International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 4, July-Aug 2014

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4 selection process and how the selection process is done according to the proportion weight of the data center. The pseudo code of the proposed algorithm is mentioned below. Pseudo code 1: (Data Center Selection) Input: Region number Output: Destination DC name Function: getanydatacenter(region) regionallist regionaldatacenterindex.get(region) if regionallist is not NULL then listsize regionallist.size(); if listsize == 1 then dcname regionallist.get(0) else Assign the weight to the entire data center datacenterid proportionaldcselection(listsize) dcname regionallist.get(datacenterid); end if end if return dcname end function In the above pseudo code, data centers are assigned for resource allocation. Resource allocation using the proposed policy is discussed below. The pseudo code is written for convenience, assuming that there are three data centers available in the region. Pseudo code 2: (Algorithm with respect to proportion selection ) Input: listsize, Data Center Detail Output: Destination Data center ID Function: proportionaldcselection(listsize) // if list size is 3 as there are 3 data center x0 Proportion Weight of 1 st DC; x1 x0 + Proportion Weight of 2 nd DC; x2 x1 + Proportion Weight of 3 rd DC; i++; // ( we are taking I as a static variable which is incremented with every call of the function) if ( i < x0 ) datacenterid datacenterid of 1 st datacenter else if ( i > x0-1 && i < x1 ) datacenterid datacenterid of 2 nd DataCenter else if ( i > x1-1 && i < x2 ) datacenterid datacenterid of 3 rd DataCenter else i 0 Return datacenterid ; End Function The proposed algorithm aims to show the effectiveness of proportion weight to select the data center in order to achieve better resource utilization and remove the disadvantages of random selection. V. RESULTS AND DISCUSSION The proposed policy has been implemented in the Cloud Analyst simulation tool, which is based on the CloudSim (7) extensible simulation toolkit. The simulation configuration is as: Parameter Value Used UB Name UB1 Region 2 Request Per User Per Hour 60 Data Size Per Request 100 Peak hour start(gmt) 3 Peak hour end (GMT) 9 Avg Peak Users Avg Off Peak Users DC 1 No Of VM 80 DC 2 No Of VM 40 DC 3 No Of VM 20 VM Image Size MB VM Memory 1024 MB VM Bandwidth 1000 bps DC 1 No Of Physical Machine 20 DC 2 No Of Physical Machine 10 DC 3 No Of Physical Machine 5 DC Memory Per Machine 2048 MB DC Storage Per Machine MB DC Available BW Per Machine 4000bps DC No Of Processors Per 4 Machine DC Processor Speed 1000 MIPS DC VM Policy Time Shared User Grouping Factor Request Grouping Factor 1000 Executable Instruction Length 250 Load Balancing Policy Throttled Table 1: The Cloud Analyst Configuration for simulation The entry for number of virtual machines, as mentioned in the table 1 for DC 1, DC 2 and DC 3 is 80, 40 and 20. So the considerable proportion weight is in the ratio 4:2:1. Therefore, using the proposed algorithm, DC 1 is assigned to process the first 4 cloudlet, DC 2 is assigned to process the next 2 cloudlet and DC 3 is assigned to process the last cloudlet, out of 7 first cloudlets and the whole selection process is done in a repeated manner to process the entire set of workloads. Simulations run using the same configuration for each of the two existing and the proposed broker policy reveal an improvement in the results. The results include an overall response time summary, response time by region and data centre request servicing time. The throttled load balancing policy is being used to manage the load across the VM in the data centers.the summary of the results from the three simulations with three service broker policies: closest data ISSN: Page 107

