Comparison of Trust Values using Triangular and Gaussian Fuzzy Membership Functions for Infrastructure as a Service

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1 Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC Comparison of Trust Values using Triangular and Gaussian Fuzzy Membership Functions for Infrastructure as a Service A. Supriya M 1, B. Sangeeta K 1 and C. G K Patra 2 1 Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of CSE, Bengaluru, India. {m_supriya, k_sangeeta}@blr.amrita.edu 2 CSIR Fourth Paradigm Institute, Council of Scientific and Industrial Research (Formerly CSIR C-MMACS), Principal Scientist, Bengaluru, India. [email protected] Abstract Cloud computing has emerged as a paradigm to deliver on demand resources to enable the customers with access to their infrastructure and application services on a subscription basis. In recent times, increasing number of people carry out variety of different activities on the cloud. Two important issues that arise in cloud computing relate to storing and securing data, and monitoring the use of the cloud by the service providers. Trust helps to build this consumer confidence in such open and often anonymous environments. It provides a reliable environment for customers or businesses to carry out transactions with cloud providers. The amount of trust that the trusting agent has in the trusted agent is measured by a Trustworthiness scale. Trustworthiness is determined by the trust levels specified as numeric or non-numeric values. Trust is one of the most fuzzy, dynamic and complex concepts in both social and business relationships. In this paper, a hierarchical trust model has been used to compare the trust values of various cloud service providers for five levels of trustworthiness using triangular and Gaussian fuzzy membership functions based on Infrastructure as a Service. Index Terms service providers, hierarchical model, trustworthiness, membership functions, cloud analyst, direct trust, recommended trust. I. INTRODUCTION 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]. Among the various service models available in cloud, Infrastructure as a Service (IaaS) plays a vital role. In IaaS, consumers can deploy and run software, with a Cloud Service Provider (CSP) controlling the underlying cloud infrastructure [2]. The cloud provider supplies a set of virtualized infrastructural components such as virtual machines (VMs) and storage on which customers can build and run applications. The application will eventually reside on the VM and the virtual operating system. The customer rents the resources rather than buying and installing them. Often these resources are dynamically scalable, paid for on a usage basis. Examples for IaaS include Amazon EC2 and S3. Due to the large scale and openness of these systems, a Elsevier, 2014

