Hierarchical Trust Model to Rate Cloud Service Providers based on Infrastructure as a Service

Transcription

1 Hierarchical Model to Rate Cloud Service Providers based on Infrastructure as a Service Supriya M 1, Sangeeta K 1, G K Patra 2 1 Department of CSE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India. 2 CSIR Fourth Paradigm Institute (Formerly CSIR C-MMACS), Bengaluru, India {m_supriya,k_sangeeta}@blr.amrita.edu, gkpatra@csir4pi.in Abstract In large scale distributed systems like cloud computing, customers need to interact with unknown service providers to carry out tasks or transactions. The ability to reason about and assess the possible risks in carrying out such transactions is necessary for providing a safe and trustworthy environment. Cooperative characteristics of distributed computing systems enforce a proper and secure trust management to be in place to minimize the risks posed by different malicious agents. is the estimation of competency of a resource provider in completing a task based on dependability, security, ability and availability in the context of distributed environment. It enables users to select the best resources in the heterogeneous cloud infrastructure. In this paper, a hierarchical trust model has been proposed to manage the trust and to rate the service providers and their various plans based on IaaS in cloud computing environment. 1. Introduction Cloud model is the latest advancement in the large distributed system category. Cloud computing is a pervasive paradigm, where large pools of systems are connected in private or public networks, to provide dynamically scalable infrastructure for application, data and file storage [1]. It refers to the underlying infrastructure that provides services to customers via defined interfaces. Services are provided on demand basis to cloud users over high-speed internet within the X as a service (XaaS) computing framework where X represents Infrastructure, Platform, Software, Database etc. Among the various service models available in cloud, Infrastructure as a Service (IaaS) plays a vital role. IaaS is the delivery of computing resources as a service through APIs, which includes virtual machines, operating systems and other abstracted hardware [2]. The customer rents these resources which are dynamically scalable as per usage, rather than buying and installing them. Examples for IaaS include Amazon EC2 and S3 service providers. Due to the large scale and openness of these systems, a customer is often required to interact with service providers with whom he has few or no shared past interactions. To assess the risk of such interactions and to determine whether an unknown service provider is trustworthy, an efficient trust mechanism is necessary. is an important ingredient facilitating reliable interactions among autonomous participants in diverse large-scale systems including e-commerce, distributed and peer-to-peer systems, multi-agent systems and dynamic collaborative systems [3]. Firdhous et. al. [4] have provided a comprehensive survey on the trust management systems implemented on distributed systems with a special emphasis on cloud computing. The critical security challenges like data service outsourcing security and computation outsourcing security are outlined in [5], with emphasis on the need to address access control and multitenancy issues for a trustworthy public cloud environment. A formal trust management model for Software as a Service (SaaS) based on the basics of the trust characteristics is presented in [6]. This model is capable to handle various cloud services access scenarios where an entity may or may not have a past experience with the service. Xin [7] proposes the use of stereotypes to assess trustworthiness of the target agent whose past behaviour information is not locally available to a trustor, which is very common in large scale, open distributed systems. The problem of trust evaluation has not been done practically. In recent years, fuzzy logic has been used in several decision support systems, to represent uncertainties, especially when they need to be handled quantitatively. It offers the ability to handle uncertainty and imprecision effectively, and is therefore ideally suited to reasoning about trust. The fuzzy operations and rules can be used in the formal decision-making process to handle uncertainty in trust management. 1102

