IEEE CONECCT2013 1569685043
Comparison of Cloud Service Providers Based on Direct and Recommended Trust Rating Supriya M, K Sangeeta
G K Patra
Department of Computer Science and Engineering, Amrita School of Engineering Bengaluru, India
[email protected],
[email protected]
Centre for Mathematical Modelling and Computer Simulations, Council of Scientific and Industrial Research Bengaluru, India
[email protected]
Abstract - Cloud computing provides many opportunities for enterprises by offering a range of computing services. In today’s competitive environment, the service dynamism, elasticity, and choices offered by this highly scalable technology are too attractive for enterprises to ignore. These opportunities have opened up a new dimension of challenges by introducing a different type of trust scenario. Today, the problem of trusting cloud computing is of supreme concern for most enterprises. The diversity of cloud service offerings makes it necessary for customers to evaluate the service levels of different cloud providers in an objective way such that required quality, reliability or security of an application can be ensured. In this paper, a model for Trust Management based on Fuzzy Logic is used to compare the cloud service providers available in the market.
by different cloud providers. With this diversity of cloud service offerings, an important challenge for customers is to evaluate the service levels of different cloud providers in an objective way such that the required quality, reliability or security of an application can be ensured.
Keywords - Cloud Service Provider; Trust Management; Fuzzy Logic; Cloud Analyst.
Reference [6] compares the trust management models for cloud computing given by various researchers with special emphasis on their capability, implementability and applicability in practical heterogonous cloud environments. Trust issues in technology and business perspectives of cloud computing have been analyzed in [7]. A new cloud based infrastructure that allows a clean differentiation between applications and data is described in [8]. It also introduces the concept of trusted data in binding which involves four parties namely the resource provider, software provider, data provider and coordinator. A trust model of cloud security in terms of social security is proposed in [9]. Here the security issue is identified as a social insecurity problem and is handled using a hierarchical trust model. A Multitenancy Trusted Computing Environment Model (MTCEM) for cloud computing has been proposed in [10]. In this model trusted IaaS is delivered to customers with a dual level transitive trust mechanism that supports a security duty separation function simultaneously. To protect the software running in the cloud, watermark-aware trusted running environment is described in [11]. Reference [12] describes a trust evaluation metric for cloud applications based on Fuzzy Logic. The metric uses a data set created from the online survey. SMICLOUD frame work and a mechanism for ranking CSPs is described in [13]. This paper defines the Key Performance Indicators (KPI) and compares cloud service providers based on the Service Measurement Index proposed by Cloud Service Measurement Index Consortium (CSMIC) [14]. Reference [15] provides an Inter domain trust management model based on fuzzy logic to evaluate the trust value rating for the CSPs. This rating is used to choose a CSP as per users’ requirement.
I.
CSPs can be rated based on the trust a user can have on them. Trust is the estimation of competence of a resource provider in completing a task based on reliability, security, capability and availability in the context of distributed environment [4]. These trust values can be obtained from the trust management models used to compare the behavior of the elements and entities in cloud systems [5].
INTRODUCTION
Cloud computing is a market oriented distributed computing system consisting of collection of interconnected and virtualised computers that are dynamically provisioned and presented as one or more unified computing resources based on service oriented agreements established through negotiation between service providers and consumers. The main factors contributing to the surge and interest in cloud computing are: rapid decrease in hardware cost with increase in computing power and storage capacity, advent of multi-core architecture and modern supercomputer, exponentially growing data size in scientific instrumentation/ simulation, internet publishing, archiving, and the wide-spread adoption of services computing and Web 2.0 applications [1][2]. Flexible and speedy services at low cost are provided on demand to cloud users over high-speed Internet within the ―X as a service (XaaS) computing framework in a transparent manner. However, these concentrated resources and data centres obviously present more attractive targets to attackers thus bringing new security issues for cloud service providers (CSPs). The consumers on the other hand are sceptical about cloud security before they determine to use the services of cloud providers [3]. The number of CSPs has increased exponentially in the past few years, providing more options for the customers to choose from. Each CSP provides different plans that cater to the needs of different customers at different cost. Often, there may be trade-offs between the functional and non-functional requirements fulfilled
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Agility as inputs and produces a range of values which are fed as input to the next level of processing. The second stage is the implementation using Sugeno FIS. It takes the output of the Mamdani FIS and obtains the trust rating for the CSP. The implementation of FIS in MATLAB [17] needs membership functions to be defined with membership values. The membership values for these parameters are assumed as low, medium, high and very high as per the requirement.
