Concave Pricing for Cloud Computing
Online Resource Scheduling Under
Concave Pricing for Cloud Computing
ABSTRACT
Online concave pricing have been developed computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. Thediverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers’ resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present arandomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theor etical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving
EXISTING SYSTEM:.
Online concave pricing Minimization with a concave cost function usually falls into theclass of NP-hard problems, for example, the concave network flow problem . This partially suggests the hardness of our scheduling problem. Though we have not formally proved its NPharness, we have discovered the properties of optimal scheduling with a general concave cost function. These properties provide us with valuable insights on making cost-client decisions in o_ineand online resource scheduling. Furthermore, these properties have inspired us to find an optimal online scheduling algorithm for a special concave cost function. In this section, we present the properties that an optimal schedule should have and point out why it is hard to come up with an optimal scheduling algorithm with polynomial complexity. Additional symbols used are listed in Table. The lack of convexity in the cost function invalidates all existing solutions such as those in Note that linear programming (LP) with rounding approximation is commonly used for constrained optimal job scheduling problems. In Section , we demonstrate that by proving properties of optimal solutions, elegant scheduling algorithm can be found when finding an appropriate LP solution is hard.
PROPOSED SYSTEM:
Online concave pricing Google cluster data is suitable for evaluating the proposed scheduling algorithm as it provides job requests with a large variety of resource requirement. The job requests submitted by different users exhibit different patterns in term of inter-arrival time and job length. At any time instance, the number of scheduled jobs is limited by a constant. All jobs can be processed in a unit time interval. All jobs can be preempted only at integer time points. They showed that the problem can be solved online using linear programming. In this section, constraint is relaxed. We assume Jobs can be preempted at any time points. The number of scheduled jobs at any time instant is unbounded. This problem is solvable using Linear Programming. In the rest of this section, an optimal online algorithm which schedules jobs greedily and sequentially
is introduced . This greedy approach is more intuitive. The purpose of introducing this special case cost function and the greedy algorithm is to assess the proposed online algorithm man approximate exponential distribution. To generate sample job requests for the broker, we arbitrarily form groups of ten users and perform evaluation on each group. distribution of job inter-arrival times for the trace data. It can
be seen that it is hard to fit the inter-arrival times to a known distribution. A similar observation and a more detailed analysis on the job arrivals could be found as well. This further justifies the usefulness of our online algorithm, which works for arbitrary job arrivals .The job records of the trace data contain three attributes relevant to our simulation, job arrival time, job workload, and job resource requirement. For an arbitrary job arrivaltime.
MODULE DESCRIPTION:
1. User.
2. Cloud Provider.
3. Cloud Broker.
4. Cloud Computing.
5. Bulk Purchasing.
6. Concave Pricing.
1.users .
User registration after login to different pricing for upload file to customers.The processor to text, video, image, so that a higher volume discount can be enjoyeddue to the higher amount of total requested resource of the customers.
2. Cloud Provider.
The provider takes customers advantage of volume discount by packing multiple customers’ resource requests to meet the providers’ high thresholdfor bulk resource purchase, thus, the total cost can be reduced andeach can pay less consequently. While the advantages of temporalmultiplexing have been thoroughly investigated before , thebenefit of spatial multiplexing remains less explored.
3. Cloud Broker.
The cloud brokers to emerge asmediators between the customers and the providers. dedicated cloud brokers are emerging
to help customers make better purchase decisions. Recent work
shows that cloud brokers who mediate the trading process between
the customers and the cloud providers can significantly reduce the
cost for the customers while helping the cloud providers to cloud broker
4. Cloud computing.
Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources (for example, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service-provider interaction.
5.Bulk Purchasing.
Bulk purchasing is when a consumer captures part of the benefits ofeconomy of scale for himself by doing with the retailer what the retailer does with the wholesaler: paying a lower price per unit in exchange for purchasing much larger quantities. This allows the consumer to satisfy more of his demands at a lower total cost by acquiring moreuser value dollar spent.
6.Concave Pricing.
Concave pricing depends on the firm'saverage cost, and on the customer value of the product in comparison to his or her perceived value of thecompetingproducts. Different concave pricing methods place varyingdegreeof emphasis onselection, estimation, andevaluation ofcosts,comparative analysis, andmarket situation
Scope:
Online task scheduling is required in many cases,because the cloud service provider or service broker may not haveinformation of all tasks in advance and has to make decision withinformation available so far for online job scheduling when any concave or piece-wise concavecost function is used for time is save.
Problem Statement:
Multiple customers may submit requests at randominstants with random workload that should be fulfilled before
specified deadline to a broker. We assume that the inter-arrivaltimesfor requests are arbitrary. We assume that the processing time for each is deterministic and known to the broker giventhe resource allocated to the customers. The broker is responsible forpurchasing computational resource from clouds, allocatingresource to and executing jobs, as well as meeting job deadlines.The deadlines specified by the customers are flexible. Different from cloud, where the customers directly submit job requeststo cloud service providers, brokers mediate the process by organizingthe job requests in a manner which benefits the most from the
volume discounts provided by the cloud provider. Both the cloudprovider and the customers benefit from this mediation. Individualcustomers can enjoy volume discounts which often require a largevolume of job requests. The cloud provider benefits from therevenue boosted by the brokerage. To ease analysis, we assumethat time is slotted, and jobs arrive at the beginning of a time slot.In any unit time slot, a job either is allocated with no resourceor uses allocated resource in the whole time slot, unless otherwisestated.
CONCLUSION:
Cloud is an emerging computing market where cloud providers,brokers, and users share, mediate, and consume computing resource. With the evolution of cloud computing, Pay-as-you-go pricing model has been diversified with volume discounts to stimulate the users’ adoption of cloud computing. This paper studies how a broker can schedule the jobs of users to leverage the pricing model with volume discounts so that the maximum cost saving can be achieved for its customers. We have analyzed the properties that an optimal solution should have and studied three special cases of the concave cost scheduling problem. We developed an online scheduling algorithm and derived its competitive ratio. Simulation results on a Google data trace have shown that the proposed online scheduling algorithm outperforms other conventional scheduling algorithms. Although continuous concave cost functions and piece-wise linear cost functions are used to conduct the evaluation, the properties proved and the online algorithm proposed apply to all piecewise concave cost and cost saving.