2 A model to estimate the trust value of the cloud service providers using fuzzy logic is described in [8]. This model is used in [9] to compare the cloud service providers and their various plans based on the direct and recommended trust considering, Finance and as parameters. In this model, was not considered as a parameter which is an important requirement for the users of the cloud since when a company outsources its confidential data to another company or a cloud; it needs assurance that the service provider has used reasonable security to protect those data. In this paper, we propose a hierarchical model which extends the model described in [9] and rates the cloud service providers and their plans based on 5 parameters:, Finance,, and. In this model the user has the option to give priority to /Finance as compared to other parameters and rate the service providers based on their level of security and cost. The rest of the paper is organized as follows: Section 2 describes the SMI Framework and the proposed hierarchical model is described in Section 3. Cloud Service Providers (CSPs), their plans and infrastructure details are described in Section 4. Section 5 explains the simulation and results of Cloud Analyst. The rating of the service providers using the hierarchical model is arrived at in Section 6. The paper is concluded in Section SMI Framework The Service Measurement Index (SMI) is a set of business-relevant Key Indicators (KPI's) that provide a standardized method for measuring and comparing a business service [10]. This method is used by the organizations to measure cloud-based business services based on their specific business and technology requirements. The Service Measurement Index is currently being developed by the Cloud Services Measurement Initiative Consortium (CSMIC). The SMI framework describes Accountability,, Assurance,, /Privacy, and as the seven KPI s. These KPI s have various attributes that help to measure and compare the business services. 3. Hierarchical Model Description The trust evaluation model described in [8] uses, and KPIs described by CSMIC and rates the CSPs using fuzzy logic toolbox of Matlab [11]. This model implementation comprises of 2 stages. The first stage is the implementation with the help of Mamdani which evaluates, and parameters. The parameter is evaluated by considering the number of processors and the RAM capacity available with the CSP. parameter is evaluated using Virtual Machine (V.M) Cost, Storage Cost, and Data Transfer Cost. parameter takes number of Data Centers (DCs), Storage space and number of V.Ms as its inputs. The second stage implementation takes the output of the first stage FIS and helps to obtain the trust rating for each plan of the CSP. The trust values obtained from the above is considered as the Direct (i.e) only by the observation of the infrastructure facilities available with the CSP. These infrastructure facilities are simulated using Cloud Analyst [12] which provides the DC processing time and Total cost as the output. These outputs are fed to the parameter and Finance parameter respectively (in addition to the above mentioned inputs) and the FIS is re-run to get the Recommended value between 0 and 1. The model block diagram (Direct ) is shown in Figure 1. No. of Processors Processor Speed (RAM) V.M Cost Storage Cost Data Transfer Cost Degree of No. of DCs Storage Space No. of V.Ms Figure 1 Model Block Diagram 1103

3 The model discussed above rates the CSPs and their plans with no emphasis given to security. But the data security raises a number of concerns, including the risk of loss, unauthorized collection and usage, if the CSP does not provide adequate data protection [2]. In other words, security is a major concern for all consumers. So the model of Figure 1 has now been extended to include two more parameters - and. The parameter is described in terms of the Physical, Internal and Network levels available with the cloud provider, while the parameter of the model is calculated based on the contributions from the Understandability, Easability and Flexibility attributes. Contribution of various attributes towards the model parameters is listed Table 1. Once again a two stage (FIS) is used for estimation of trust value corresponding to different plans of the CSP for the model shown in Figure 2. But in this model, the second stage of the FIS alone takes 5 5 = 3125 rules, i.e the number of inputs to the FIS to the power number of membership values (very low, low, medium, high and very high). If we desire to extend this model further with one additional parameter it would take 5 6 = rules. Hence the complexity of the system increases exponentially, if we desire to compute the trust values based on all parameters of the SMI as mentioned in section 2. This necessitates the development of hierarchical model where the input parameters can be chosen by the consumer as per his requirements and priority to bring down the number of rules for rating the service providers. TABLE 1 Model Parameters KPI Parameter Contributing Attributes No. of Physical Units (DCs), No. of V.Ms, Memory Size Finance V.M Cost, Storage Cost, Data Transfer Cost No. of Processors, Processor Speed (RAM) Physical, Internal, Network Understandability, Easability, Flexibility No. of Processors Processor Speed (RAM) V.M Cost Storage Cost Data Transfer Cost No. of DCs Storage Space No. of V.Ms Degree of Physical Internal Network Understandability Easability Flexibility In this work a hierarchical model to rate the CSPs has been designed giving priority to Finance/ as input parameters to the trust model as shown in Figures 3 and 4 respectively. These models provide the trust value for the CSP based on Direct rating. The DC Processing time and Total cost obtained from the Cloud Analyst simulation are then added to the and FIS respectively to obtain the Recommended rating. Figure 2 Proposed Model Block Diagram The user or customer of the cloud may need to transact with the service provider and the task he needs to complete may be a highly confidential one. He may not bother about the aspect. His concern would be only on the aspect. For, such scenario the user may prefer the model shown in Figure 4 whereas if his concern is mainly on the Finance rather than he may choose the model shown in Figure

