Cloud Pricing Models: A Survey and Position Paper.
Atul Gohad IBM India Software Lab, Bangalore India.
Nanjangud C. Narendra IBM India Software Lab, Bangalore India.
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Abstract—In recent years, cloud computing has emerged as a successful mode of delivery for Software as a Service (SaaS) offerings and more generically anything as a service (XaaS) offerings. Cloud computing is, an evolutionary paradigm shift in the ways computing platforms and services are made available to the consumers, and this has been made possible due to numerous technology enablers, along with changing business strategies. These changes have contributed significantly in achieving the market shift from differentiated to undifferentiated price models, thereby helping the market movements from monopolistic to perfect competition, and resulting in converting traditional enterprise class software tools and hardware platforms into a commodity. This paper, is a survey on pricing strategies and schemes employed in cloud offerings wherein we study various mechanisms currently being used. The literature survey encompasses market trends on cloud pricing with specific focus on emerging market scenario of India. Based on the need of providing flexible pricing, we discuss our position on a revenue framework wherein cloud pricing strategy is a function of periodic resource utilization analysis, and provide details of our revenue generation model that depends on cross over of different pricing schemes.
Keywords. Cloud Pricing Models, Cloud Marketplace, Cloud Services I. I NTRODUCTION Cloud computing often referred to as simply the cloud, is the delivery of on-demand computing resources - everything from applications to data centres - over the Internet and on a pay-for-use basis [1]. Cloud computing services • Software as a service (SaaS): Cloud based applications run on distant computers in the cloud that are owned and operated by others and that connect to user’s computers via the Internet and, usually, a web browser. • Platform as a service (PaaS) Provides a cloud-based environment with everything required to support the complete life cycle of building and delivering web based (cloud) applications - without the cost and complexity of buying and managing the underlying hardware, software, provisioning and hosting. • Infrastructure as a service (IaaS) Provides companies with computing resources including servers, networking, storage, and data center space on a pay-per-use basis. The various pricing model elements for SaaS offerings in cloud computing can be depicted as in Fig. 1. The price strucWe would like to thank Akshat Dixit for his help on prototype implementation.
Parathasarthy Ramachandran Department of Management Studies,IISc,Bangalore India.
[email protected]
ture includes scheduled discounts, packaging and licensing terms, and the components in value metric. The price details can be classified as list prices which are constant and deal prices which are customized [2]. As mentioned in [3], pricing strategies and a lack of clear value propositions are seen as the biggest inhibitors to cloud computing. One of the motivations for this work is our strong belief that there is a need for a revenue management system which can offer dynamic cross-over across pricing models, based on changes in resource utilization and tenancy requirements over time. For instance, a specific tenancy request T Ra could have been provisioned using the best suited reserved instances pricing policy, so as to meet tenancy SLAs during peak load. At the same time, assume a new tenancy requirement T Rb with higher priority of provisioning, and consuming the same set of resources is needed to be satisfied. Now, the cloud provider would like to have an option wherein firstly, a negotiated increased price for a spot instance would be offered to T Rb after freeing the currently provisioned resources of T Ra , during the time when T Ra does not have to satisfy peak loads, and secondly, switch back the resources to T Ra during the time of its peak load. Using such a model would help increase revenue generation of provider, since now it can satisfy two tenancy requests instead of one, using an optimized set of resource usage spread across time intervals of both tenancy requirements. Our paper is organized as follows, the details about pricing strategies and pricing models, including specific discussion for pricing models in emerging markets like India is discussed next. Section III focuses on recent industry trends and their effects on pricing models along with discussion on cloud hosting costs. Our proposed pricing model is presented in Section IV and the experimental results are provided in Section V.The paper concludes in Section VI with suggestions for future work. II. L ITERATURE S URVEY A. Pricing Strategies There are many pricing strategies such as RAM hours, CPU Capacity, Bandwidth (Inbound/Outbound Data Transfer), Storage Space, Software License Fee and Subscription-Based Pricing [4] which are of relevance to cloud computing domain. Optimal Pricing for Monopolist Authors of [5] examine which of three commonly-used pricing schemes flat fee pricing, pure usage-based pricing, and two-part tariff pricing
Fig. 1: Pricing Model Elements
