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2 A Case Study: Criteria for Assessing the Effectiveness of Cloud Computing According to Small and Medium Enterprises in th...

2 A Case Study: Criteria for Assessing the Effectiveness of Cloud Computing According to Small and Medium Enterprises in th...

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P. Maresˇova´ and K. Kucˇa



772

Fig. 89.1 System of

criteria for assessing the

effectiveness of cloud

computing



Economic

criterion



• Efficiency and Performance, Hardware

cost, Software cost ,Usability,

Customisation



Operational

criterion



• Client support, Compliance with standards,

Legal Matters Laws and treaties covering

the storage, access and transmission of data,

Packet loss, Connectivity



Technical

criterion



• Adaptability, Availability and Usage

Restriction, Backup and Recovery,

Response time, Elasticity, Interoperability,

Scalable storage, Portability



• Technical

• Operational

• Economic

The model is based not only on the literature review but also on the opinions of

five experts in the field of implementing this technology in the Czech Republic.

Economic criteria are related to the effectiveness of application, costs and the

user friendliness of cloud. These criteria include costs of hardware and software.

Overall, these are investment costs. These items should be then compared with the

qualitative benefits. However, this causes difficulties in numerical formulation of

effectiveness of cloud computing. Nevertheless, even for these purposes, there are

well-established methods (cost-benefit analysis).

Operational criteria are related to determining the service-level agreement

(SLA). SLAs are part of service contracts and are agreements usually between

two parties (service provider and customer), which formally define the services.

Service contracts use the percentage of service availability as a unit [30]. SLA is a

specification of services [31]. The company Cisco Systems, Inc. implements the

agreement on the quality of service in its devices under the name Cisco IOS IP [32].

Finally, the last group consists of technical criteria. The success of cloud

services depends on the required functionality and other characteristics such as

availability, respond time, latency, performance, timeliness, scalability and high

availability. All of these characteristics can be covered by the term Quality of Cloud

Service (QoCS), which comes from general QoS [33]. QoS (Quality of Service) is

used in computer science for booking and control of data flows in telecommunication and computer networks. QoS can set, for example, a top or low transfer zone

for certain data, prefer some operations or divide the operations into categories



89



Assessing the Effectiveness of Cloud Computing in European Countries



773



according to the set parameters. Thus, QoS attempts to provide its users with the

services which can guarantee quality in advance in order to avoid any delays, lossmaking or waste [34].

Conclusion

Currently, a growth is predicted in the area of cloud computing utilisation in

Europe in the following years. At the same time, the economic situation in

many countries makes companies consider every new investment. Cloud

computing is a technology that is related to costs and benefits, many of

which are difficult to grasp and express.

The aim of this contribution is to propose a system for assessing the

effectiveness of cloud computing. The described system is based on the

study of relevant literature and on the opinions of experts. This research

suggests that the basic criteria for assessing the effectiveness of cloud computing is a set of technical, operational and economic criteria.

The created model for the time being only specifies the basic levels at

which subjects implementing cloud computing could identify their requirements and expectations (using both soft and hard metrics). The model is

expected to be further developed with the intention to propose concrete

methods for measuring individual levels. The model is expected to be further

tested in real practices.



Acknowledgements This paper is published thanks to the support of the internal projects of the

University of Hradec Kralove: Economic and Managerial Aspects of Processes in Bio-medicine

and specific university research (MSMT no. 2111/2014).



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www.sla-zone.co.uk/



Chapter 90



Coordination Strategies in a Cloud

Computing Service Supply Chain Under

the Duopoly Market

Lingyun Wei, Xiaohan Yang, and Xiaoguang Zhou



Abstract Nowadays, cloud computing has become a very hot topic in the IT

industry. We study a cloud computing service supply chain consisting of one

application infrastructure provider (AIP) and two competing application service

providers (ASPs). As a result of longitudinal and transverse competitions, the

efficiency of integration is always difficult to achieve in the distributed cloud

computing service supply chain. Therefore, how to coordinate the cloud computing

service supply chain under the duopoly market to achieve the efficiency of integration is of great importance to the cloud computing industry. We develop and

evaluate four situations. We analyze the reasons that the wholesale price contract

and revenue-sharing contract can’t achieve coordination effectively and propose

all-quantity discount contract.

