Global Journal of Flexible Systems Management DOI 10.1007/s40171-015-0093-1
ORIGINAL ARTICLE
Flexible Pricing Models for Cloud Computing Based on Group Decision Making Under Consensus Arpan Kumar Kar • Atanu Rakshit
Received: 11 September 2014 / Accepted: 12 January 2015 Global Institute of Flexible Systems Management 2015
Abstract Today, cloud computing is transforming the consumption of IT/ITeS. Numerous vendors are offering services where computing, storage and application resources, can be dynamically provisioned on a pay per use basis, purely based on the user’s need. However, the demands and requirements of different users vary significantly. In order to maximize the revenue, a flexible pricing approach is required, which can address these diverse requirements systematically. These systemic approaches need to estimate the potential value of such services to specific users for a specific context. The tradeoffs from potential value drivers also need to be accounted for while prioritizing the value drivers. In these lines, the current study proposes a flexible pricing approach for Infrastructure as a Service (IaaS), one of the important delivery models, based on its perceived value to multiple key stakeholders. The proposed approach prioritizes and aggregates the key features of IaaS for the migration to cloud, from multiple key users’ perspective by integrating fuzzy set theory and analytic hierarchy process for group decision making under consensus. Subsequently, the prioritization is mapped with a utility function to estimate the trade-offs from each value driver. The performance of the proposed approach has also been compared with that of another flexible pricing model through a case study.
A. K. Kar (&) Information Systems Area, DMS, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India e-mail:
[email protected] A. Rakshit Information Technology & Systems Area, Indian Institute of Management Rohtak, Rohtak 124001, Haryana, India
Keywords Analytic hierarchy process Cloud computing Consensus models Decision support systems Group decision making Information technology pricing
Introduction Cloud computing is a parallel and distributed computing system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as unified computing resources based on servicelevel agreements (SLA) between the service provider and consumers (Buyya et al. 2009). In recent times, cloud computing has revolutionized how technology based services are being provided to the consumer (Marston et al. 2011; Dhar 2012). It implies a service oriented architecture, reduced information technology overhead for the enduser, great flexibility, reduced total cost of ownership, and on-demand (Vouk 2008; Jula et al. 2014). Indeed factors like deployment models, scalability, security, strategies for pricing, interoperability and maturity of services affect the adoption of cloud based services (Lombardi and Di Pietro 2011; Nacer and Aissani 2014; Hsu et al. 2014; Wang and He 2014). In fact, adoption of business processes on global cloud-based platforms enhance the agility of the enterprise (Sushil 2012a; Gromoff et al. 2012). These cloud based service models are offering computing, data, platform, software and business process ‘‘as a service’’ (Zhang et al. 2010; Tsai and Hung 2014). Cloud based technologies (Weiss 2007; Stieninger and Nedbal 2014) aim to power the next-generation data centers as the platform for dynamic and flexible application provisioning. However, it is important to note that, despite its obvious benefits, pricing of cloud based offerings remains a
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challenge and is yet a major decision factor in the adoption of the services (Tsai and Hung 2014; Hsu et al. 2014). Flexible pricing models have been proposed in literature to address the customer’s concerns surrounding the trade-off between value and cost of information technology enabled services (Bichler et al. 2002). In fact, effective use of knowledge of key stake-holders among the customers improves the service experience encounters and increases customer satisfaction (Gibbert et al. 2002). This is precisely the motivation for the current work, so that a systematic and flexible approach may be developed for the pricing of cloud based offerings based on the value perceptions of the key users under consensus achievement. The present study primarily focuses on the Infrastructure as a Service (IaaS) market and critically reviews the pricing strategies of cloud computing service providers. Based on a literature review and interviews conducted with experts, six user service dimensions were identified. Fuzzy set theory and analytic hierarchy process (AHP) have been integrated for the prioritization and aggregation of the users’ preferences for these six dimensions. Subsequently, theories of consensus achievement within the group decision making have been adopted to estimate the group decision vector. Subsequently, a sigmoid curve has been used to address the actual trade-off among the priorities for estimating the overall perceived value, based on these evaluation dimensions so as to estimate the final perceived value of the offering to a specific client, based on their needs. Finally, the method has been illustrated with a case bearing close resemblance to an actual implementation. How the proposed hybrid methodology contributes for both the academia and practitioners, has been discussed in the end.