2 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. Normally, in on open distributed environment like cloud computing, a trustworthiness system called as a ranking or rating system helps to conduct business with trustworthy people. Many well established sites like Amazon, e-bay, Yahoo etc. use such trust rating systems that automatically assign trustworthiness to all agents they interact with. Trustworthiness is the level of trust, a customer has on a CSP and is measured using a scale system defined based on numeric values, non-numeric values or stars [3]. There are many trustworthiness scales proposed in literature. Wang and Vassileva [4] [5] has proposed a Bayesian network based model for determining trust, where a node has two values: satisfying and unsatisfying denoted by 0 and 1 respectively. A decision function for determining trustworthiness, where trust is represented by 1 and mistrust is represented by -1 is discussed by Aberer and Despotovic in [6]. In [7], a trust metric based on the amount of agent satisfaction and the credibility of agent feedback has been developed. Kamvar, Schlosser and Garcia-Molina [8] use normalized trust values between 0 and 1. Four different levels of trustworthiness ratings namely very trustworthy, trustworthy, untrustworthy and very untrustworthy has been used in [9] to rate the systems. A non-numeric rating expressed using stars with each additional star denoting a higher rating is proposed in [10]. Huaizhi and Mukesh Singhal [11] measure trust using six different trust scales ranging between -1 and 4 representing distrust, ignorance, minimal, average, good and complete. Comparison of the several fuzzy trust methods for P2P environment using complete trust, good trust, average trust, minimal trust and distrust as the 5 levels of trustworthiness is presented by Hongwei ShengSheng, Jianga, Wang and Zhiwei [12]. In [13], Xia, Jia and Zhu use malicious node, low trustworthy node, trustworthy node and complete trustworthy node as the scales to measure the trust of the nodes in mobile adhoc networks. The trust model described in [14] and [15] uses very poor, poor, good, excellent and outstanding as five levels of trustworthiness to rate the CSPs and their plans. The trust parameter is evaluated using triangular fuzzy membership function. The model used in [15] compares the cloud service providers and their various plans based on the Direct and Recommended trust considering Agility, Finance and Performance as parameters. In [16], Li, Yang, Srikanth and Zhang demonstrate a systematic comparator called CloudCmp, that compares the performance and cost of cloud providers considering elastic computing, persistent storage, and networking services offered by a cloud as parameters. A framework to measure the Quality of Service (QoS) and prioritize Cloud services based on the Performance Indicators set by CSMIC is described in [17]. In this paper, we use five levels of trustworthiness namely very low, low, medium, high and very high for a hierarchical framework which extends the model described in [15] with Security and Usability as additional parameters. This model is then used to estimate the trust values using both triangular and Gaussian fuzzy membership functions. Section II describes the Hierarchical model based on Security and Finance. CSPs, their plans and infrastructure details are described in Section III. Simulation and results from Cloud Analyst are presented in Section IV. The membership functions, trustworthiness levels and the results of the hierarchical model are explained in Sections V and VI. The paper is concluded in Section VI. II. HIERARCHICAL MODEL DESCRIPTION The model parameters are chosen based on the attributes defined by Service Measurement Index (SMI) [18]. These include Accountability, Agility, Assurance, Financial, Performance, Security, Privacy, and Usability. Each of these attributes consists of a set of Key Performance Indicators (KPIs) which describe the data to be collected for measurement. KPIs are quantifiable measurements, agreed to beforehand, that reflect the critical success factors of an organization. They will differ depending on the organization. However, of the KPIs, not all are measurable i.e. quantifiable, some are qualitative in nature. Based on the KPI that make up the attributes in evaluating the CSPs, the trust evaluation model described in [15] uses the CSMIC parameters viz. Agility, Financial and Performance KPIs to estimate trust and is implemented in 2 stages. The first stage is the implementation with the help of Mamdani Fuzzy Inference System [19] which evaluates Performance, Financial and Agility parameters. The Performance parameter is evaluated by considering the number of processors and the RAM capacity available with the CSP. Financial parameter is evaluated using Virtual Machine (V.M) Cost, Storage Cost, and Data Transfer Cost. Agility parameter takes number of Data Centers (DCs), Storage space and number of V.Ms as its inputs. In the second stage, the FIS takes the above three parameters to obtain the trust rating for each plan of the CSP. The trust values so obtained are referred as Direct trust as they are based only on the observation of infrastructure facilities available with a CSP. The model block diagram (Direct trust) is shown in Fig

3 Fig. 1 Direct Trust Model Block diagram But, in large online communities, Recommended trust is often useful as it is based on the prior experiences of other users. To obtain an estimate of Recommended trust, the infrastructure facilities of each service provider are simulated using Cloud Analyst [20] which provides the DC processing time and Total cost as the outputs. These outputs are fed to the Performance parameter and Finance parameter respectively (in addition to the above mentioned inputs) and the FIS is re-run to get the Recommended trust value between 0 and 1. The model discussed above provides an estimate of the Trust for each of the CSPs which can be used to compare the various CSPs and their plans. But, in this model no emphasis has been given to security which is an important parameter too. So the non-hierarchical model of Fig. 1 is extended to include two more parameters - Security and Usability and is then converted to a hierarchical model in which Security or Finance can be chosen as a priority. This hierarchical model reduces the total number of fuzzy rules used in evaluation of the trust parameter. When the user or customer of the cloud needs to have a highly confidential transaction, Security may be his concern and he may not focus on the Financial aspect. For, such scenario the user may prefer the model shown in Fig. 2 whereas if his concern is mainly on the Finance rather than Security he may choose the model shown in Fig. 3. The Security parameter is described in terms of the Physical Security, Internal Security and Network Security levels available with the cloud provider, while the Usability 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 in Table I. TABLE I. MODEL PARAMETERS KPI Parameter Contributing Attributes Agility Finance Performance Security Usability No. of Physical Units (DCs), No. of V.Ms, Memory Size V.M Cost, Storage Cost, Data Transfer Cost No. of Processors, Processor Speed (RAM) Physical Security, Internal Security, Network Security Understandability, Easability, Flexibility The hierarchical models shown in Fig. 2 and 3 have three stages and work as follows: If Security has a higher priority then as shown in Fig. 2 Security and Agility FIS gets evaluated as one set and Performance, Financial and Usability FIS gets evaluated as another set to obtain the trust value corresponding to a CSP plan. Likewise, if Finance parameter is more important, then as shown in Fig. 3, the model evaluates the Finance and Agility FIS separately and the Performance, Security and Usability FIS separately, and finally the trust 739