In this paper, the model proposed in [15] is used to compare the various CSPs based on their direct and recommended trust rating. The paper is organized as follows: Section II describes the trust model and its parameters. Simulation of CSPs and the results are discussed in Sections III and IV respectively. The paper is concluded in Section V. II.
MODEL DESCRIPTION
The above two stages are implemented hierarchically using the fuzzy logic blocks in Simulink of MATLAB.
The model architecture described in [15] is based on Direct and Recommended information and estimates the trust value for CSP in Inter and Intra Domain. The model is simulated using Cloud Analyst developed by Wickremasinghe, B, Calheiros R.N and Buyya, R. [16] at the CLOUDS Laboratory.
III.
SIMULATION OF SERVICE PROVIDERS
The trust management model is now tested for its validity using different scenarios and capabilities for the following service providers: Go Grid, Rackspace, Nephoscale and Reliacloud. The parameters identified to evaluate these cloud providers are No. of V.Ms, No. of D.Cs, Storage Space, V.M Cost, Storage Cost, Transfer Cost, No. of Processors and RAM capacity. Table I lists these parameters corresponding to each plan of the CSP obtained from online survey. Simulation of these CSPs in Cloud Analyst simulation needs the User Bases (U.B) to be defined randomly across the regions in the globe, the regions being the 7 continents numbered 0 through 6 which are assumed as mentioned in Table II. This U.B description is kept constant to analyze the performance of different CSPs under the same load.
Simulation in Cloud Analyst involves: defining and configuring User Bases (U.Bs) and Data Centers (D.Cs), allocating of Virtual Machines (V.Ms) in Data Centers and adjustment of various parameters such as Packet size, No of packets, Bandwidth, and Load balancing policies. The Cloud Analyst enables to model different scenarios of CSPs and U.Bs, and provides a comprehensive output detailing the response time, D.C processing time and total cost involved in the communication and computation. The evaluation of the trust value for CSP comprises of 2 stages. The first stage is the implementation with the help of Mamdani Fuzzy Inference System (FIS) [17], which takes the parameters Performance, Financial and
TABLE I.
CSP PARAMETERS
CSP and Server type
No of V.M
No. of D.C
Storage Space in T.B
V.M Cost/hr($)
Storage Cost/GB($)
Transfer Cost/GB($)
No. of Processors
RAM in GB
GoGrid Standard Dedicated Server
4
3
0.642
0.4166
0.15
0.29
4
8
GoGrid Advanced Dedicated Server
8
3
1
0.5553
0.15
0.29
8
12
GoGrid Ultra Dedicated Server
8
3
0.735
0.8333
0.15
0.29
8
24
GoGrid Elite Dedicated Server
12
3
0.934
1.666
0.15
0.29
12
48
Nephoscale DS 2
2
1
0.25
0.2069
0.12
0.13
2
2
Nephoscale DS 4
4
1
0.5
0.3458
0.12
0.13
4
4
Nephoscale DS 8
4
1
1
0.4847
0.12
0.13
4
8
Nephoscale DS 16
4
1
0.588
0.8319
0.12
0.13
4
16
Nephoscale DS 24
8
1
1
0.8319
0.12
0.13
8
24
Nephoscale DS 144
8
1
1
2.0819
0.12
0.13
8
144
Rackspace Enhanced One
2
8
0.219
1.068
0.1
0.18
2
8
Rackspace Enhanced Two
4
8
0.292
1.525
0.1
0.18
4
8
Rackspace Performance One
6
8
0.6
1.694
0.1
0.18
6
24
Rackspace Performance Two
12
8
0.292
2.083
0.1
0.18
12
64
ReliaCloud One
2
2
0.05
0.05
0.15
0.12
2
0.5
ReliaCloud Two
2
2
0.1
0.08
0.15
0.12
2
1
ReliaCloud Three
4
2
0.2
0.16
0.15
0.12
4
2
ReliaCloud Four
8
2
0.4
0.32
0.15
0.12
8
4
ReliaCloud Five
16
2
0.8
0.64
0.15
0.12
16
8
2
TABLE II.