Review of Literature Cloud Computing and its Pricing Strategies Computing as a utility has changed the basic concepts of IT support and how it is provisioned for (Jula et al. 2014). Further, the related layers like platform, applications and other services are also provided pay-as-you-go approach. This has given IT user company a dramatic change as well as the options in their business model. Cloud Computing can be delivered through different delivery models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS), Data as a Service (DaaS), application Platform as a Service (aPaaS) and many other delivery models (Grossman 2009; Furht 2010; Tsai and Hung 2014). Infrastructure as a Service (IaaS) provisions a virtual server what may be determined to be a utility computing model, to meet customer requirement as per SLA. IaaS
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offers numerous organizational benefits like increased scalability, reduced overheads, optimal utility, improved throughput, quality of service, reduced latency, specialized environment, cost effectiveness, and simplified interface (Manvi and Shyam 2014). In this study, the focus is on providing a decision support approach for pricing IaaS, which has become one of the more common services for the cloud provider (Repschlaeger et al. 2012). Pricing cloud computing services is a major focused area of research in recent times (Stieninger and Nedbal 2014). Cost to the end user is in fact one of the major factors that drives customer loyalty in such services (Sagar et al. 2013) and is also an evaluation parameter for IT service quality (Lepmets et al. 2014). Flexible usage based pricing schematics are popular for cloud based services (Grossman 2009). In fact, it has been illustrated that user welfare and the percentage of successful requests is increased by using dynamic pricing in cloud based services (Mihailescu and Teo 2010). Further, service models need to take into consideration the total cost of ownership for such offerings (Walterbusch et al. 2013). Many cloud service providers have a pay-as-you-go pricing scheme (e.g. Amazon Web Services, Windows Azure, Google AppEngine) and a need is there to analyze these flexible pricing schemes (Wang et al. 2010; Kantere et al. 2011; Sharma et al. 2012). Sometimes a provider sets a static or infrequently updated per-unit price, and users pay for only what they use. Along with the pay-as-you-go offer, there are two additional pricing schemes widely adopted in cloud markets: subscription (Weinhardt et al. 2009) and spot market (Weiss 2007). In subscription based pricing, a user pays a one-time subscription fee to reserve one unit of resource for a certain period of time. The spot market based pricing is an auction-like mechanism where users periodically submit bids to the provider, who in turn posts a series of spot prices. In fact, some providers use multiple pricing schemes simultaneously for IaaS. In fact, it has been illustrated that resource price can gradually converge to an equilibrium state without other competitors’ bidding information (Teng and Magoules 2010). Further, perceived value based pricing has also been proposed for cloud based services (Kar and Rakshit 2014). This highlights the need of development of consensus based pricing mechanisms in cloud computing based on the perceived value of the end user. In this study, an application of the AHP methodology has been highlighted to price IaaS based on the perceived value of the service, under the achievement of consensus. Pricing of IT and IT Enabled Services IT vendors have mostly focused on cost based pricing approaches using metrics like function point count, code size, development effort, development time and
Global Journal of Flexible Systems Management
development complexity (Pasura and Ryals 2005). However, although cost based pricing is often used for information goods, it is limited in addressing the customer’s price sensitivity and potential competitor action (Brennan et al. 2007). Pricing literature (Goldman et al. 1995) argue that all effective pricing strategies should be mapped to the customer’s value perception about the offering. However, linking perceived value to pricing strategies is a challenge, for intangible goods (like IT). Also, the marginal cost of information goods is often negligible that cost based pricing is often viewed as being a viable strategy (Fishburn et al. 1997). Literature (Harmon et al. 2005; Wirtsch-Ing et al. 2009; Harmon et al. 2009) highlights different IT pricing strategies and the major issues faced by technology developers, like cost based approaches, flat fee pricing, nonlinear usage-based pricing, two-part tariff pricing and many other strategies. However, traditional non-linear pricing theory (Maskin and Riley 1984; Wilson 1993; Armstrong 1996) argues that the optimal pricing strategy for a seller’s market is most rewarding when it is based on usage. For emergent technologies like cloud computing, the seller is more likely to be operating in a market with low direct competition but wider and costlier range of substitutes. When such an offering is being introduced in the market, the provider may adopt a single pricing scheme to simplify management (Curle 1998; Wilson 1993). However, it was established after comparing cost based pricing against usage-based pricing and established that when users are characterized by heterogeneous consumption levels, incorporating usage in pricing generates greater revenue for the service provider. Therefore, such services are often defined through data driven segmentation of customers, differentiation of service offerings and flexible pricing schemes (Clemons and Weber 1994). This is why a pricing strategy for an emergent technology like cloud computing needs to be mapped with the perceived value from usage, to generate greater revenue (Wu and Banker 2010). This was the point of emergence of flexible pricing models for such technology facilitated services and technology solution. Flexible pricing includes both differential pricing for different buyers based on expected valuations (often referred to value based pricing), and dynamic-pricing mechanisms like auctions, where prices are decided through competitive bidding (Bichler et al. 2002). The thoughts of flexible pricing models are actually an extension of greater customer orientation and participation in the business decisions, thereby, facilitating greater openness to the customer’s needs, deeper understanding of the variability in needs, greater relationship orientation of the organization, improved agility to adopting to changing needs and improved participation within a network of systems (Sushil 1997; Wadhwa and Rao 2002; Sharma and
