International Journal of Pure and Applied Mathematics

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1 Volume 119 No , ISSN: (on-line version) url: Criteria based Optimal Resource Placement Model using Analytic Hierarchy Process in Cloud Environment T Sunitha Rani Research Scholar, Bharathiar University, Coimbatore Head, Department of Computer Science, M.O.P. Vaishnav College For Women, Chennai , India Dr. Shyamala Kannan Research Supervisor, Bharathiar University, Coimbatore Associate Professor, P G & Research Department of Computer Science, Dr.Ambedkar Govt. Arts College, Chennai , India ABSTRACT Cloud computing services operating on data centers, face the problem of where and how much resource to be placed at data centers. Criteria based cloud resource placement model is developed by making use of Analytic Hierarchy Process technique. The primary objective of this technique is to place resources in suitable data centers spread across multiple geographies. The model presented in this paper helps in the identification of a hypothetical Data Centre that provides the best user experience for Resource Placement. Functional aspects such as latency, placement cost and resilience are considered as criteria for resource placement. These three performance metrics are prioritized by attaching weights based on business characteristics and user needs. Appropriate resource placement can be achieved by satisfying all specified criteria. Hence, the suggested model focuses on identifying appropriate placement at reduced cost, low latency and high resilience. KEY WORDS Cloud Computing, Resource Placement, Criterion, Prioritization, Analytic Hierarchy Process INTRODUCTION Cloud Service providers use data centers spread across multiple geographies to house cloudservices and cloud based resources. Any placement which provides a service with no/very low latency, maximum resilience and minimized placement cost is the most preferred one. This paper aims at solving the problem of resource placement at an appropriate data center of the cloud environment with Analytic Hierarchy Process (AHP) method of Multi Criterion Decision Making (MCDM) [2] [9] [11]. Criteria-based resource placement model for cloud environment is developed using MCDM-AHP technique. The problem is to choose the most favourable data center to place the resource which satisfies all specified constraints as enormous repositioning cost is incurred in changing the optimal placement. The challenge is to place resources for service at the best data center. The main objective of this paper is to place resources at an appropriate data center. This primary objective is met by proposing an algorithm which helps in 515

2 achieving maximum benefit for various applications that are latency sensitive as well as computationally complex with no hard deadlines for completion. The proposed algorithm monitors parameters under consideration by assigning weights to them and making pair wise comparison to arrive at an optimal solution which provides the best user experience. REVIEW OF LITERATURE Changing the optimal placement incurs a huge repositioning cost [14]. This problem was addressed using lazy algorithm and shortest cycle algorithm which provides an optimal resource placement to meet the demand and minimize the repositioning costs. The dynamic service placement problem was investigated with control framework based on model predictive approach and game theory. It served as a control mechanism that reduced service provider cost and identified the competition among various service providers [10] [3]. A scheduling model that optimized virtual cluster placement [4] was proposed to suggest an optimal deployment using cloud prices trends. The deployment cost was not considered in the problem. Service placement at nodes with sufficient capacity and network connectivity was addressed using sub-graph matching mechanism [1] [8]. Graph index framework was adopted for frequent label updates and partial ordering of labels to obtain appropriate results. Resource placement problem was with loss function and min-cost flow to find a pertinent location that optimizes the system operation [15] [12]. Placement based on request prediction focused on dynamic redeployment and avoided delayed reconfiguration [6] [7]. Multi-objective ant colony system was also adopted for virtual machine placement [13]. PROBLEM FORMULATION A cloud environment includes service providers and users. Appropriate placement provides the best user experience. Placement of resources in cloud environment could be described as follows: Given a set constraints with appropriate weight, finding out where the best placement of resources can happen is the problem considered. The best choice can be obtained using AHP of MCDM. Complexity of the problem increases with the increase in the number of criterion (parameters). The weight of each parameter is determined with linear scale. This can be achieved with AHP. The focus is on how resources can be placed in a cost-effective way within this model. Overview of Analytic Hierarchy Process A formal description of this AHP is provided. The fundamental AHP Scale (Satty s scale) [5] is used for pairwise comparison to identify the less dominant of any two criteria and ultimately helps to determine how many times more the dominant member of the pair is. Reciprocal value is used to compare the less dominant criterion with more dominant one. Hence Matrix takes values of the form if i th criterion is more important than j th criterion. 516