is optimal for a monopolist providing information services. Their analysis shows that when the sum of the marginal costs and the monitoring costs is below a threshold value, flat fee pricing is the optimal scheme regardless of how large or how small the monitoring costs are as long as they are positive. When monitoring costs are zero, the two-part tariff becomes one of the optimal pricing schemes. Resource Pricing A fundamental problem in any federated system is the allocation of shared resources. Economic efficiency is a global measure and represents the total welfare for both buyers and sellers. More specifically, there are two factors that affect the economic efficiency i) average user welfare; and ii) number of successful requests for buyers, and number of allocated resources for sellers.To evaluate the impact of dynamic pricing, the authors of [6] compare the percentage of successful buyer requests, the percentage of allocated seller resources and the average buyer welfare with fixed pricing. Multi-service QoS based pricing schemes Authors of [5] have developed inter-organizational economic models for pricing cloud network services when several cloud providers coexist in a market, servicing a single application type. These models can drive optimal resource provisioning in cloud networks. The Nash Equilibrium price and QoS levels for each cloud provider drives optimal end-user demand in a given time period w.r.t. maximizing individual cloud providers profits under competition. They also developed an optimization framework for single-tiered and multi-tiered cloud networks to compute the optimal provisioned capacity once the equilibrium price and QoS levels for each cloud provider have been determined. However, they do not provide any models for situations wherein cloud providers are in simultaneous competition with other cloud providers on multiple application types. Incremental recreation strategy In [7] authors present an idea to use the minimum number of replicas while meeting the data reliability requirement. Due to the uncertainty of the data storage durations, it needs to decide how many replicas are sufficient to meet the reliability requirement. Initially, the minimum data replica number is bound to 1 by default, i.e. only the original data will be stored and no extra replicas will be made at the beginning of a data storage instance. When time goes by,more replicas need to be incrementally created at certain time points to maintain the reliability assurance. At the beginning of each data storage instance or when the latest replica creation time point reaches, a process maintained by
the storage system for calculating the replica creation time points is activated. This strategy is employed for cloud data centres, and the authors demonstrate that this strategy can reduce the data storage cost substantially, especially when the data are only stored for a short duration or have a lower reliability requirement. Optimal Service Pricing The authors of [8] propose a price-demand model designed for cloud cache in data management services and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The proposed solution allows on one hand, long term profit maximization, and, on the other, dynamic calibration to the actual behaviour of the cloud application, while the optimization process is in progress. The authors have provided this approach for improving optimization in the cloud data management service, but we could extend it as a cloud pricing strategy, based on optimization of correlations in collaborative cloud providers. B. Pricing Models Perpetual Pricing In this model, the cost to own an offering is calculated up-front and charged to the licensee in return to a perpetual (forever) right to use the offering. In e-commerce software offerings, there is sometimes a mandatory annual maintenance cost included to provide support on the offering. This model usually is suitable for enterprises with more capital. Renting Renting a resource involves paying a negotiated cost to have the resource over some time period,whether or not you use the resource [9]. Subscription based PricingThis is the most widely used pricing model for SaaS. This model allows the users to predict their periodic expenses of using the cloud applications. However, it lacks the accuracy of charging the users for what they actually have used [10]. Pay-as-you-go Pay-as-you-go involves metering usage and charging based on actual use, independently of the time period over which the usage occurs. Amazon rounds up their billing to the nearest server-hour or gigabyte-month, but the associated dollar amounts are small enough (pennies) to make it a true pay-as-you-go service [9]. Tiered Pricing Tiered pricing is the model adopted by Amazon’s cloud systems, where the cloud services are offered