Gupta 2004; Gorod et al. 2008). In fact, greater focus in such flexible strategies are on customer’s requirements rather than on the core offering for innovative strategy formulation (Sushil 2012a, c; Malaviya and Wadhwa 2005). In fact, it also makes financial sense for the service provider to invest in flexible systems even when demand patterns are perfectly positively correlated, despite the potential pooling of risk (Bish and Wang 2004). Further, the customer perceives the cost of consuming the service in such a case being directly correlated to the perceived value of the service (Clemons and Weber 1994). In line with these propositions, studies (Harmon and Laird 1997; Hinterhuber 2004; Kar and Pani 2011; Kar and Rakshit 2014) have highlighted systematic approaches for pricing based on perceived value for the end users, depending on usage, whereby prioritizations have been used to estimate tradeoffs. However, these studies did not focus on the subjectivity in prioritization of different perspectives and the achievement of consensus through a systematic method, which are important dimensions of flexible strategic initiatives and group decision making (Sushil 1994; Kar and Pani 2014; Kar 2015). Exploration of Decision Support Literature for Group Decision Making In recent times, decision support theories of group decision making have gathered a lot of prominence for multi-criteria decision making problems. For complex decision making problem with multiple alternatives, arriving at an outcome is very complex. Therefore, systematic decision support methods need to be adopted to improve and document the improvement of outcome with proper justification for every consideration, which may have been accounted for in the process (Korhonen et al. 1992; Stewart 1992). Again for complex decision making problems, collective intelligence of a group of decision makers in consensus often is more effective than the same decision makers working in isolation (Dyer and Forman, 1992; Kerr and Tindale 2004). In fact, group decision making under consensus has greater reliability, consistency and regularity than decision makers working in isolation for even the same prioritization problem (Bryson 1996; Herrera-Viedma et al. 2007; Kar and Pani 2014). Therefore, group decision support systems provide a better approach to solve complex decision making problems involving trade-offs between multiple and often divergent objectives. However, there are different approaches for providing decision support for group decision making like using fuzzy set theory (Tapia Garcia et al., 2012); Total Interpretive Structural Modeling (Sushil 2005, 2012b), VIKOR (Wan et al. 2013), TOPSIS (Shih et al. 2007; Chen and Lee 2010; Tan 2011; Yue 2011, 2012), Analytic Network
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Process (Levy and Taji 2007), Linguistic models (Pe´rez et al. 2011; Chen et al. 2012), Outranking models (Tavares 2012), Delphi method (Okoli and Pawlowski 2004), Mathematical Programming (Iz 1992; Kar 2014), Neural Networks (Kar 2015) and Multi-Attribute Utility Theories (Wei 2010; Pang and Liang 2012), to name a few of the more prominent approaches. Another such major method for group decision making under consensus is the AHP (Saaty 1980), which has been used in this study for providing decision support to the problem. The AHP was developed for use in multi-hierarchical, multi-criteria decision making problems (Saaty 1980, 1994). It decomposes the problem into a hierarchy of more easily comprehended sub-problems, each of which can be analyzed independently through comparative independent judgments. AHP has been adopted for this study due to multiple reasons. The problem under exploration has fewer number of explicitly defined evaluation criteria but they are having contrasting objectives and a mix of qualitative and quantitative dimensions. Therefore, the relative impact of these evaluation criteria on the overall outcome is not known or structured objectively. So to address this problem, the tacit knowledge extraction and problem re-structuring is required from the experience of the core decision makers (Korhonen et al. 1992). This can be achieved by the prioritization of judgments using AHP. Subsequently AHP has appropriate measures for estimating consistency of priorities of expert decision makers (Saaty 1980; Aguaron et al. 2003; Aguaro´n and Moreno-Jime´nez 2003; Escobar et al. 2004). There are also well defined approaches to improve the consistency of priorities systematically (Finan and Hurley 1997; Cao et al. 2008). Further, AHP provides extensive theories for the aggregation of group preferences when the priorities of multiple users need to be combined (Bolloju 2001; Condon et al. 2003; Escobar and Moreno-Jime´nez 2007). Lastly, AHP provides robust theories for consensus building within groups (Bryson 1996; Moreno-Jime´nez et al. 2008; Dong et al. 2010; Wu and Xu 2012) and consensus achievement is an extremely important dimension of success in group decision making. Further, fuzzy set theory has been integrated with the AHP theories to accommodate the subjectivity in the prioritization process while estimating trade-offs among the different features. The adoption of fuzzy AHP is in line with the need of forming a bridge between hard and soft methods through which consensus may be achieved, thereby providing flexibility in the process while accommodating the needs of different key stake-holders (Sushil 1994). However, while the earlier studies using AHP were conducted using crisp AHP theory, the recent studies have started integrating AHP with fuzzy set theory to accommodate the subjectivity in the human decision making process in complex problems (Zahedi 1986; Ho 2008). Fuzzy set
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theory (Zadeh 1965, 1978; Zimmermann 2001, 2010) has increasingly found its application in complex problems where the subjectivity in the decision making process impacts the possibility of choices and thus affects the outcome. It has been widely integrated to AHP (Xu 2000; Ruoning and Xiaoyan 1992; Mikhailov 2000; Wang et al. 2006) and has been applied in different business domains in applications involving prioritization of multiple evaluation criteria (Kar 2014, Kar and Pani 2014). In this study, the integrated approach has been adopted using fuzzy set theory and AHP for the prioritization of the dimensions for the migration to Cloud, by extending previous literature (Kar and Pani 2011; Kar and Rakshit 2014) by using the Geometric Mean Method for the prioritization and aggregation. Subsequently, consensus in group decision making is developed using the Geometric Ordinal Consensus Index (GOCI) improvement approach (Dong et al. 2010).