Keywords Cloud computing • Service supply chain • Coordination strategies

• Bertrand competition • Duopoly market



90.1



Introduction



Cloud computing has been expanded so rapidly in recent years. According to

Rebollo and Mellado [1], cloud computing is a model for enabling convenient,

on-demand network access to a shared pool of configurable computing resources

that can be rapidly deployed and released with minimal management effort. Based

on the above definition, cloud computing can be composed of three service models:

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a

Service (SaaS) [2].

With the information technology developed rapidly in the world, cloud computing has become a very hot topic in the IT industry. Many scholars do a lot of

research focusing on this field. Toka, Aivazidou, and Antoniou [3] are the advanced

L. Wei • X. Yang (*) • X. Zhou

School of Automation, Beijing University of Posts and Telecommunications, 100876 Beijing,

China

e-mail: weilingyun2010@sina.com; yangxiaohan2101@163.com; zxg@bupt.edu.cn

© Springer International Publishing Switzerland 2015

W.E. Wong (ed.), Proceedings of the 4th International Conference on Computer

Engineering and Networks, DOI 10.1007/978-3-319-11104-9_90



777



778



L. Wei et al.



group of scholars who study cloud computing in the supply chain management.

They address an overview of cloud-based supply chain management. However,

there has been relatively little research conducted that studies the coordination

strategies in a cloud computing service supply chain. Demirkan and Cheng [4] are

the first group of scholars who study the ASP strategies. They discuss the supply

chain coordination strategies and analyze a monopolistic ASP’s optimal pricing and

capacity policies considering the delay cost of users. Kar and Rakshit [5] evaluate

the cloud computing service pricing models and contract terms, and they point out

that the per transaction charge is the most common way of charging the cloud

computing service in industry. We also charge the ASP services by this method. But

neither of them had a discussion about the other actor of the cloud computing

service supply chain—AIP. Moreover, previous studies about the supply chain

coordination are more based on one AIP and one ASP. Demirkan and Cheng [6]

study coordination strategies in an SaaS supply chain consisted of one AIP and one

ASP. They propose that it is possible to create the right incentives so that the

economically efficient outcome is also the Nash equilibrium. However, their works

mentioned above don’t involve the horizontal completion between two ASPs. We

analyze duopolistic price competition of two ASPs by considering the impact of

congestion cost. Our model is more in line with the reality of the cloud market.

In this paper, we design a cloud computing service supply chain consisting of

one AIP and two competing ASPs. AIP provide packages the computer capacity as

services and supplies them to ASPs, and in turn ASPs sell the same value-added

application services to the common market via the Internet. We consider two ASPs

who sell their same value-added services to the common market and compete for

customers.



90.2



The Model of the Cloud Computing Service Supply

Chain



We attempt to research the coordination strategies of the cloud computing service

supply chain. We assume that there is full information, and AIP and ASPs are risk

neutral. Supply chain contacts in our article are by Cachon [7]. The following

sequence of events occurs in the Stackelberg Game: the AIP dominates the cloud

computing service supply and plays the leading role. The AIP offers two ASPs the

same contact; as a follower, the ASPi, i ¼ 1, 2, accepts or rejects the contact;

assuming the ASPi accepts the contract, the ASPi submits a computer capacity

order quantity, μi , to the AIP; the AIP must provide computer capacities to the ASPi

on demand; and finally transfer payments are made among the players based on the

agreed contract.

At the same time, we assume that there are only two ASPs sharing the market of

services provided by two ASPs, and the market can be regard as a duopoly market.



90



Coordination Strategies in a Cloud Computing Service Supply Chain Under. . .



779



Fig. 90.1 The ASP duopoly market



In this case, the Bertrand competition model [8] is used to characterize the competitive relationship between two ASPs.

The AIP provides the IaaS to two ASPs at wholesale price w per unit of capacity.

In turn the ASPi sells its SaaS to the market at price pi per transaction of processing.

The ASPi service system is modeled as an M/M/1 queuing system with processing

capacities μi in transactions per unit of time, and λi is a Poisson rate of transactions

per unit of time arriving at the ASPi system. We assume that λ is the market demand

and one has

λ ¼ 1 ỵ 2 ,

i ! 0,



ẳ 1 ỵ 2

i ! i ỵ



90:1ị

90:2ị



where > 0 is xed and arbitrarily small. Figure 90.1 shows the ASP duopoly.