4 value of the CSP plan is obtained. Direct and Recommended trust values from these models can be evaluated using Cloud Analyst and the Fuzzy Inference System as described above. These estimated values can be used to compare the plans offered by different CSPs. Fig. 2 Hierarchical Model based on Security III. CSPS AND THEIR PLANS Fig. 3 Hierarchical Model based on Finance In this paper, three real service providers those offer IaaS are identified and the various plans offered by them are used to test the models described in the previous section. The names of the identified service providers 740

5 Rackspace, Gogrid and Amazon are changed randomly to SP1, SP2 and SP3 with the plan names as P1, P2, P3 etc. These plans have been compared for their trustworthiness using the hierarchical trust model described in Fig. 2 and 3. Table II and Table III show the different plans, Data Center (DC) locations across the globe and the input parameters corresponding to each service provider [21, 22 and 23]. This information has been used to set up the DCs available with each CSP during the simulation. TABLE II. CSPS AND THEIR LOCATION CSP Name of the Plans DCs and their Location S1 4 plans: P1, P2, P3, P4 3 DCs : two in U.S.A, one in Europe S2 4 plans: P1, P2, P3, P4 8 DCs: Five in U.S.A, two in Europe, one in Asia S3 3 Plans: P1, P2, P3 6 DCs: three in U.S.A, one in Europe, two in Asia TABLE III. VARIOUS PLANS OF CSPS WITH THEIR PARAMETER DETAILS Agility Financial Performance Security Usability 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 Security Internal Security Network Security Understandab ility Easability Flexibility S1-P S1-P S1-P S1-P S2-P S2-P S2-P S2-P S3-P S3-P S3-P IV. CLOUD ANALYST SIMULATION AND RESULTS Cloud Analyst simulation of CSPs mentioned in Section III needs the User Bases (UB) to be defined randomly across the regions in the globe as described in [14] and [15]. The regions considered are the six continents labelled R0 through R5 as listed in Table IV. This UB description is kept constant throughout the simulation to analyze the performance of different CSPs under the same load. A sample Cloud Analyst simulation results after simulation of S3-P2 is shown in Fig. 4. Fig. 4 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. The simulation also lists the maximum and minimum response times against each of the User Bases (as seen in Fig. 4). Since this response time is random for every simulation, it has not been 741

6 TABLE IV. USERBASE DESCRIPTIONS 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 Fig. 4 S3-P2 after Simulation considered in the evaluation of Recommended trust. 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 trust. V. MEMBERSHIP FUNCTIONS AND TRUSTWORTHINESS LEVEL A. Membership Functions The membership function (MF) of a fuzzy set is a generalization of the indicator function in classical sets. In fuzzy logic, it represents the degree of truth as an extension of valuation. These membership functions can be used to represent the trust, a consumer has in the service provider [3]. Types of MFs could be triangular, trapezoidal, sigmoid, Gaussian, generalized bell curve etc. The hierarchical model described in section II is 742