Contribution While many studies have argued that perceived value should be considered while pricing any product, few studies have attempted to operationalize this concept while pricing emergent IT offerings with multiple dimensions of value. In fact, there has been no systematic approach to provide decision support to the pricing problem for cloud computing. Within cloud computing, the current study focuses on the pricing of IaaS from a value based approach, from the perspective of multiple users but for the same context. The current study follows the line of existing literature (Kar and Pani 2011; Kar and Rakshit 2014) and extends the same by incorporating AHP theory where the prioritization has been done with geometric mean method. The earlier work focused on prioritization based on the eigen vector method. However, for group decision making, the geometric mean method is more suitable for prioritization and aggregation of preferences since it maintains the reciprocal properties and extreme estimates in aggregated hierarchies better. The proposed method also uses fuzzy set theory while estimating subjective preferences thereby accommodating the possible dilemma of multiple decision makers, while estimating trade-offs. Further, the GOCI improvement approach (Dong et al. 2010) for the achievement of consensus have also been used, thereby extending the methodologies highlighted by the previous two studies. It is important to note that consensus achievement is an important dimension in group decision making. Then, a sigmoid utility curve has been used to manage the trade-offs between different dimensions based on its priority to estimate the total perceived value of the IaaS. These approaches have been illustrated through a hypothetical case study, which has a very strong resemblance to an actual business problem of pricing IaaS for a specific engagement.
Global Journal of Flexible Systems Management
This proposed hybrid methodology would also address the need of flexibility literature in pricing schemas, while addressing the requirements surrounding greater customer participation and orientation in the business strategy formulation thereby facilitating greater openness to the customer’s uniqueness of needs, deeper understanding of the variation in needs, improved accommodation of the fluctuation in needs and therefore greater relationship orientation of the organization (Sushil 1997; Bichler et al. 2002; Zhang et al. 2010). Further, the hybrid methodology also addresses the need of literature focusing on the systematic approaches to accommodate the diversity in customer’s requirements rather than on the core offering while pricing such services (Sushil 2012c; Wu and Banker 2010).
Conceptual Methodology The conceptual framework of cloud computing (NIST Cloud Framework) provides the virtual centralize processing environment in a distributed computing architecture. This is the key differentiator. The virtualization layer helps to create virtual computer of specific requirement like—performance consideration, storage consideration, cost consideration and many more. Resource optimization algorithms are extensively used to define required resources fulfilling all criteria. Even one can use the concepts of dynamic bidding of resources in such scenarios. The cloud computing environment can create fault-tolerance virtual computer on the fly to provide reliability of the service. Hence, it is very easy to support business critical applications and services. Further, information security and privacy is one of the most critical aspects for any service or solution. The types of security solution to be provided for different services and solutions and also for different users are of specific need. Cloud computing environment can provide different levels of security solutions to same or different services as per user need. These criteria are of immense importance for any service or solution to succeed. These are found to be varying widely from consumers to consumers and also from services to services. The cloud environment should have the flexibility in defining and configuring these criteria through service level agreement (SLA). Hence, the potential and capabilities of these criteria or dimensions (C1…C6) as specified below are of immense importance from the perspective of whether to migrate or not to cloud computing. In this study, the users’ priorities have been captured using an integrated decision support approach. The criteria or dimensions which were prioritized for the same context (i.e. evaluation of IaaS for migration) were derived from literature (Clarke 2010; Marston et al. 2011; Repschlaeger et al. 2012), after checking their contextual suitability
through focused interviews with five cloud computing consultants with over 10 years of expertise through a Delphi study which required 2 iterations for the achievement of consensus. (C1)
(C2)
(C3)
(C4)
(C5)
(C6)
Flexibility The ability to respond quickly to changing requirements, in terms of scaling. Example, if today, a lesser number of databases are required for operations, based on need, how quickly the number of data bases may be scaled up, as a service. Costs The capital expenditure and working expenditure, like acquisition and maintenance costs for servers, licenses and other hardware and software. While most of the software incur license fees which are typically, working expenditure, hardware provisioning may often be capital expenditure. Scope and performance Factors include the degree of fulfillment of specific requirements, knowledge about the service and performance quality. This pertains to the adherence to the SLAs which may also be flexible to an extent. However, adherence to the specified requirements is an indicator of performance and scope. IT security and compliance Factors like government, industry and firm specific needs in the areas of security, compliance and privacy are covered. Data centric privacy, security and compliance is a major example for this dimension for data centers which may be providing this service on or off premise. Reliability and trustworthiness Factors like service availability and fulfillment of the Service Level Agreements. This focuses on the minimization of service downtime during the periods when the service is expected from the provider. Service and cloud management Factors like offered support and functions for controlling, monitoring and individualization of the web interface. This focuses on the degree of customization which can be made, to address the requirements of the individual client, like creating a personalized custom built user interface for the access.