Two ASPs sell the same value-added application services to customers via the

Internet. The Bertrand competition model is used to characterize the competitive

relationship between two ASPs. We let λi( p1, p2) be the market demand of ASPi

( j 6ẳ i):

i p1 ; p2 ị ẳ pi ỵ mpj ,



0


90:3ị



where , , and m are constants; αi is the market scale of ASPi; β is the linear

demand distribution parameter; and m is the substitutability coefficient of the

services.

As a result of the queuing delay [9], let Tsi(λi, μi) denote the expected time each

transaction stays in the service system. The marginal cost of a customer is

pi ỵ vT si i ; i ị



90:4ị



The marginal value of cloud computing services is represented by V0 (λi):

0



V ðλ i ị ẳ



i ỵ mj





m m ị ỵ m ị



When the market equilibrium is achieved, thus, one has



90:5ị



780



L. Wei et al.

0



V i ị ẳ



i ỵ mj





ẳ pi ỵ vT si

m mị ỵ mị



90:6ị



Hence, we get the market clearing price:

pi ẳ



90.3



i ỵ mj



v





m mị ỵ mị i λi



ð90:7Þ



Coordination Strategies of the Cloud Computing

Service Supply Chain



90.3.1 Centralized Control

In this situation, a single firm plays an integrated role of one AIP and two competing

ASPs. The supply chain can be coordinated and obtain the maximal profit. The per

unit time expected profit of the whole supply chain SP1 is

SP1 ¼ p1 1 ỵ p2 2 c1 ỵ 2 ị eμ21 À eμ22



ð90:8Þ



To find the whole optimal profit

maxλ1 , λ2 , μ1 , μ2 SP1



ð90:9Þ



The first-order conditions lead to

À

Á

dSPi λi ; λj ; μi ; μj

vλi

¼

À c À 2eμi ¼ 0

dμi

ðμi À λi Þ2

À

Á

dSPi λi ; λj ; μi ; j

2i ỵ 2mj



vi







ẳ0

di

m mị ỵ mị i i ị2



90:10ị

90:11ị



We nd that 1 ẳ 2 ẳ 2λ, μ1 ¼ μ2 ¼ μ2 and p1 ¼ p2 ¼ p0. Hence, solutions to the

expressions above provide us the optimal arrival rate, λ*; the optimal capacity, μ*;

and the optimal whole profit, SPÃ1 .



90.3.2 Wholesale Price Contract

With a wholesale price contract, the AIP, ASP1, and ASP2 independently make

decisions to pursue optimal profits. Let HP2(w), AP2i(Pi, λ1, μ1) be the per unit time

expected profits of the AIP and ASPi, respectively, and they can be described by



90



Coordination Strategies in a Cloud Computing Service Supply Chain Under. . .



781



HP2 wị ẳ w1 ỵ 2 ị c1 ỵ 2 ị e21 e22



90:12ị



AP2i pi ; i ; 1 ị ẳ pi i À wμi



ð90:13Þ



In this case, the ASPi solves the following problem to find its optimal profit:

maxp1 , λ1 , μ1 AP2i



ð90:14Þ



The rst-order conditions lead to

2i ỵ mj

dAP2i i ; i ị



vi







ẳ0

di

m mị ỵ mị i i Þ2

dAP2i ðλi ; μi Þ

vλi

¼

Àw¼0

dμi

ðμ i À λ i Þ2



ð90:15Þ

ð90:16Þ



If the wholesale price contract can coordinate the cloud computing service supply

chain, under the premise that equations above are satisfied, we should enable

1 ðλ;μÞ

Eqs. (90.10) and (90.11) to be valid. However, dAP21d1 1 ;1 ị ẳ dAP22d2 2 ;2 ị 6ẳ dSPdλ

.

Hence, the wholesale price contract cannot coordinate the cloud computing service

supply chain.