5 centre, Optimized Response Time and the Proposed Proportion Based Algorithm is shown in table 2 below: Table 2: Results for overall response time and DC processing time Over All Response Time DC processing time Closest Data Center Optimize Response Time Proposed algorithm The table clearly shows that the values of overall response time and DC processing time for the proposed algorithm is far better than that for the other two existing broker policies. Graph for the same is shown in figure 2 below: DC 1, DC 2 and DC 3 are quite comparable when using the proposed policy, which show a better utilization of all the data centre. Graph for the same is shown in figure 2 below, which is showing the comparable processing time of proposed policies Closest Data Center Optimize Response Time Proposed Algorithm Figure 3: Bar chart for individual DC processing time VI. CONCLUSION AND FUTURE SCOPE Figure 2: Bar chart for overall response time and DC processing time for the three broker policies Table 3 shows the processing times of data centers individually for each of the three broker policies. Table 3: Result of individual data centre processing time Closest Data Center Optimize Response Time Proposed Algorith m DC 1 Processing Time DC 2 Processing Time DC 3 Processing Time Table 3 reveal the DC processing times of all the data centres using the three policies. If we consider the closest data center, the gap of processing time among all the data center is much more. When we consider the optimize response time as service broker the gap of processing time is also available. The data center with high capacity (DC1) finishes their task earlier and remains ideal and data center with low processing capacity takes more time accordingly. When the proportion based broker policy is being considered the values have greatly improved for DC 2 and DC 3. The workload of DC 2 and DC 3 is shifted to DC 1, whose capacity to process the request is more than the others. The DC processing time for The major advantage of cloud computing is the availability of computing and storage resources in the form of various services, on a pay-per-use basis. Also, the clients are free from the worry of knowing about the underlying hardware that is servicing their requests. In order to maintain this feature, it is necessary to efficiently utilize the available resources. Response time is the major governing factor when considered from the point of view of the user. A better response time and data centre request servicing time account for service efficiency, and the dependent directly on the choice of an appropriate data centre. If the correct data centre in terms of data processing time and response time is chosen, the request can successfully be serviced, without any delays. The proposed broker policy is a move in this direction of perfection. It makes use of the concept of proportion weights in order to assign the workload to data centers. Whereas the existing broker policies based their decisions on the location or randomly or optimality of data centers, the proposed policy takes into consideration the efficiency of underlying hardware, where a greater number of hardware machines (though of the same configuration) mean greater number of virtual machines can be created on that underlying hardware configuration and hence a larger number of cloudlets that they can handle by that data center. With implementation of proposed data center selection policy, the resource utilization of data center can be done effectively. Also we got a great improvement in the overall response time, the regional request servicing time and the data centre processing time using the proposed service broker policy. The proposed algorithm considers different data centers that are of the same configuration. The future work in this regards can be done by maintaining a proportion for data ISSN: Page 108

6 centres in such a way that distinct hardware configurations are also considered. REFERENCES [1] VMWare. Virtualization overview. White paper. Palo Alto : VMWare. p. 11. [2] A Survey of Load Balancing Algorithms in Cloud Computing Al. Brar, Harmandeep Singh, Thapar, Vivek and Kishor, Kunal. 3, Ludhiana : s.n., June 2014, International Journal of Computer Science Trends and Technology, Vol. 2, p. 4. ISSN: [3] Wickremasinghe, Bhathiya. CloudAnalyst: A CloudSimbased Tool for Modelling and Analysis of Large Scale Cloud Computing Environments. CSSE department, University of Melbourne. Melbourne : s.n., p. 44, MEDC Project Report. [4] Modeling Local Broker Policy Based on Workload Profile in Network Cloud. Sandhu, Amandeep and Kaur, Maninder. 8, Banur : s.n., August 2013, International Journal of Science and Research, Vol. 2, p. 5. ISSN: [5] Cost effective selection of Data center by Proximity- Based Routing Policy for Service Brokering in Cloud Environment. Chudasama, Devyaniba, Trivedi, Naimisha and Sinha, Richa. 6, 2012, International Journal of Computer Technology & Applications, Vol. 3. ISSN: [6] Cost Effective Selection of Data Center in Cloud Environment. Dash, Manoranjan, Mahapatra, Amitav and Chakraborty, N R. 1, 2013, International Journal on Advanced Computer Theory and Engineering, Vol ISSN: Page 109

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