The prioritization of these dimensions is addressed through an integrated approach. In this approach, first the linguistic judgments of users are captured and mapped to quantifiable fuzzy judgments. Subsequently, these fuzzy linguistic judgments are converted to crisp priorities using AHP theory. These crisp priorities have been further combined using geometric mean method for the aggregation of priorities for estimating the trade-offs for the different dimensions. Let U = (u1,…,un) be the set of n users having a relative importance of wi such that w = (w1,…,wn) is the weight P vector of the individual users and wi = 1. These are the
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Global Journal of Flexible Systems Management Table 1 Scale for the conversion of linguistic preferences Linguistic judgment
Judgment values in fuzzy set (m) ~
Equal importance
1~ 3~ 5~
{(1, 0.25), (1, 0.50), (3, 0.25)}
7~ 9~
{(5, 0.25), (7, 0.50), (9, 0.25)}
Moderate importance Strong importance Very strong importance Extreme importance
{(1,0.25), (3,0.50), (5,0.25)} {(3, 0.25) (5, 0.50) (7, 0.25)} {(7, 0.25), (9, 0.50), (9, 0.25)}
I a~i
a~j ¼
I ai;1
I I aj;1 ; ai;2 aj;2 ; ai;3 aj;3 ð1Þ
The individual priorities may be obtained by solving the following system: min
k X k X
ðln a~i;j ðln p~i ln p~j Þ2 Þ s:t: a~i;j 0; a~i;j a~i;j
i¼1 j [ i
¼ 1; p~i 0; p~i ¼ 1 users who are prioritizing the important dimensions for migrating to Cloud. Comparative fuzzy judgments A = (aij)k9k would be coded as illustrated in Table 1. A triangular fuzzy function has been used for coding the judgments since there is equal probability of the response of the next level as is to the response of the previous level, when a comparative judgment is made by Cloud user (Figs. 1, 2). The simple pairwise comparison approach (Buckley 1985) for fuzzy set operations has been used for the fuzzy sets a~i ¼ ai;1 ; ai;2 ; ai;3 and a~j ¼ ðaj;1H; aj;2 ; aj;3 Þ as illustrated with the hypothetical operator :
ð2Þ The individual priority vector is derived by Crawford and Williams (1985) as shown: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Qk 1=k ~ a i;j j¼1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi p~i ¼ P ð3Þ Qk n 1=k ~ a i;j i¼1 j¼1 where p~i is the priority of the decision criteria i such that P~i ¼ fp~1 ; p~2 ; . . .; p~7 g for user i. In the subsequent step, consistency of these priorities needs to be evaluated, for which the geometric consistency index (GCI) is used (Aguaro´n and Moreno-Jime´nez 2003). GCI ðAdi Þ ¼
2 ðk 1Þðk 2Þ k X log j~ ai;j j ðlog j~ pi j log j~ pj jÞ2
ð4Þ
j[i
Fig. 1 Triangular fuzzy function for coding judgments
Fig. 2 Sigmoid functions for mapping preferences (Adapted from Wikipedia). (Color figure online)
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GCI ðAdi Þ GCI is the dominant criteria for the achievement consistency within a single priority. Collective preferences of the group for estimating the requirement trade-offs can be estimated subsequently by the aggregation of individual priorities. The aggregate priorities (i.e. the collective priority vector) are defined as ðcÞ ðcÞ ðcÞ ðcÞ P~ðcÞ ¼ p~1 ; p~2 ; . . .; p~r where p~i is obtained by the aggregation of priorities.
Global Journal of Flexible Systems Management
Qn
ðcÞ
p~i
ðkÞ
1 pi ¼ Pr Q n 1
wi
Assume GOCI AðzsÞ
ðkÞ max ¼k GOCI AZ and sth priority has the highest ::
(f) ð5Þ
ðkÞ wi 1 pi
GOCI consensus score
For crisp conversion of priority j~ pi j ¼ pi;2 0:25 þ pi;2 0:50 þ pi;3 0:25
After the crisp priorities are estimated, consensus achievement for group decision making is checked using the GOCI. n 1X ðkÞ ðcÞ GOCI PðkÞ ¼ ð7Þ vi vi r i¼1 Here v(k) and v(c) is the rank of the i-th criteria in i i ðkÞ ðcÞ p~i and p~i respectively If GOCI PðkÞ GOCLr for all the individual priorities, consensus is achieved, where GOCL 2r . Else, the consensus may be achieved through the GOCI Improvement Approach (Dong et al., 2010) as explained subsequently. Let z denote the number of iterations and be initialized to 0.