7 TABLE V CLOUD ANALYST SIMULATION RESULTS CSP and Server type DC Processing Time (ms) Total Cost ($) S1-P S1-P S1-P S1-P S2-P S2-P S2-P S2-P S3-P S3-P S3-P used to estimate the trust value of the service providers using triangular and Gaussian MFs. The obtained trust values are compared and an appropriate MF is suggested for the model discussed. B. Trustworthiness Level A trustworthiness scale provides a standard measuring system that allows the trusting agent (customer) to measure the level of trust that he has in the trusted agent (service provider). The linguistic definition of each level provides the meaning of confidence or the trust that the trusting agent has in the trusted agent. The trustworthiness levels can be divided into two groups, positive and negative trust. A negative trust rating is given to a trusted agent who delivers a bad service or who contradicts his mutual agreement with the trusting agent. A positive trust rating is assigned to a trusted agent who is willing to or has the capability required in delivering some, if not all, of the services that are expected by the trusting agent. The trustworthiness value depends on the number of levels of trustworthiness used in the model. This paper uses five levels of trustworthiness which is described below: The five levels of trustworthiness are described as very low, low, medium, high and very high. These five levels correspond to 10%, 30%, 50%, 80% and 90% respectively. When the trusting agent assigns the trustworthiness level as very low, it means that he has around 10% confidence on the trusted agent. This would happen when the trusted agent has not met even the basic requirements of the trusting agent. A 50% confidence from the trusting agent indicates a medium confidence on the trusted agent s behaviour while a very high (> 90%) trustworthiness value demonstrates a complete confidence on the trusting agent. These levels of trustworthiness can be represented using fuzzy membership functions. Fig. 5 shows the triangular membership function (as represented in Matlab). Here, x axis represents the trust values ranging between 0 and 1 and the y axis corresponds to the membership values ranging between 0 and 1. Similarly, Fig. 6 shows the Gaussian membership functions for the trust parameter with five levels of trustworthiness. Fig. 5 Five levels of Trustworthiness Triangular MF 743

8 Fig. 6 Five levels of Trustworthiness Gaussian MF VI. RESULTS AND DISCUSSION In this section, we estimate the trust values obtained using the hierarchical models shown in Fig. 2 and 3. The models are implemented in simulink which in turn calls the FIS created for each parameter. Execution of the simulink model gives a set of Direct and Recommended trust values corresponding to each plan of a CSP. It is observed that the Recommended trust values are higher than the Direct trust values [15] as these include the recommendations or references collected from other parties in the initial trust. Therefore only the Recommended trust values have been analyzed in this paper by varying the membership functions as depicted in Table 6. The Recommended trust values using triangular and Gaussian MFs based on Security are listed in columns 2 and 3 of Table 6. From these, the following inferences can be drawn: The trust value of the CSP listed here have been evaluated by considering Agility and Security parameters as one group and the remaining parameters as other group as shown in Fig. 2. It is observed that the increase in Trust values exhibit the influence of Agility and Security parameters provided by the CSP listed in Table 3, which demonstrates a consistency in the estimated trust values from the model. The coefficient of variations for security based Recommended trust is and for triangular and Gaussian MFs respectively. This indicates that both triangular and Gaussian MFs are suitable for the model described in this paper as there is not much variation in the trust values. But, Gaussian MF gives a better result for Recommended trust as the trust values seen in the table is high for most of the plans, say for example, S1-P1 and S1-P2 has a considerable increase in the trust value which makes it easy for the customer to choose an appropriate plan. Therefore having chosen a reasonable level of security, Finance can be considered as a decisive parameter in choosing a particular plan of a CSP. It is also seen from the table that for some of the plans, say for example S2-P2 and S3-P3 the recommended trust values go down for the Gaussian MF compared to triangular MF. This is due to the reason that the output of a Gaussian MF is very smooth. But it could be observed that the variation in reduction is very less and it is ignorable. Most of the service providers offer a minimum of 50% security. At the same time, none seems to assure a 100% security. It is the consumers responsibility to choose an appropriate service provider as per their requirement. The Recommended trust values based on Finance are listed in columns 4 and 5 of Table VI, which lead to the following observations: The coefficient of variation for the model is and for Recommended trust based on triangular and Gaussian MFs. As in Security based model, here too the variations are ignorable and Gaussian MF gives a better result compared to triangular MF because of the smoothness in the change in values. Also, Gaussian MFs are more adequate in representing uncertainty in measurements. Similar to the Security based model, in the Finance based model too, qualitative ranking of all the CSPs does not change which shows the consistency of the model in capturing the behaviour of the CSPs. A. Customization of the Model In this section, we describe the customization of the hierarchical model described in Section II. For eg: the consumer who has security as a priority may not have focus on the financial aspect. So, the model represented in Fig. 2 can be slightly modified by removing the Finance parameter which leads to the 744