4 No. of DCs Storage Space No. of V.Ms V.M Cost Storage Cost Data Transfer Cost No. of Processors Processor Speed (RAM) Degree of Physical Internal Network Understandability Easability Flexibility Figure 3 Hierarchical Model based on Finance No. of DCs Storage Space No. of V.Ms Physical Internal Network No. of Processors Processor Speed (RAM) Degree of V.M Cost Storage Cost Data Transfer Cost Understandability Easability Flexibility Figure 4 Hierarchical Model based on 1105

5 The hierarchical models shown in Figures 3 and 4 have three stages and works as follows: If Finance parameter is more important then as shown in Figure 3 the Finance and FIS gets evaluated separately and the, and FIS gets evaluated separately and finally the trust value of the CSP plan is obtained. Likewise, if has a higher priority then as shown in Figure 4 and FIS gets evaluated as one set and, and FIS gets evaluated as another set to obtain the trust value corresponding to a CSP plan. When compared with the model shown in Figure 2 which requires 3125 rules in the second stage, the hierarchical model requires only 175 rules including both second and third stages. 4. CSPs and their Plans The various plans provided by the following service providers: GoGrid,, Amazon EC2 and Cloudflare have been rated using the hierarchical trust model. Table 2 and Table 3 show the different plans, Data Center (DC) location across the globe and the input parameters corresponding to each service provider drawn from the published information [13, 14, 15, 16]. However, and are customer dependent and not exactly quantifiable. So, to test the model a value between 0 and 1 is assigned based on the available survey data. The information of Table 2 and 3 has been used to set up the DCs available with each CSP during the simulation. TABLE 2 CSPs and their location CSP Name of the Plans DCs and their Location GoGrid 4 plans: standard, 3 DCs : two in U.S.A, advanced, ultra, elite one in Europe 4 plans: Enhanced 8 DCs: Five in U.S.A, one, Enhanced two, two in Europe, one in one, Asia Two Amazon EC2 Cloudflare 3 Plans: Amazon EC2 Small, Amazon EC2 Medium, Amazon EC2 Large 3 Plans: Cloudflare Pro, Cloudflare Business, Cloudflare Enterprise 6 DCs: three in U.S.A, one in Europe, two in Asia 4 DCs: one in U.S.A, one in South America, two in Asia TABLE 3 Various Plans of CSPs with their Parameter Details CSP and Server type No of V.M No of DC Storage Space in T.B V.M Cost/hr($) Storage Cost / GB($) Transfer Cost / GB($) No. of Processors RAM In GB Physical Internal Network Understand ability Easability Flexibility Gogrid Standard Dedicated Server Gogrid Advanced Dedicated Server Gogrid Ultra Dedicated Server Gogrid Elite Dedicated Server Enhanced One Enhanced Two