(a)
ðkÞ Let AðkÞ z ¼ aijz
rr
ðkÞ ¼ aij
ð8Þ
rr
be the matrix from decision maker k
(b)
ðkÞ ðkÞ ðkÞ Let PðkÞ ¼ p ; p . . .p z zr z1 z2
ð9Þ
be the new individual priority vectors for iteration z derived from A(k) z . (c)
ðkÞ
ðcÞ
ðkÞ
~i;z Similarly, vi;z and vi;z are derived for w ðcÞ
ð10Þ
~i;z respectively: and w
(d)
ð13Þ
ð6Þ
Calculate consensus GOCI n 1X k ðcÞ v v ¼ AðkÞ z i;z 11 n i¼1 i;z
ðkÞ ðkÞ ðkÞ Let Azþ1 ¼ aij;zþ1 & aij;zþ1 rr 8 wðcÞ 1h > ð Þ kÞ > < aðij;z if k ¼ s h i;zðcÞ ðwj;z Þ ¼ > > ðkÞ : aij;z if k 6¼ s
(g)
ð14Þ
(h) Increment z = z ? 1 and return to step 2. (i) The output judgment matrices and priorities, i.e. ðkÞ ðcÞ AðkÞ are noted for the subsequent z ; Pz andPz computations. After consensus is achieved, these aggregated priorities will be used for the subsequent stage of computations to estimate the tradeoffs. Now the dimension with the highest importance is fixed as pmax. Then to evaluate the collective value after trade-off of the dimensions for the users, arising from the six dimensions, each dimension’s priority may be discounted using a preference function. A sigmoid curve has been used since such curves represent the deterministic behavior of users better than linear approximations. Further, it has an increasing rate of utility in the initial stages and a decreasing rate of utility in the final stages and thus corresponds to the utility theory of expectations from marginal returns (Freifelder 1979). Some of the different sigmoid curves are illustrated in Fig. 2. An exponential sigmoid function has been used in the current context as it is real-valued, positive and differentiable throughout the scope. Stretching pi over the range [±6] ensures that the range of the priorities captures 99.6 % of the curve. Also, for normalizing the weights for 0–1, pmin is initialized as 0. Now, for each quantified benefit denoted as Bi, the perceived benefit PBi from that dimension is estimated as follows: Perceived Benefit PBi ¼ Bi ti 1 where xi 1 þ e xi pi pmin ¼ 6 þ 12 pmax pmin
ð15Þ
where ti ¼ f ðxi Þ ¼ (e)
If for all the individual priorities, consensus is achieved; i:e: GOCI PðkÞ GOCIr or z zmax ; go to step ðiÞ; else go to step ðf Þ
ð12Þ
ð16Þ
Here xi is calculated by stretching the range of pi over the range [±6] such that 99.6 % of the curve is achieved by the normalization with a 0.2 % cut-off at each tail. Now,
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total perceived value (TPV) for the technology as estimated by the client would be calculated as X X PBi ¼ Bi ti ð17Þ TPV ¼ k
k
This TPV may be further discounted by the profit margin (operating profit percentage or target profit percentage) of the firm, to get the upper limit of what the IaaS provider may quote as the price for the service offering, so that it may be readily accepted by the user.
Illustration of the Methodology This numerical example is a hypothetical case bearing a close resemblance to a multi-national firm, which had recently migrated to Cloud and had subscribed to an IaaS. Priorities of five executive users are captured, who had significant buy-in from the migration, so as to capture the collective preferences. Further, the prioritization as specified in the following illustration can have the practical perspective. The different dimensions used for evaluating the different sources of value are elaborated subsequently. In cloud computing, through dynamic provisioning and de-provisioning optimum size of the virtual computing environment is created on the fly to meet the dynamic behavior of the users and its size. This is the most significant key differentiator compare to traditional physical size of the computing system(s) (C1). Further, with proper resource optimization algorithms, cloud computing ensures virtual computer with optimum cost for it. Today the nature of disperse location of distributed computing environment is exploited through
dynamic price bidding optimization scenarios for further cost reduction (C2). With service being accessible at any condition and also affordable as specified, users’ basic need is met. Once it is fulfilled, the need for performance guarantee of the service/application with defined scope is the next fundamental requirement (C3). This satisfies the business need of being dynamic and realtime. Further the same service/application can have different performance guarantee to different user group. For any critical services or applications its reliability factor is of immense importance. The service should be uninterrupted under any failure or disruption situation. This ensures the true dependency of the services of critical nature (C5). Once the services or applications are of true use of business perspective, the next importance issues the information security. One has to secure the information in rest and more importantly in transit with many forms. This will helps to ensure secure information and ita access to the authorize users only (C4). The final feature is to provide the management facility to ensure these flexibilities and capabilities can the implementable, achievable and easily manageable. This feature should also ensure the capability of monitoring and controlling these features (C6). The fuzzy judgments elicited from the linguistic responses for comparing these evaluation criteria (C1–C6), were converted to crisp priorities. The individual unstandardized priorities are illustrated in Table 2. Subsequently, each of these individual priorities were standardized. Then for each estimated priority, the Geometric Consensus index (GCI) was estimated. The computations are illustrated in Table 3 subsequently.