USER BASE DETAILS
Name
Region
Requests per user per Hr
Data Size per Request (bytes)
Peak Hrs. (GMT)
Peak Hrs End (GMT)
Avg Peak Users
Avg OffPeak Users
UB1
2
300
100
6
14
1000
100
UB2
1
600
100
3
18
1000
100
UB3
3
610
100
3
18
1000
100
UB4
4
150
100
0
9
1000
100
UB5
5
1000
100
9
21
1000
100
UB6
2
60
100
3
9
1000
100
UB7
0
60
100
3
9
1000
100
Simulation of U.B and D.Cs is carried out for each CSP in Cloud Analyst as follows:
needs. This is represented in Fig. 2. C. Rackspace
A. Go Grid
Rackspace [20] consists of four different instances of dedicated servers: Enhanced one, Enhanced two, Performance one, Performance Two. It has eight D.Cs across the globe: Five in U.S.A, two in Europe, and one in Asia as shown in Fig. 3.
Go Grid [18] CSP consists of four different plans for its users: standard, advanced, ultra and elite. It has 3 D.Cs located across the globe, two in U.S.A and one in Europe. Fig. 1 shows the Go Grid setup simulated on Cloud Analyst. B. Nephoscale
D. ReliaCloud
Nephoscale [19] has six different plans: DS 2, DS 4, DS 8, DS 16, DS 24, and DS 144 each of which caters to different users. It has only one D.C to cater to all the
ReliaCloud [21] provides five different configurations of server models. It has two D.Cs both located in U.S.A. Fig. 4 shows this simulated configuration.
Fig. 1 Go Grid Simulation Setup
3
Fig. 2 Nephoscale Simulation Setup
Fig. 3 Rackspace Simulation Setup
4
Fig. 4 ReliaCloud Simulation Setup
IV.
Total cost as an additional parameter to Financial attribute. These updated parameters along with the data described in Table I are once again given to the hierarchical fuzzy model to obtain the recommended trust rating.
RESULTS AND DISCUSSIONS
The parameters in Table II are categorized as Performance, Financial and Agility and fed as input to the fuzzy logic tool box to yield the direct trust rating corresponding to each plan of a CSP. For a given set of U.B details, Cloud Analyst provides the maximum D.C Processing time and total cost of the computation corresponding to each plan of a CSP as given in Table III. TABLE III.
A sample output from the FIS is shown with the inputs of agility, financial and performance being 0.456, 0.855, 0.55 respectively and overall trust rating as 0.4.
CLOUDANALYST RESULTS
CSP and Server Type
Max D.C Processing Time (ms)
GoGrid Standard Dedicated Server
100.8
Total Cost ($) 127.54
GoGrid Advanced Dedicated Server
100.8
193.37
GoGrid Ultra Dedicated Server
100.8
247.07
GoGrid Elite Dedicated Server
100.8
567.78
Nephoscale DS2
200.1
88.36
Nephoscale DS4
100.8
111.08
Nephoscale DS8
100.8
124.94
Nephoscale DS16
100.8
158.36
Nephoscale DS24
100.8
237.38
Nephoscale DS144
100.8
478.82
Rackspace Enhanced One
100.8
25.21
Rackspace Enhanced Two
100.8
29.94
Rackspace Performance One
100.8
34.71
Rackspace Performance Two
100.8
46.02
Reliacloud 1
227.35
43.7
Reliacloud 2
157.1
168.18
Reliacloud 3
100.8
58.85
Reliacloud 4
100.8
105.28
Reliacloud 5
100.8
289.42
Fig. 5 Sample Output from Trust FIS
Fig. 6 shows the comparison of direct trust and recommended trust values that are obtained from the hierarchical FIS against its corresponding scenarios. It can be seen that even when the direct trust rating is zero, the recommended rating is non-zero which helps the consumers to make a judgement based on the rating of the plans of various CSPs, e.g. Nephoscale DS 2 and DS 4. Direct trust rating simulations using Cloud Analyst gives values which are akin to values obtained from recommendation.