90.3.3 Revenue-Sharing Contract

In this situation, the ASPi shares to the AIP a percentage (1 À δ) of his/her revenue,

so δ is the fraction of supply chain revenue the ASPi keeps. The profit function of

AIP is

HP3 ðp1 ; p2 ; 1 ; 2 ị ẳ w1 ỵ 2 ị c1 ỵ 2 ị e21 e22

ỵ 1 ịp1 1 ỵ 1 ịp2 2



90:17ị



The prot function of ASPi is

AP3i pi ; i ; i ị ẳ pi λi À wμi



ð90:18Þ



In this case, the ASPi solves the following problem to find its optimal profit:

maxpi , λi , μi AP3i

The rst-order conditions lead to



90:19ị



782



L. Wei et al.



"

#

2i ỵ mj

dAP3i i ; i ị



vi



ẳ0





di

m mị ỵ mị i i ị2

dAP3i i ; i ị

vi



wẳ0

di

i À λ i Þ2



ð90:20Þ

ð90:21Þ



If the revenue-sharing contract can coordinate the cloud computing service supply

chain, under the premise that equations above are satisfied, we should enable

Eqs. h (90.10) and

i h (90.11) to be valid. iWe find that w ¼ δ(c + e) and







m



2v



=

ị2





m



2ỵm

2v

2m

ịỵmị ị2 . However, after illustrating the



results by numerical examples, we find that only when δ tends to zero can the

supply chain be coordinated. In other words, the ASPi shares so little profit that it

refuses this revenue-sharing contract. So the revenue-sharing contract can’t coordinate the cloud computing service supply chain.



90.3.4 All-Quantity Discount Contract

We investigate whether an all-quantity discount strategy coordinates a supply chain

with one AIP and two competing ASPs. Let w(μi) be the AIP’s wholesale price:

&

wðμi Þ ¼



w0 ,

wÃ4 ,



μi < μÃ

μi ! μÃ



ð90:22Þ



The profit function of ASPi is

AP4i pi ; i ; i ị ẳ pi λi À wðμi Þμi



ð90:23Þ



The profit function of the whole supply chain is

SP4 ẳ p1 1 ỵ p2 2 c1 ỵ 2 ị e21 e22



90:24ị



Assuming that the revenue of ASPi is a constant proportion of SP4, we have

X

i < 1

90:25ị

AP4i ẳ i SP4 , 0 <

We assume 1 ẳ and nd that

1 ẳ 2 ẳ



90:26ị



90



Coordination Strategies in a Cloud Computing Service Supply Chain Under. . .



8



>

< w, i <

2

wi ị ẳ ẵp 2

p0 λà À cμà À 0:5eμÃ2 ފ

μÃ

>

0

:

, μi !

Ã

μ

2



783



ð90:27Þ



ηSP4 ! AP11 ¼ AP12



ð90:28Þ



As a result of the flexibility in the profit distribution, the all-quantity discount

contract ensures that participation constraints of AIP and ASPs can be satisfied

when the system performance achieves the optimal. So, the supply chain can be

coordinated.



90.4



Numerical Examples



In this part, we select a set of parameters (see Table 90.1) to illustrate whether the

contracts can coordinate the cloud computing service supply chain under the

duopoly market. After some calculation, we can get the computed results in

Table 90.2. We can find that the maximal supply chain expected profit is 3.2447.

In other words, when the expected profit can reach 3.2447 with one supply chain

contract, we can deem that this contract can coordinate the cloud computing

service supply chain. Obviously, the revenue-sharing contract and all-quantity

Table 90.1 Baseline

parameters



c



α



v



e



β



m



1.00



3.00



0.025



1.00



1.00



0.50



Table 90.2 Computed results

λ1

λ2

λ

μ1

μ2

μ

P1

P2

P0

δ

η

w

HP

AP1

AP2

SP



Centralized



Wholesale price



Revenue sharing



All-quantity discount



0.7557

0.7557

1.5114

0.8397

0.8397

1.6794

4.1908

4.1908

4.1908













3.2447



0.9740

0.9740

1.9480

1.0727

1.0727

2.1454

3.7987

3.7987

3.7987





2.5

0.9167

1.0182

1.0182

2.9531



0.7557

0.7557

1.5114

0.8397

0.8397

1.6794

4.1908

4.1908

4.1908

0



0

3.2447

0

0

3.2447



0.7557

0.7557

1.5114

0.8397

0.8397

1.6794

4.1908

4.1908

4.1908



0.32

2.5352

1.1681

1.0383

1.0383

3.2447



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