Table 2 Individual unstandardized priorities of five users Row geometric mean
GMM
C1
C2
C3
C4
C5
C6
Sum
User 1
2.08
2.08
0.69
0.69
0.69
0.69
6.93
User 2
3.51
1.73
0.64
0.64
0.53
0.76
7.81
User 3
1.20
3.71
1.04
0.53
0.64
0.64
7.76
User 4
2.08
2.08
0.69
0.83
0.58
0.69
6.96
User 5
1.81
0.46
1.20
0.53
1.31
1.44
6.75
Table 3 Individual unstandardized priorities of five users C1
C2
C3
C4
C5
C6
User 1
0.300
0.300
0.100
0.100
0.100
0.100
0.133
User 2
0.450
0.222
0.082
0.082
0.068
0.098
0.105
User 3
0.155
0.479
0.134
0.068
0.082
0.082
0.098
User 4
0.299
0.299
0.100
0.120
0.083
0.100
0.160
User 5
0.268
0.068
0.178
0.079
0.194
0.214
-0.142
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GCI
Global Journal of Flexible Systems Management Table 4 Ranking or preferences after initial prioritization Criteria
C1
C2
C3
C4
C5
C6
P
User 1
1.50
1.50
4.50
4.50
4.50
4.50
2.35
User 2
1.00
2.00
4.50
4.50
6.00
3.00
2.55
User 3
2.00
1.00
3.00
6.00
4.50
4.50
1.58
User 4
1.50
1.50
4.50
3.00
6.00
4.50
3.61
User 5
1.00
6.00
4.00
5.00
3.00
2.00
5.10
|Vk - Vc|
Table 5 Individual priorities after achievement of consensus six dimensions after standardization, has been illustrated in Table 6 Criteria
C1
C2
C3
C4
C5
C6
User 1
0.300
0.300
0.100
0.100
0.100
0.100
User 2
0.450
0.222
0.082
0.082
0.068
0.098
User 3
0.155
0.479
0.134
0.068
0.082
0.082
User 4
0.302
0.263
0.118
0.102
0.100
0.116
User 5
0.294
0.205
0.138
0.091
0.128
0.144
After these individual priorities were estimated and subsequently aggregated, the collective priority vector that was obtained was (0.303, 0.250, 0.124, 0.096, 0.106, 0.121). From the aggregated vector, it is found that pmax = 0.303, pmin = 0.0. Now, the primary rank matrices were computed for both the initial individual priorities and aggregate priorities as illustrated in Table 4. The GOCI approach was then used for the adjustment of priorities for the achievement of consensus. After achievement of consensus, the individual priorities were obtained as illustrated in Table 5. The consensus vector after the achievement of consensus is as follows: {0.295, 0.290, 0.116, 0.091, 0.097, 0.110}. This consensus vector will be used for estimating the trade-offs among features and the values estimated from these features, in subsequent computations. Further, across all the six dimensions, value was quantified by considering some, assumptions, some industry specific data and firm specific data which bears resemblance to actual data. However, from case to case, such data would vary, and these numbers are only representative figures for illustration. •
•
Flexibility Because the service was flexible, over provisioning was not needed. Hence, the cost savings from not over-provisioning of resources was in tune of $40,000. This was estimated from comparing against flat pricing methods for static provisioning propositions. Costs Due to a shift from major CapEx to WorkEx and due to lack of dedicated hardware systems, the annual cost savings was estimated to be $60,000 after the migration.
•
•
•
•
Scope and performance Due to high performance, there was a reduction in the dedicated man-power allocated for systems. Further, there was improvement in the employee productivity due to shared resources. These created an annual benefit of $40,000. IT security and compliance The industry faces regular security breaches, due to which major investments are required on IT infrastructure, which on an average, costs $500,000. Despite these investments, the annual probability of a breach is 10 %. The solution mitigates this risk and creates a value of $50,000. Reliability and trustworthiness The availability of the service has improved the firm’s customer orientation, due to greater customization and better usage of the information assets. This, in turn, has created an improvement in the customer satisfaction. This has indirectly created a benefit of $30,000 in sales output. Service and cloud management Outsourced service support created additional cost-savings from employee productivity, when the employees could focus in their domain specific roles. Further, individualization created benefits by improving the business workflows which enhanced employee productivity and reduced time to close any tickets. This created an additional benefit of $55,000.
The estimates highlighted in the case are very close approximation to an actual case study. These values were estimated after considering data from three sources, namely, market research data on industry standards, company internal data, and client specific requirements. The total perceived value, computed after the prioritization and trade-offs estimated from the six dimensions after standardization, has been illustrated in Table 6.