Now the Maximum D.C processing time is considered as an additional parameter to the Performance attribute and
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Dirrect vs Recommended Trust ReliaCloud Five ReliaCloud Four ReliaCloud Three ReliaCloud Two ReliaCloud One Rackspace Performance Two Rackspace Performance One Rackspace Enhanced Two
Direct Trust Rating
Rackspace Enhanced One Nephoscale DS 144
Recommended Trust Rating
Nephoscale DS 24 Nephoscale DS 16 Nephoscale DS 8 Nephoscale DS 4 Nephoscale DS 2 GoGrid Elite Dedicated Server GoGrid Ultra Dedicated Server GoGrid Advanced Dedicated Server GoGrid Standard Dedicated Server 0
0.2
0.4
0.6
0.8
Figg. 6 Direct vs Recommended Trust Evaluation Results
V.
November 2010. [9] Sato H., Kanai A. and Tanimoto S., “A cloud trust model in a SJ International Symposium on security aware cloud,” IEEE/IPS Applications and the Internet, Korea, K pp.121-124, July 2010. [10] Xiao Yong Li, Li Tao Zhou, Yoong Shi and Yu Guo, “A trusted computing environment modell in cloud architecture,” International Conference on Machine Learnning and Cybernetics, China, vol. 6, pp.2843-2848, 2010. [11] Junning Fu, Chaokun Wang, Zhhiwei Yu, Jianmin Wang and JiaGuang Sun, “A watermark awaare trusted running environment for Software clouds,” Annual ChinnaGrid Conference, China, pp. 144151, July 2010. [12] Mohammed Alhamad, Tharam Dillon and Elizabeth Chang, “A trust evaluation metric for cloud appplications,” International Journal of Machine Learning and Compuuting, vol. 1, no. 4, pp.416-421, 2011. [13] Garg S.K., Versteeg S. and Buyyya R , “SMICloud: A framework for comparing and ranking cloud services,” s International Conference on Utility and Cloud Computing, Australia, A pp.210-218, December 2011. m/documents/10508/186d5f13-f40e[14] http://www.cloudcommons.com 47ad-b9a6-4f246cf7e34f, “Clouud Service Measurement Index Consortium, Service Measurem ment Index Version 1.0,” PDF Report, September 2011. [15] Supriya M, Venkataramana L.JJ, Sangeeta K and G.K Patra, “Estimating trust value for clouud service providers using fuzzy logic,” International Journal off Computer Applications, vol. 48, no. 19, pp. 28-34, June 2012. [16] Wickremasinghe B, Calheiros R.N R and Buyya, R., “CloudAnalyst: A CloudSim based visual modelleer for analyzing cloud computing environments and applications,”” International Conference on Advanced Information Networkking and Applications, Australia, pp. 446-452, April 2010. [17]http://www.mathworks.com/hellp/pdf_doc/fuzzy/fuzzy.pdf, Fuzzy Logic Toolbox™ User‘s Guide.. [18]http://www.gogrid.com/cloud-hhosting/dedicated-servers.php, GoGrid Cloud Hosting: Dedicated Serveers, Physical Servers. [19]http://www.nephoscale.com/deddicated-servers, NephoScale OnDemand Dedicated Servers. [20]http://www.rackspace.com/mannaged_hosting/configurations/, RackSpace: Dedicated Server, Managed M Hosting & Web Hosting Configurations. [21]http://www.reliacloud.com/clouudservers/pricing/, ReliaCloud: Cloud Computing Pricing.
CONCLUSION
This work can be used to rate plans provideed by CSPs. It is very clear from the comparison given in i the previous section that the recommended trust raating helps the consumers to choose a CSP based on theeir requirement. The hierarchical fuzzy trust model used in this paper rates the Rackspace Performance One and Two plans to be the highest preferred plans for the U.B detailss given in Table II. However, based on the need the useers can use this model to choose a best plan for them. VI.
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