in several tiers; each tier offers fixed computing specifications (i.e. memory allocation, CPU type and speed, etc.), and SLA at a specific price per unit time [10] . Reserved Instances Amazon’s reserved instances give consumers the option to make a low, one-time payment for each instance you want to reserve and in turn receive a significant discount on the hourly charge for that instance. There are three reserved Instance types (Light, Medium, and Heavy Utilization Reserved Instances) that enable consumers to balance the amount you pay upfront with your effective hourly price [11]. Reserved Instances can be purchased directly from Amazon for 1 or 3 year terms. Using the Reserved Instance Marketplace, you have the flexibility to purchase Reserved Instances from Amazon Reserved Instance Marketplace Sellers for terms ranging between 1 month to 36 months (depending on available selection). In either case, the one-time fee per instance is non-refundable. Light and Medium Utilization Reserved Instances also are billed by the instance-hour for the time that instances are in a running state; if consumers do not run the instance in an hour, there is zero usage charge. Partial instance-hours consumed are billed as full hours. Heavy Utilization Reserved Instances are billed for every hour during the entire Reserved Instance term (which means consumers are charged the hourly fee regardless of whether any usage has occurred during an hour). Spot Instances Spot Instances [11] enable consumers to bid for unused Amazon EC2 capacity. Instances are charged the Spot Price, which is set by Amazon EC2 and fluctuates periodically depending on the supply of and demand for Spot Instance capacity. To use Spot Instances, consumers place a Spot Instance request, specifying the instance type, the Availability Zone desired, the number of Spot Instances to be run, and the maximum price that consumer is willing to pay per instance hour. To determine how that maximum price compares to past Spot Prices, the Spot Price history is available via the Amazon EC2 API and the AWS Management Console. If consumer’s maximum price bid exceeds the current Spot Price, your request is fulfilled and the consumer’s instances will run until either consumer chooses to terminate them or the Spot Price increases above the current maximum price (whichever is sooner). On Demand Instances Amazon’s on-demand instances let consumers pay for compute capacity by the hour with no long-term commitments. This frees consumers from the costs and complexities of planning, purchasing, and maintaining hardware and transforms what are commonly large fixed costs into much smaller variable costs [11]. Per-Unit PricingPer-Unit Pricing is normally applied to data transfers or memory usage. Main-memory allocation, for example is used by GoGrid Cloud offering, where they denoted RAM/hour as the usage unit for their system. This model is, arguably more flexible than the tiered pricing, as it allows the users to customize the main memory allocation of their system based on their specific applications needs [10]. Variable Pricing models Herein the pricing is based on the level of demand of a particular resource type, daytime versus
night-time, weekdays versus weekends, spot prices, and so forth. The different cluster nodes can change dynamically their locations, from one cloud provider to another one, in order to reduce the overall infrastructure cost [12]. Cloud Option Pricing Model The authors of [13], have proposed an approach that utilizes financial option theory to simultaneously mitigate risk and minimize cost for cloud users. One of the basic underlying principles in mathematical finance is the Efficient Market Hypothesis which states that stock prices already incorporate all available information. Otherwise predictable price movements would generate possibilities for speculators to gain risk free profits. In efficient markets, such speculators always exist and they always take advantage of the presented opportunities, as a result in the end all such opportunities have been taken and all available information has been incorporated into the current market price. The basic underlying idea of any option pricing scheme is as that to value an option, one must form a self-financing hedging strategy that replicates the pay-off of the option. A call option gives the holder the right to buy the underlying asset by a certain date (called expiration date, exercise date or maturity) for a certain price (called exercise price or strike price). American options can be exercised at any time up to the expiration date, whereas a European option can only be exercised on the final expiration date. American options are more practical and widely used, but European options are easier to analyze mathematically. An option gives the holder a right to exercise the option, but it is not an obligation. This is different from forwards and futures, where the holder is obligated to buy or sell the underlying asset. However, forwards and futures are free, where as an investor must pay to purchase an option contract. Agreements based billing Agreements based billing (ABB) allows businesses to adjust rates, promotions, discounts, accelerators, multi-axis pricing and more. These modifications are easy to incorporate into existing processes and increase opportunities to grow the business. It also can automate business processes and business models to address rapidly changing or complex business strategies. Companies can quickly model any new business requirement, thereby reducing the time and cost required to go to market. Monetization options support the internal management needs of enterprises with rapid modelling allocation of fees, modification of account hierarchies and automation of repetitive processes. ABB, therefore, enhances payment accuracy by reducing the chances for error, which increases customer satisfaction. Easily Integrated ABB is an agile solution designed for rapid implementation. Once agreements-based billing is in place, it can easily integrate with a company’s existing business applications, streamline the end-to-end order-to-cash process and adapt to changes in a company’s business model [14]. C. Preferred pricing models - India Scenario The essence of the cloud IaaS model is a pay-as-yougo financial model. As seen from the results in Fig. 2, of survey [15] carried out by Ernst & Young, the high percentage