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Global Journal of Flexible Systems Management Table 6 Total perceived value of IaaS State
C1
C2
C3
C4
C5
C6
pi
0.295
0.290
0.116
0.091
0.097
0.110
xi
6.000
5.815
-1.261
-2.299
-2.048
-1.522
ti
0.998
0.997
0.221
0.091
0.114
0.179
Bi
$40,000
$60,000
$40,000
$50,000
$30,000
$55,000
PBi
$39,901
$59,822
$8,835
$4,559
$3,429
$9,857
Total Perceived Value TPV =
P PBi = $1,26,402
pi = consensus priority vector; xi = standardized priority after consensus within the range; ti =transformed priority on the sigmoid function; Bi = actual benefit; PBi = perceived benefit
This would be further discounted by the profit margin (%) of the firm (assume 15 % in this case). Hence, the annual subscription fee for the IaaS may be safely quoted up to $107,442. The values obtained from each of the features or driver for the final estimation were evaluated by the five decision makers on a five point Likert scale, to check its suitability and compare it with the performance outcome of the approach proposed by Kar and Rakshit (2014), without applying the theories of consensus. The means of the preference of the computed perceived value of the current method and Kar and Rakshit (2014) was estimated to be 4.167 and 3.267 respectively, where a higher score on the Likert scale denotes a more suitable estimation of the perceived value from a specific feature prioritization. This was for 30 pairs of observations (five users for six criteria for the two methods). The z value of a paired Wilkoxon signed rank test was calculated to be 7.265 and hence there is a significant difference of the median between the responses received between the two approaches. Hence, the proposed methodology extends the existing literature (Kar and Rakshit 2014) by providing a better estimate of how to capture the trade-offs between different value drivers of an IaaS service. Further, it highlights that the achievement of consensus provides a better estimation among the tradeoffs between the value perceptions of different features.
Concluding Discussion This paper proposes the application of an integrated approach for decision support for the pricing of IaaS based on the total perceived value of the offering, from the perspective of multiple decision makers. Since it incorporates the consensual trade-offs while prioritizing and estimating the value from different features of the offering, greater buy-in from the business would be expected from such an approach. Such a pricing approach would also be extremely beneficial for the service provider, who may be able to command a higher revenues from a contract. Further, this would also be beneficial for the IT team of the firm that is
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migrating to cloud, since mapping the returns on the investment would be relatively easier and thus again, getting the business buy-in would be facilitated. Since business buy-in in a critical dimension of success for any technology, value based pricing would greatly facilitate the same, by providing direct insights of potential benefits from well-defined business cases with details on RoI achievement. This proposed method would also address the need of flexibility in pricing schemas, while addressing the requirements surrounding greater customer orientation and participation in the business decisions thereby facilitating greater openness to the customer’s needs, deeper understanding of the variability in needs, greater relationship orientation of the organization (Sushil 1997; Bichler et al. 2002; Zhang et al. 2010). Further, the hybrid method addresses the need of greater focus on the diversity in customer’s requirements rather than on the core offering while developing the pricing strategy (Sushil 2012c; Wu and Banker 2010). As established through the comparison of the outcome of the proposed method with Kar and Rakshit (2014), this approach provides a more suitable estimate of the perceived value from different dimensions of the features of an IaaS offering. Since the mean of the two are significantly different, the difference of satisfaction of the achievement of the correct perceived value for each feature, is also established. Since this approach includes the perspective of multiple decision makers, it is possible to prioritize the relative importance among these decision makers also. Further, the inclusion of fuzzy set theory accommodates the subjectivity in decision making for such complex business problems by incorporating better estimation of the prioritization of the trade-offs between different value drivers. Thus, the integration of fuzzy set theory, AHP and sigmoid utility function to capture the priorities after consensus achievement, and the associated trade-offs from the difference of priorities in the final value estimation, gives a systematic approach for arriving at the pricing of cloud services based on the needs of an individual client but from the perspective of multiple key stakeholders.
Global Journal of Flexible Systems Management
Further, it may be noted that the current research is limited in scope in providing an approach for decision support only for the problem domain of pricing flexibility in cloud offerings based on customer specific requirements. It may be extended in future research while trying to explore the causality of the evaluation dimensions to the final outcome. This would need the current research to be revisited using empirical research methodologies, whereby data could be collected from a much larger and diverse sample and analysis may be done using multivariate methods and statistical tools. Such a future study would enrich the current research significantly in bringing deeper insights from the customer’s perspective to the domain under exploration. Further, such insights would also be generalizable and would provide greater understanding of the needs for cloud computing services.
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Key Questions 1. What are the different models to offer flexibility in pricing technology products, especially emergent technologies? 2. How different consensus approaches be used for achieving multi-stakeholder involvement in estimating value of any technology solution? 3. What is the added advantage of using AHP with consensus while developing a decision support model for pricing Cloud Computing solutions? Arpan Kumar Kar is an assistant professor in Indian Institute of Technology Delhi, India. His research interests are decision sciences, e-business and technology management. He has multiple refereed journal publications and has presented his research in leading academic conferences. He has earlier worked for Indian Institute of Management Rohtak, IBM Research Laboratory and Cognizant Business Consulting. He has rich domain experience and has handled major research and consulting projects for private and public organizations under Government of India.
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Global Journal of Flexible Systems Management Atanu Rakshit is the Dean – Academics Affairs of Indian Institute of Management Rohtak. He was earlier a Professor and BoG member in NITIE. He has also served as the Deputy Director and Dean of International Institute of Information Technology (IIIT), Pune. His areas of specialization are Cloud Computing, Business Analytics and Business Process Management. Prof. Rakshit has more than 34 years of Teaching, Research and Consulting experience in the area
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of Information Technology. He is a Fellow of Computer Society of India. He has published many papers in International Journals and Conferences and has guided many Ph.D candidates.
