service request is too long Sharp increase of the renting. Cost or the electricity cost such increased cost may counterweight the gain from penalty reduction. So[Fig- 1], the single renting scheme is not a good scheme for service providers. III. PROPOSED SYSTEM The ask for landing rate of a specialist co-op is influenced by numerous components in genuine [Fig. 1], and customer satisfaction is the most vital factor. For instance, customers could present their undertakings to a cloud computing stage or execute them on their neighborhood computing stages. The customer conduct relies upon if the cloud benefit is sufficiently appealing to them. To arrange a cloud benefit stage legitimately, the cloud specialist organization should know how customer satisfaction influences the administration requests. Henceforth, considering customer satisfaction in profit improvement issue is fundamental. Nonetheless, few existing works contemplate customer satisfaction in taking care of profit expansion issue, or the current works considering customer satisfaction don't give an appropriate formalized definition for it. To address the issue, this paper embraces the idea in Business Administration, and initially characterizes the customer satisfaction level of cloud computing. A. Advantages The waiting time of the service request is too short. Providing unique key of each file due to cloud provides security to database. IV. ARCHITECTURE The high -level architecture for supporting market-oriented resource allocation in Data Centers and Clouds. There are basically four main entities involved. Fig. 1 The three-tier cloud structure. B. Queuing model A client sends demands that are approaching administration demands can't promptly process after they arrived, right off the bat ask for put in the line then it took care of by the access server. Queuing model follows first - come-first-serves (FCFS) technique Fig. 2: The multiserver system model, where service requests are first placed in a queue before they are processed by any servers. Malla Reddy Engineering College (Autonomous), Vol. 4, Issue 1, March,

3 Fig. 2 Multiserver System Model V. IMPLEMENTATION 1. Customer satisfaction module 2. M/M/m queuing model module 3. Service-Level Agreement module 4. Cloud Computing module A. Customer Satisfaction Module In light of the meaning of customer satisfaction level in financial matters, build up a figuring equation for estimating customer satisfaction in the cloud. Analyze the interrelationship between customer satisfaction and profit, and manufacture a profit streamlining model thinking about customer satisfaction. A profit expansion show in which the impact of customer satisfaction on the nature of administration (QoS) and cost of administration (PoS) is considered. From a monetary viewpoint, two components influencing customer satisfaction are QoS and PoS. The PoS is dictated by cloud specialist co-ops. The QoS is controlled by the administration limit of a cloud specialist organization which to a great extent relies upon its stage arrangement. Under the given evaluating methodology, the best way to enhance the customer satisfaction level is to advance the QoS, which can be accomplished by arranging cloud stage with higher administration limit. Doing as such can influence a cloud specialist co-op from two asides. On one hand, the higher customer satisfaction level prompts a higher piece of the overall industry, so the cloud specialist co-op can acquire incomes. Then again, more assets are leased to enhance the administration limit, which prompts the expansion of expenses. Consequently, a definitive answer for enhancing profit is to locate an ideal cloud stage arrangement conspire. In this paper, we assemble a customer satisfaction-mindful profit enhancement show and propose a discrete slope climbing calculation to locate the numeric ideal cloud arrangement for cloud specialist organizations. B. M/M/M QUEUING MODEL In the M/M/m display[fig-2], m is the quantity of servers, and all servers keep running at an indistinguishable speed s (estimated by the quantity of directions that can be executed in one unit of time). Expect that the inter arrival times of administration demands are free and indistinguishably circulated exponential arbitrary factors, at the end of the day, the entry demands take after a Poisson procedure with landing rate λ.the execution necessities of the errands (estimated by the quantity of guidelines to be executed) are i.i.d. exponential arbitrary factors r with mean r. Since the server execution speed is s, the administration times of the solicitations are additionally i.i.d. exponential irregular factors x = r/s with mean x = r/s. Subsequently, the normal administration rate, i.e., the normal number of administration asks for that can be completed by a server with speed s in one unit of time, is μ = 1/x = s/r. C. Service-Level Agreement The QoS is influenced by numerous components, for example [Fig-1], the administration time, the disappointment rate et cetera. Notwithstanding, in this paper, we measure the QoS of a demand by its reaction time for two reasons. In the first place, the administration time is effectively estimated. Second, it gives customers an instinctive sentiment QoS. For customers, they couldn't care less how disappointments are overseen when disappointments happen. They just care whether the errand can be completed effectively and to what extent it takes. The reaction times of solicitations are unique in relation to each other because of the changing framework workload and restricted administration limit, which prompts distinctive QoS and QoS satisfaction. By and large, every customer has a middle of the road reaction time which is identified with the execution necessity of its solicitations. We indicate the middle of the road reaction time of a demand with Malla Reddy Engineering College (Autonomous), Vol. 4, Issue 1, March,