Fig. 2: IaaS - Preferred Pricing Model - India
of respondents have their preference for annual contract-based pricing indicates lack of clarity on the cloud’s financial model. Further analysis indicates that the majority of respondents opting for an annual contract-based model are the large enterprises, while the majority of SMB segment prefers resourcebased usage model. The survey concludes that at this stage, a single pricing model is unlikely to satisfy all potential customers in the market. Vendors need to have pricing structures that are easily understood, transparent and offer substantial benefits in terms of cost savings. Options for alternative pricing models can be as follows 1) A true pay-as-you-use model based on the use of resources such as per hour usage or CPU cycles consumed will be attractive to the SMB segment. 2) More flexible models integrating the features of usageand contract-based pricing can be developed, where server instances can be charged on a daily or monthly basis instead of hourly. 3) Reserved instances with discounts on hourly rates can be more cost-effective for larger enterprises with visibility on demand. Reserved instances are likely to help large enterprises better estimate and plan their cloud IaaS needs. III. I NDUSTRY T RENDS This section describes the current trends that affect cloud hosting and pricing models. The trends in traditional software development such as offering rapid changes in product and application features, increasing usage of web and mobile, providing a richer and self service user experience have been around for a while but are still significant for SaaS offerings [2]. A. Effect of trends on pricing models. Commoditization
Managing price certainty separately from physical supplier selection is standard best practice in most commodities markets, and we are starting to see initiatives in the cloud industry that are finally following this best practice. A financially settled exchange that relies on a cloud pricing index that is not based on a single cloud provider should appear soon, and that would support implementation of commodities market best practice in cloud computing [16]. Amazon is offering to trade cloud computing in a commodity-like way [16]. Amazon’s reserved instance marketplace allows AWS customers who no longer require ondemand instances of a particular type and in a particular availability zone to offer reserved instances for sale to other Amazon EC2 cloud users. This much-requested feature now means that paying AWS upfront for heavily discounted usage of on-demand instances is not quite so daunting, as there is now a way of selling reserved instances. Business Transparency Transparency is an unavoidable trend across industries. The pricing models such as agreement based billing [14] provide a system to simplify the calculation, auditing and understanding of state of agreements across many interrelated relationship models. Trends in charging models In an IBM Academy of Technology survey of 110 cloud implementations across industries [3], flexible pricing or charging models ranked lower compared to the typical market hype in cloud publications. The analysis of survey feedback on the importance of future payas-you-go capabilities, indicates a difference between private cloud implementations versus public cloud implementations. Public clouds appear to have already a good proportion of pay-as-you-go charging models compared to private clouds, but this will not always be the case. Survey participants strongly believe that pay-as-you-go models will also have to be implemented in private cloud settings between the IT organization and lines of business. This finding suggests that we will see an interesting challenge in integrating public and new private cloud charging schemes in hybrid cloud scenarios. Another clear survey trend is based on the observation that we will see flexible charging concepts to address sustainable value-for-money relationships between cloud providers and consumers. Predictive Revenue Analytics Analysis of the information between each transaction using the pricing models can offer intuitive reporting. Companies can build upon those metrics to understand which cloud offerings and models are most profitable, at the same time providing predictive analytics to estimate future revenue based on real-time trends. Adaptability As virtual commerce and mobile payments continue to move into the commercial sector, these markets need a monetization system that can support profitability analysis, dynamic pricing and large volumes of transactions [14]. New Revenue models Companies can aim even higher than pricing by thinking bigger. For example, when you offer software as a service via the cloud, it gives software developers and other third parties the opportunity to create market-specific products based on