9 TABLE VI. RECOMMENDED TRUST VALUES FOR TRIANGULAR AND GAUSSIAN MFS CSP and Server type Recommended Trust Security based Recommended Trust Finance based Triangular MF Gaussian MF Triangular MF Gaussian MF S1-P S1-P S1-P S1-P S2-P S2-P S2-P S2-P S3-P S3-P S3-P model as shown in Fig. 7. This model is evaluated for the trust values of the CSPs as discussed in the earlier sections and the results obtained are listed in Table VII. Likewise, the model parameters can be relaxed as required by the user and the trust value of the CSP can be estimated. Fig. 7 Hierarchical Trust model based on Security (without Finance Parameter) Table VII lists the trust values of the security based model of Fig. 7. Column 3 is taken from the Table 6 for ease of comparison. On comparing the trust values, the following observations could be drawn: For the plans like S2-P1 and S2-P2, the Recommended trust values are and considering Finance parameter whereas the corresponding trust values are and when the Finance parameter is removed. This shows that the level of security offered by them is very high but since their Finance level is also high. The model of Fig. 2 compromises on the security and reduces the trust value. But, the model shown in Fig. 7, captures the same and gives a better result. For some plans like S1-P1 and S1-P2, the change is in reverse direction. The table shows less trust values in column 2 compared to the value in column 3. The reason for this is that the Finance level offered by them is very cheap and the level of security is comparatively low, hence the model of Fig. 2 balances these parameters and gives a better rating for these plans. But on using the model of Fig. 7, the difference is noticeable. 745

10 TABLE VII. TRUST VALUES FOR GAUSSIAN MF WITH AND WITHOUT FINANCE PARAMETER CSP and Server type Security Based-Gaussian MF (without Finance Parameter) Recommended Trust Security Based-Gaussian MF (with Finance Parameter) Recommended Trust S1-P S1-P S1-P S1-P S2-P S2-P S2-P S2-P S3-P S3-P S3-P This shows that, if the Finance is not the priority of the consumer then he can drop that parameter from the model in Fig. 2. Thus the hierarchical model can be customized as per user priorities. VII. CONCLUSION This paper uses a hierarchical model to rate the various plans of CSPs considering Agility, Financial, Performance, Security and Usability 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 by using triangular and Gaussian MFs. Likewise by providing suitable priority to Security, users can ensure that cloud applications are sufficiently secure. The computed trust values corresponding to a particular plan is the measure of the level of trust that the trusting agent has in the trusted agent. The trust value however would depend on the appropriate trustworthiness level and the membership function used in building a trust model. REFERENCES [1] Mell P. and Grance T., A NIST definition of cloud computing. National Institute of Standards and Technology. NIST SP [2] Siani Pearson, Privacy, Security and Trust in Cloud Computing. HP Laboratories, Springer, June [3] Elizabeth Chang, Tharam Dillon and Farookh K. Hussain, Trust and Reputation for Service-Oriented Environments: Technologies for Building Business Intelligence and Consumer Confidence. John Wiley & Sons, Ltd., [4] Wang Y. and Vassileva J., Trust and Reputation model in Peer-to-Peer /publications/120_wang_y.pdf, [5] Wang Y. and Vassileva J., Bayesian Network Trust Model in Peer-to-Peer Networks. Proceedings of Second International Workshop on Agent and Peer to Peer Computing, Melbourne, 2003, pp [6] Aberer K. and Despotovic Z., Managing Trust in a Peer-2-Peer Information System. Peer%20Information%20System.pdf, [7] Xiong L. and Liu L., A Reputation based Trust Model for Peer-to-Peer ecommerce Communities [8] Kamvar S.D., Schlosser M.T. and Garcia-Molina H., The EigenRep Algorithm for Reputation Management in P2P Networks. download?doi= &rep=rep1&type=pdf,