6 Performanc e One Performanc e Two Amazon EC2 Small Amazon EC2 Medium Amazon EC2 Large Cloud flare Pro Cloud flare Business Cloud flare Enterprise Cloud Analyst Simulation and Results Cloud Analyst simulation of CSPs mentioned in Section 4 needs the User Bases (UB) to be defined randomly across the regions in the globe as described in [8] and [9]. The regions considered are the six continents labelled R0 through R5 as listed in Table 4. This UB description is kept constant throughout the simulation to analyze the performance of different CSPs under the same load. TABLE 4 UserBase Description Name Region Requests per User per Hr Data Size per Request (bytes) Peak Hrs. (GMT) Peak Hrs End (GMT) Avg Peak Users Avg Off- Peak Users UB UB UB UB UB UB UB A sample Cloud Analyst simulation setup and the results after simulation of Amazon EC2 Medium Plan are shown in Figures 5 and 6 respectively. Figure 5 Amazon EC2 Medium Simulation setup Figure 5 shows the Data Centers represented as DC numbered 1 through 18 (6 DCs each offering 3 plans, three in USA, one in Europe, two in Asia as mentioned in Table 2) and the User Bases numbered 1 through 7 located across the globe (as mentioned in Table 4). The infrastructure details of Table 3 and the UB requests of Table 4 are loaded in the configuration window of Cloud Analyst. The simulation run corresponding to each CSP plan provides the average response time, DC processing time and total cost involved in the transaction. A snapshot from Cloud Analyst simulation showing the maximum and minimum response times against each of the User Bases for the Amazon EC2 Medium plan is shown in Figure 6. Since this response time is random for every simulation, it has not been considered in the evaluation of Recommended. Table 5 lists the DC processing time and total cost obtained from the Cloud Analyst simulation for each CSP plan which has been used to obtain the Recommended. 1107

7 TABLE 5 Cloud Analyst Simulation Results DC CSP and Server Total Cost Processing type ($) Time (ms) Gogrid Standard Dedicated Server Gogrid Advanced Dedicated Server Gogrid Ultra Dedicated Server Gogrid Elite Dedicated Server Enhanced One Enhanced Two One Two Amazon EC2 Small Amazon EC2 Medium Figure 6 Amazon EC2 Medium after Simulation Amazon EC2 Large Results and Discussion 6.1 Direct and Recommended with 3 and 5 model parameters The non hierarchical models shown in Figures 1 and 2 have been modelled in simulink which in turn calls the FIS created for each parameter. Execution of the simulink model gives a set of Direct and Recommended values for each plan of CSP as listed in Table 6. It is observed that the Recommended values are higher than the Direct values as these include the recommendations or references collected from other parties in the initial trust. Addition of and parameters to the model of Figure 1 reduces the variations in the trust values for all CSPs which are as expected. Also, the plans that provide high security are clearly differentiated from the other CSPs. For example, with three parameters Gogrid Elite Dedicated Server and Amazon EC2 Large plans are rated highest in Recommended (with trust value of 0.794), but their trust values go down with five parameters, which is because of the level of security provided by these CSP plans. Cloud flare Pro Cloud flare Business Cloud flare Enterprise Direct and Recommended based on Finance/ Table 7 shows the trust values of the CSPs corresponding to the models in Figures 3 and 4. Here too the Recommended values are higher than 1108

8 the Direct values except for Two (highlighted in the Tables 6 and 7) due to higher total processing cost (in $) for the user requests (as shown in Table 5, this plan takes the highest $807.77). This is also reflected as a considerable reduction in Recommended value for the Finance based model. Another important observation is that the priority based model is better in distinguishing between various plans. This can be seen from Figure 7, which shows comparison of the recommended trust values corresponding to various plans from different models. In the non-hierarchical model, where all parameters have equal weights, trust values of all the plans (see column 2 of Table 6) fall in the range between and 0.52 (excluding 0.38) which makes it difficult to rank the CSPs. But in a priority based model with Finance / (Table 7), we can see that the range varies from to Thus we can rank the various service provider plans. Thus it is seen that with Finance as the main requirement, Gogrid Elite Dedicated Server, One, Amazon EC2 (Small, Medium, Large) and Cloud flare Enterprise plans are favourable whereas with as the main requirement One and Amazon EC2 Large would be preferable. Such a conclusion cannot be arrived at from Table 6. It may be noted that all the above results are subject to variation depending on the network load conditions. CSP and Server type TABLE 6 Values for 3 and 5 Model Parameters Three Parameters (, and ) Five Parameters (,,, and ) Direct Recommended Direct Recommended Gogrid Standard Dedicated Server Gogrid Advanced Dedicated Server Gogrid Ultra Dedicated Server Gogrid Elite Dedicated Server Enhanced One Enhanced Two One Two Amazon EC2 Small Amazon EC2 Medium Amazon EC2 Large Cloud flare Pro Cloud flare Business Cloud flare Enterprise Although the results have been described prioritizing Finance/ we would like to emphasize that the model utilizes most of the parameters listed by CSMIC to arrive at a trust value, and hence can be customized to get a trust value for a service provider by selecting parameters as per user requirement. For eg: a consumer who has security as a priority may not have focus on the. The model represented in Figures 3 and 4 can be modified by removing the respective parameter and can then be evaluated using the FIS to get the trust value of the service provider. For eg: the trust value obtained for Enhanced One considering based Recommended as shown in Figure 4 is (highlighted entry in Table 7), which increases to when we remove the Finance parameter while evaluating the trust value. Thus, the model parameters can be relaxed too as required by the user and the trust value of the CSPs can be estimated. 1109