your offering, as well as plug-ins, extensions and even new features of your core product. One company that has done this with extraordinary success is Apple, which has created a ”cloud market-space” for delivery of mobile software. Apple has not only profited greatly from direct sales, but also spawned a huge industry of independent software developers writing successful applications for the iPhone, every sale of which earns Apple a percentage [17]. B. Discussion on cloud hosting costs In this section, we study the various factors affecting the cloud providers in terms of cost for hosting data centres, cost of managing cloud resources and data communication costs. Data center hosting costs The cost structure of an average data center is illustrated in Fig. ??. As a result of underutilization, the overhead cost of under-utilized infrastructure tends to be a significant cost driver. Leased resource costs The cost of leased resources from cloud providers are mainly derived from three sources: computing resources, storage, and network data transfer. These costs have been analyzed by different authors in the context of single-cloud based test beds running high-performance computing applications.The authors of [12] analyze the deployment of a computing cluster in a multi-cloud environment, using resources from local data center, and resource from three different cloud sites: Amazon EC2 Europe, Amazon EC USA, and ElasticHosts. These providers can offer different pricing schemes for computing resources, e.g. on-demand instances, reserved instances, or spot instances in Amazon EC2; and monthly subscription instances, or hourly burst instances in ElasticHosts. However, they use a single pricing method, based on a pay per compute capacity used scheme, which is available in most of cloud providers (on-demand instances in Amazon EC2, and hourly burst instances in ElasticHosts). This payper-use pricing scheme is the most flexible one, since a user can start or stop compute instances dynamically as and when needed, with no long-term commitments, that are charged by the cloud provider at a given price per hour of use. Dynamic resource provisioning for multi cloud environments When deploying a service in multi-cloud environment, the use of efficient scheduling strategies is essential to make an optimal resource selection among different clouds, according to some user optimization criteria (e.g., infrastructure cost, service performance, etc.) and, optionally, some user constrains (e.g. maximum budget, minimum service performance, location constraints, load balancing, etc.). Furthermore, in a real multi-cloud deployment, cloud providers can exhibit changing conditions during the service life span, such as variable instance prices (time of the day, new agreements, spot prices,...), new instance offers, dynamic resource availability, arrival of new providers in the cloud market, withdraw of an existing provider, etc. In this context, the scheduler must follow some dynamic provision strategy to adapt the cloud resource selection to the variable cloud conditions. [12]. Data communication cost in hybrid clouds The authors of [18] present a model for hybrid cloud costs, encompassing the
Fig. 3: System architecture
costs of computing capacity and data communication capacity. In the proposed model, the costs are modelled as a function of the threshold demand for computing capacity, which is provided with the private cloud. The demand up to this threshold value is served with the private cloud infrastructure, which is assumed to be acquired beforehand and reserved for the purposes of service provisioning; whenever the demand exceeds the threshold value, the exceeding portion of the demand is served with the public cloud infrastructure, which is used without a prior reservation (on-demand) and charged based on the actual usage. When estimating the costs of a capacity, quantity discounting is taken into account. Using the model, the cost-optimal threshold for dividing the private and the public cloud computing capacity can be identified. Finding such optimal division has been numerically exemplified for the case of a demand uniformly distributed between zero and maximum levels. IV. P ROPOSED P RICING M ODEL A. System Architecture and Models. The architecture of the proposed system is depicted in Fig. 3. The cloud depicts the set of all available resources in the cloud provider’s environment. These resources could be of varying types, each with multiple instances. The repository notation of Tenancy Requirements Model (TRM) depicts multiple tenancy requirement models. Each of these models provides details on specific tenancy such as (a) the state (Created, Active, Paused) of the tenant, (b) its functional and non-functional behaviour characteristics, (c) constraints on inter-tenancy and intra-tenancy, and (d) tenancy attributes. The repository notation of Catalogued Tenancy Requests (CTR) depicts a collection of past requests made by each of the tenants. This repository is typically helpful in determining the resource needs for a specific tenancy in a given time period, based on historical data. The repository notation of Resource Usage Model (RUM) consists of details of resource parameters such as probability of resource instance being used, cost of resource etc, that are of critical importance for determining the composition of resources used to satisfy the tenancy requirements. This data is updated periodically based on the output from Resource Utilization Monitor.