11 [9] Rahman A.A. and Hailes S., A Distributed Trust Model. DistributedTrustSystem.pdf, [10] Cornelli F., Damiani E., Vimercati S., Vimercati S.D.C., Paraboschi S. And Samarati P., Choosing Reputable Servants in a P2P Network [11] Huaizhi Li and Mukesh Singhal, Trust Management in Distributed Systems, %2Fruimtool.com%2Flibrary%2Fcommon%2Ftrust_management_in_distrib_systems.pdf&ei=naMwUuPRJMiLrQf 6qYDgDQ&usg=AFQjCNEVgeKnXmZN2-4dUC6k6o7QCVnNEg, February [12] Hongwei Chen, ShengSheng Yu, Jianga Shang, Chunzhi Wang, Zhiwei Ye, Comparison with Several Fuzzy Trust Methods for P2P-based System. International Conference on Information Technology and Computer Science, China, July 2009, pp [13] H. Xia Z. Jia L. Ju Y. Zhu, Trust Management Model for Mobile Ad hoc Network based on Analytic Hierarchy Process and Fuzzy Theory. IET Wireless Sensor Systems, December 2011, Vol. 1, Iss. 4, pp [14] Supriya M., Venkataramana L.J., K. Sangeeta and G. K. Patra, Estimating Trust Value for Cloud Service Providers using Fuzzy Logic. International Journal of Computer Applications, Volume 48 No.19, June [15] Supriya M, K Sangeeta and G K Patra, Comparison of Cloud Service Providers Based on Direct and Recommended Trust Rating. IEEE International Conference on Electronics, Computing and Communication Technologies, India, January 2013, pp [16] Ang Li, Xiaowei Yang, Srikanth Kandula and Ming Zhang, CloudCmp: Comparing Public Cloud Providers. Internet Measurement Conference 2010, Melbourne, Australia, November [17] S. K. Garg, S. Versteeg and Buyya R., A Framework for Ranking of Cloud Computing Services. Future Generation Computer Systems, June 2012, Volume 29, pp [18] Cloud Service Management Index Consortium (CSMIC). Service Management Index Version 1.0 (PDF), September [19] Fuzzy Logic Toolbox User s Guide. [20] 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 [21] servers.php GoGrid Cloud Hosting: Dedicated Servers, Physical Servers. [22] RackSpace: Dedicated Server, Managed Hosting and Web Hosting Configurations. [23] different entities. Ms. Supriya M has done her B.Tech in Computer Science and Engineering under Bharathiar University and M.Tech in Computer Science and Engineering under Dr. M G R University, Tamilnadu. She is currently working as an Assistant Professor at Amrita Vishwa Vidyapeetham, Amrita School of Engineering, Bengaluru, Karnataka, India. Her research interests include Operating Systems, Trust Management etc. Dr. K. Sangeeta has done her Ph D in Mathematics from Indian Institute of Science, Bangalore. She has worked for 6 years in CSIR, Center for Mathematical modelling and Computer Simulation, Bangalore, India. She has published papers in the areas of Finite Difference and Finite Element methods, Cryptography, Security, Trust Management. Presently she is working as an Associate Professor at Amrita Vishwa Vidyapeetham. Amrita School of Engineering, Bengaluru, Karnataka, India. Dr G K Patra, is currently working as a Principal Scientist, at the CSIR Fourth Paradigm Institute (Formerly CSIR C-MMACS) and also an associated professor at the Academy of Scientific and Innovative Research. He has obtained his Ph. D. degree from Berhampur University in year His research interest includes information security and high performance computing. He has more than 25 papers in various International journals and peer reviewed con-ference proceedings. His major contribution includes planning, designing and setting up of one of the India s largest supercomputing facility. He is also an adjunct faculty at the Amrita School of Engineering. 747