9 TABLE 7 values for Hierarchical Models (Finance and based) CSP and Server type Finance based based Direct Recommended Direct Recommended Gogrid Standard Dedicated Server Gogrid Advanced Dedicated Server Gogrid Ultra Dedicated Server Gogrid Elite Dedicated Server Enhanced One Enhanced Two One Two Amazon EC2 Small Amazon EC2 Medium Amazon EC2 Large Cloud flare Pro Cloud flare Business Cloud flare Enterprise Figure 7 Comparison of Recommended Values 1110

10 7. Conclusion This paper proposes a hierarchical model to rate the various plans of CSPs considering,,, and parameters listed by CSMIC which provide a standardized method for measuring and comparing business services. Considering Finance as priority requirement results are obtained to compare the various plans of CSPs available in the market. Likewise by providing suitable priority to users can ensure that cloud applications are sufficiently secure. The paper also suggests the addition/dropping of parameters from the model as per the requirements of the consumer. 8. References [1] Mell P, Grance T, A NIST definition of cloud computing. National Institute of Standards and Technology. NIST SP [2] Siani Pearson, Privacy, and in Cloud Computing. HP Laboratories, Springer, June [3] Xin Liu, Gilles Tredan and Anwitaman Datta, A generic trust framework for large-scale open systems using machine learning. March [4] Mohamed Firdhous, Osman Ghazali and Suhaidi Hassan, Management in Cloud Computing: A Critical Review. International Journal on Advances in ICT for Emerging Regions, 2011, 04 (02): [5] Kui Ren, Cong Wang, and Qian Wang, Challenges for the Public Cloud. Illinois Institute of Technology, February [6] Somesh Kumar Prajapati, Suvamoy Changder and Anirban Sarkar, Management Model For Cloud Computing Environment. Proceedings of the International Conference on Computing. Communication and Advanced Network - ICCCAN [7]Liu Xin, beyond reputation: Novel trust mechanisms for distributed environments. A thesis report, [8] Supriya M, Venkataramana L.J, K Sangeeta and G K Patra, Estimating Value for Cloud Service Providers using Logic. International Journal of Computer Applications, Volume 48 No.19, June [9] Supriya M, K Sangeeta and G K Patra, Comparison of Cloud Service Providers Based on Direct and Recommended Rating. IEEE CONECCT, January [10] d5f13-f40e-47ad-b9a64f246cf7e34f, Cloud Service Management Index Consortium (CSMIC). Service Management Index Version 1.0 (PDF), September [11] Logic Toolbox User s Guide. [12] Wickremasinghe, B, Calheiros R.N and Buyya, R. CloudAnalyst: A CloudSim-Based Visual Modeller for Analyzing Cloud Computing Environments and Applications 24 th International Conference on Advanced Information Networking And Applications, Australia, April [13] GoGrid Cloud Hosting: Dedicated Servers, Physical Servers. [14] ions RackSpace: Dedicated Server, Managed Hosting and Web Hosting Configurations. [15] different entities. [16] types/pricing/ Cloudflare services. 1111