Fig. 4: Flow control of proposed system.
The pricing cross over point, and resource available during crossover are determined and cost is customized by the Pricing Crossover Identifier, Customizer component. This component is primarily responsible for executing the algorithms to determine if a new tenancy request can be satisfied and if so at what price value, based on changes in resource usage and tenancy requirements..
B. Flow control. The high level flow control of our proposed system is shown in Fig. 4. First, the functional and non-functional requirements are captured as provided by the business users. These requirements are translated into a Tenancy Requirement Model (TRM). Second the TRM’s are translated into resource requirements based on existing capacity planning tools [19]. Third, from the pool of blocked reserved instances by this tenancy requester, a required set of resources is provisioned. It
Fig. 5: Resource Usage.
Fig. 6: Increase in revenue.
may be possible that there is a short fall of reserved instances for this tenancy, in which case a check is made to find out additional resources as spot instances. Based on the highest bidder for the matching spot instance it is provisioned to the tenancy request. The set of provisioned resources are monitored for usage patterns and are marked as available for spot instances, if not being used. At the end of tenancy period, resources are released back to the pool, ready to be used for next tenancy request. V. E XPERIMENTAL R ESULTS In our experimentation setup, we had sample applications hosted on servers with 3.1 GHz processor speed. These
machines had varying number of processors per resource instances, along with Storage units attached to each of these machines. The tenancy application requests of T1, T2, T3 and T4 had varying number of hits causing fluctuations in the average number of CPU and Storage units being used. The sampling of data was performed to arrive at a monthly average of resource usage, and is depicted in Fig. 5. The increase in revenue generation after usage of cross over pricing model is depicted in Fig. 6. For the ease of calculations, the revenue value associated with each of the CPU and Storage Units is assumed to be 1000 $, and a constant revenue of 30K$ is generated by ensuring all the resources have been contracted
to subscribers as reserved instances. By leasing out unused resources through spot instances a maximum increase of about 23% could be seen, with respect to the revenue generated from reserved instance during months of April and May. VI. C ONCLUSION AND F UTURE W ORKS In this paper we have attempted to arrive at a pricing cross over model which can be used for increasing revenues of providers hosting multi-tenant cloud environments. We surveyed the various pricing techniques, and determine that the current pricing strategies do not offer such a flexibility. We presented our approach and framework for reveune management with specifics of our pricing cross over model. Our model based on mapping of functional and non-functional tenancy requirements with appropriate resources and their parameters, along with our experimentation data of pricing schemes such as reserved instance and spot instance for cloud resources. We demonstrated our experiments via a proof-ofconcept prototype, using a realistic example of cloud resources and applications. In future work we will test our approach on larger case studies based on practical data available from cloud data centres. R EFERENCES [1] IBM, “Computing as a service over the internet.” [Online]. Available: http://www.ibm.com/cloud-computing/us/en/ what-is-cloud-computing.html [2] J. Geisman, “Saas pricing for the cloud.” Sponsored by Rackspace Hosting., 2011. [Online]. Available: http://softwarepricing. com/readingroom/Content/Rackspace-SaaS-Pricing-for-the-Cloud.pdf [3] IBM, “Cloud computing insights from 110 implementation projects.” IBM Academy of Technology Survey, 2010. [4] B. P. Rimal and E. Choi, “A service-oriented taxonomical spectrum, cloudy challenges and opportunities of cloud computing,” Int. J. Commun. Syst., vol. 25, no. 6, pp. 796–819, Jun. 2012. [Online]. Available: http://dx.doi.org/10.1002/dac.1279 [5] P. H. Ranjan Pal and P. Hui, “Economic models for cloud service markets.” in ICDCN, 2012, pp. 382–396. [6] M. Mihailescu and Y. M. Teo, “On economic and computational-efficient resource pricing in large distributed systems,” in Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, may 2010, pp. 838 –843.
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