4 execution necessity r by cr/s0, where s0 is standard speed of a server and c is a steady coefficient. On the off chance that the reaction time of a demand surpasses the passable esteem, the customer feels dissatisfaction with the administration, which prompts the debasing of the general customer satisfaction of the specialist co-op. D. Cloud Computing Cloud computing portrays a kind of outsourcing of computer administrations, like the manner by which the supply of power is outsourced. Clients can basically utilize it. They don't have to stress where the power is from, how it is made or transported. Consistently, they pay for what they expended. The thought behind cloud computing is comparable: The client can just utilize stockpiling, computing power, or exceptionally made improvement conditions, without worrying how these work inside. Cloud computing is normally Internet-based computing. The cloud is a representation of the Internet in view of how the web is portrayed in computer arrange charts; which implies it is a deliberation concealing the complex foundation of the web. It is a style of computing in which IT-related capacities are given "as a service", enabling clients to get to innovation empowered administrations from the Internet ("in the cloud") without information of or control over the advances behind these servers. VI. ALGORITHM A. Algorithm Implementation: Double Quality Guaranteed Scheme algorithm: The traditional single resource renting scheme cannot guarantee the quality of all requests but wastes a great amount of resources due to the uncertainty of system workload. To overcome the weakness, we propose a double renting scheme as follows, which not only can guarantee the quality of service completely but also can reduce the resource waste greatly. Step 1: A multi-server system is running with m servers and waiting for events. Step 2: queue Q is initialized as empty. Step 3: Event-when service request arrives and server is available then assign service request to that server. Step 4: if server is not available then put service request at end of the queue Q and record its waiting time. Step 5: Event-when server becomes idle and queue is empty then waiting for new service request. Step 6: if queue is not empty then take first service request and assign to that idle server. Step 7: Event - deadline of a request is achieved then rent a temporary server to execute the request and release that server when request is completed. B. Mathematical Module S = (I, F, O, C) S = System I = Set of inputs= user requests, package O = generated output = plan granted F = set of functions F1 = cloud User logon, registration, upload file, view policy terms, choose package, cost. F2 = BSP login, view user requests and accept them, publish infrastructure service and view graph modulations. F3 = ISP login, provide infrastructure services, view broker request, maintain users. C. Hill Climbing Algorithm Hill Climbing is a technique to solve certain optimization problems. In this technique, we start with a suboptimal solution and the solution is improved repeatedly until some condition is maximized. The idea of starting with a sub-optimal solution is compared to starting from the base of the hill, improving the solution is compared to walking up the hill, and finally maximizing some condition is compared to reaching the top of the hill. Algorithm: Hill Climbing Evaluate the initial state. Loop until a solution is found or there are no new operators left to be applied: Select and apply a new operator Evaluate the new state: goal quit better than current state new current state VII. DISCUSSION AND SUMMARY We consider customer satisfaction in taking care of ideal setup issue with profit boost. Since the current works don't give an appropriate definition and count equation for customer satisfaction, subsequently, we first give a meaning of customer satisfaction utilized from financial aspects and build up a recipe for estimating customer satisfaction in cloud. In view of the warmth of customer satisfaction on workload, we break down the association between the market request and the customer satisfaction and give the estimation of the real assignment landing rate under various designs. What's more, we think about an ideal setup issue of profit boost [Fig.1]. The ideal arrangements are unraveled by a discrete slope climbing calculation. In conclusion, a Malla Reddy Engineering College (Autonomous), Vol. 4, Issue 1, March,

5 progression of computations is led to dissect the changing pattern of profit. A group of calculations are conducted to compare the profit and optimal configuration of two situations with and without considering the affection of customer satisfaction on customer demand. The results show that when considering customer satisfaction, our model performs better in overall. REFERENCES [ 1 ] K. Hwang, J. Dongarra, and G. C. Fox, Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, Elsevier Publications, Morgan Kaufmann, [ 2 ] J. Cao, K. Hwang, K. Li, and A. Y. Zomaya, Optimal multiserver configuration for profit maximization in cloud computing, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 6, pp , [ 3 ] A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, and I. Stoica, Above the clouds: A berkeley view of cloud computing, Electrical Engineering and Computer Sciences University of California at Berkeley, Technical Report No. UCB/EECS , [ 4 ] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, Vol. 25, No. 6, pp , [ 5 ] P. Mell and T. Grance, The NIST definition of cloud computing. National Institute of standards and Technology, Information Technology Laboratory, vol. 15, pp. 1-3, [ 6 ] J. Chen, C. Wang, B. B. Zhou, L. Sun, Y. C. Lee, and A. Y. Zomaya, Tradeoffs between profit and customer satisfaction for service provisioning in the cloud, pp , [ 7 ] J. Mei, K. Li, J. Hu, S. Yin, and E. H.-M. Sha, Energyaware preemptive scheduling algorithm for sporadic tasks on dvs platform, MICROPROCESS MICROSY., Vol. 37, No. 1, pp , [ 8 ] P. de Langen and B. Juurlink, Leakage-aware multiprocessor scheduling, J. Signal Process. Sys., Vol. 57, No. 1, pp , [ 9 ] G. P. Cachon and P. Feldman, Dynamic versus static pricing in the presence of strategic consumers, Tech. Rep., [ 10 ] Y. C. Lee, C. Wang, A. Y. Zomaya, and B. B. Zhou, Profitdriven scheduling for cloud services with data access awareness, J. Parallel Distr. Com., Vol. 72, No. 4, pp , [ 11 ] M. Ghamkhari and H. Mohsenian-Rad, Energy and performance management of green data centers: a profit maximization approach, IEEE Trans. Smart Grid, Vol. 4, No. 2, pp , [ 12 ] A. Odlyzko, Should flat-rate internet pricing continue, IT Professional, Vol. 2, No. 5, pp , [ 13 ] G. Kesidis, A. Das, and G. de Veciana, On flat-rate and usage-based pricing for tiered commodity internet services, in 42nd Annual Conf. Information Sciences and Systems. IEEE, pp , [ 14 ] S. Shakkottai, R. Srikant, A. Ozdaglar, and D. Acemoglu, The price of simplicity, IEEE J. Selected Areas in Communications, Vol. 26, No. 7, pp , [ 15 ] H. Xu and B. Li, Dynamic cloud pricing for revenue maximization, IEEE Trans. Cloud Computing, vol. 1, no. 2, pp , Malla Reddy Engineering College (Autonomous), Vol. 4, Issue 1, March,