3 if i th criterion is less important than j th criterion. if both i th and j th criteria have equal importance. Also these values satisfy the constraint. These intensities are transformed to elements of matrix for pair-wise comparison using linear relation. For criteria, comparisons are made. General Computation of Eigenvalues and Eigenvectors Suppose a vector is transformed by a matrix B then a vector Y is obtained as. (1) A vector Y parallel to Vector X can be written as where is a scalar. Then the eigenvector of a square matrix B is a vector X such that where (order n).. (2).. (3) The system of simultaneous linear algebraic equations are given below The vanishing of the determinant of the coefficients results in the solution for the system of equations specified above. 517

4 The characteristic equation is used to find the eigenvalues, where is the identity matrix. For each eigenvalue obtained above equations provide a solution for the vector. Here concern is only with real matrices as preferences for the criterion are considered. is obtained from the normalized principal eigenvector to evaluate consistency index and consistency ratio. Consistency Index is given by.. (4) Consistency Ratio is given by.. (5) where is the random consistency index. Consistency ratio helps in judging whether the preference is consistent or not. The value of is obtained from the Satty s book which displays the order of random matrix and the corresponding random consistency index. OPTIMAL RESOURCE PLACEMENT MODEL Optimal Resource Placement (ORP) is model designed to embrace all service providers. ORP enables providers to solve all issues arising out of placement. This model can be extended to include n metrics that exist in a resource placement problem. Here three essential metrics namely the latency, resilience and placement cost are considered. These metrics contribute to the best placement of resources in cloud environment. Optimal placement point can be achieved by choosing these performance metrics as the standard criteria. The solution methodology adopted is generic and can also be used if any other metric is added or existing metric is removed. Though many researchers have addressed the problem of resource placement with different algorithms, they have not addressed the problem of choosing the best data center from the existing ones. Choosing the best data center is essential as relocation cost is too high for a changeover. Hence the problem of resource placement has been approached in a new dimension. METHODOLOGY The developed methodology is based on Analytic Hierarchy Process. It applies a decomposition synthesis approach to solve the data center selection problem. The overall goal or objective is placed at level 1, criterion at level 2 and the alternatives at level 3. Figure 1 shows the hierarchical representation of goal, criteria and alternatives at 3 different levels. Level 1 portrays the overall goal of locating an appropriate data center for resource placement. Level 2 represents the criterion to be considered for the selection of the best data center. The last level is identified as the highest level that gives the best among alternatives. Level: 1 Overall Objective Identifying Appropriate Data Center to place resources Level: 2 Criteria Latency Resilience Placement Cost 518

5 Figure1: Hierarchical Representation of Goal, Criteria and Alternatives ALGORITHMS Algorithm 1: Analytic Hierarchy Process 1: 2: 3: 4: 5: 6: 7: 8: Algorithm 2: Decomposition Synthesis approach 519

6 1: Overall goal is to identify the optimal datacentre to place resource 2: List relevant performance metrics (criteria) 3: Place the goal, criteria and decision alternatives at various levels in the hierarchy. 4: Prioritize each criterion by assigning weights 5: Pair wise comparison is made with criteria. 6: Rate all DCs (Alternatives) at level3 after pair wise comparison. 7: Overall score of each alternative is calculated. 8: Assimilate assigned weights and DC s rating, to determine the final score. 9: The overall decision is made by selecting the DC with highest rating. COMPUTATIONAL RESULTS Preference of each criterion against the other is displayed in Table1.1 as. According to the linear preference weightage, 1 indicates equal preference, 5 - a strong preference, 7 - a very strong preference and 9 - Extreme preference. The value is the reciprocal of. Consistency Index, consistency ratio and the values shown in Table 1.2 are calculated using equations 2, 4 and 5. Criteria preference with respect to data centers is portrayed in Table 1.3. Importance of each criterion after pairwise comparison is depicted in Table 1.4. After evaluation, alternatives are ranked according to the criterion prioritization as in table 1.5. Table 1.1: Preferences of Criteria Placement Criteria Latency Resilience Cost Latency Placement Cost Resilience Table 1.2: CI, CR and λ for Criteria with respect to DCs Consistency Consistency Criteria Index Ratio Latency Placement Cost Resilience Table 1.3: Criteria Preference with respect to DCs Criteria Placement Latency Resilience Preferences Cost DC

7 Criteria Data Center International Journal of Pure and Applied Mathematics DC DC Table 1.4: Importance of Criteria Criteria Result Latency Placement Cost Resilience Table 1.5: Rankings of Alternatives DC\Criteria Latency Cost Resilience Result DC DC DC The choice of alternatives portrayed in figure 1.2 is based on consistency index. Figure 1.3 demonstrates criteria preference and figure 1.4 depicts the overall ranking of the data centers based on all three criteria weightage. It is clear from the figure 1.4 that placement cost has the highest preference followed by resiliency and then latency. Consistency Index DC DC DC Figure 1.2: Choice of the alternative Consistency Index Placement Cost Latency Resiliency Figure 1.3: Criteria importance 521

8 Data Center International Journal of Pure and Applied Mathematics Rank DC1 DC3 DC2 Figure 1.4: Ranking of alternatives CONCLUSION Resource placement in data centers plays a vital role in providing pertinent services to the users. Competing goals with different priorities contribute to optimal resource placement. The developed model provides placement recommendations ensuring workload demands are met in cloud environment. Effectiveness of the suggested solution that optimizes the desired objective is demonstrated. Experimental results exhibit the efficiency of the proposed model in identifying the suitable datacentre for the resource placement. It is evident for the above result that data center (DC1) is selected for optimal placement as the highest rank value is obtained for DC1. Data center DC3 gets the next higher value and DC2 is REFERENCES [1] Bo Zong, Ramya Raghavendra, Mudhakar Srivatsa, Xifeng Yan, Ambuj K. Singh, Kang- Won Lee, Cloud Service Placement via Subgraph Matching, IEEE 30 th International Conference on Data Engineering, Chicago, ICDE 2014, [2] Ge Wang, Samuel H. Huang, John P. Dismukes, Product-driven supply chain selection using integrated multi-criteria decision-making methodology, International journal of production economics, /$ Elsevier B.V., doi: /s (03) [3] Hendrik Moens, Eddy Truyen, Stefaan Walraven, Wouter Joosen, Bart Dhoedt, Filip De Turck, Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud, Journal of Network and Systems Management, Volume 22 Issue 4, October 2014, Pages [4] Jose Luis Lucas Simarro, Rafael Moreno-Vozmediano, Ruben S and I M Llorente Dynamic Placement of Virtual Machines for Cost Optimization in. Multi-Cloud Environments, IEEE Explore, International Conference on High Performance Computing & Simulation, 2011 [5] Klaus D Goepel, Comparison of Judgement Scales of Analytical Hierarchical Process A New Approach, Business Performance Management Singapore, Preprint of an article submitted for consideration in International Journal of Information Technology and Decision Making, World Science Publishing Company,

9 [6] L. Qiu, V. N. Padmanabhan, and G. M. Voelker. On the placement of web server replicas. In IEEE INFOCOM, Anchorage, AK, USA, April [7] Liang Quan, Zhang Jing, ZhangYong-hui and LiangJiu-mei, The placement method of resources and applications based on request prediction in cloud data center, Information Sciences, Volume 279, 20 September 2014, Pages [8] Moritz Steiner, Bob Gaglianello, Vijay Gurbani, Volker Hilt, W. D. Roome, Michael Scharf, and Thomas Voith, Network-Aware Service Placement in a Distributed Cloud Environment, SIGCOMM 12, August 13 17, 2012, Helsinki, Finland. ACM /12/08 [9] P. Kousalya, G. Mahender Reddy, S. Supraja, V. Shyam Prasad, Analytical Hierarchy Process approach An application, of engineering education Mathematica Aeterna, Vol. 2, 2012, no. 10, [10] Qi Zhang Quanyan Zhu Mohamed Faten Zhani Raouf Boutaba and Joseph L Hellerstein, Dynamic Service Placement in Geographically Distributed Clouds, IEEE Journal on Selected Areas in Communications, Vol. 31, No.10, October [11] S.D. Pohekar, M. Ramachandran, Application of multi-criteria decision making to sustainable energy planning A review, Renewable and Sustainable Energy Reviews 8 (2004) [12] Y. Rochman, H. Levy, and E. Brosh. Resource placement and assignment in distributed network topologies. In IEEE INFOCOM, Turin, Italy, April [13] Yongqiang, Haibing Guan, Zhengwei Qi, Yang Hou, Liang Liu, A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement, Journal of Computer and System Sciences Volume 79, Pages [14] Yuval Rochman Hanoch Levy, Eli Brosh, On Dynamic placement of resources in Cloud Computing- Technical Report, Yuval_ on _dynamic.pdf [15] Yuval Rochman, Hanoch Levy, Eli Brosh, Efficient resource placement in cloud computing and network applications, ACM SIGMETRICS Performance Evaluation, Volume 42 Issue 2, September 2014, Pages

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