An Efficient Cloud Service Broker Algorithm

An Efficient Cloud Service Broker Algorithm 1 Gamal I. Selim, 2 Rowayda A. Sadek, 3 Hend Taha 1 College of Engineering and Technology, AAST, dgamal55@yahoo.com 2 Faculty of Computers and Information, Helwan University, rowayda_sadek@yahoo.com 3 College of Engineering and Technology, AAST, eng_hend_ocp@yahoo.com Abstract Cloud Computing introduces a new efficient way in providing and delivering the different services to the users who mainly care about two parameters; response time and cost. Since there are many Datacenters hosting the provided applications, the role of a service broker becomes very important in choosing the most suitable data center to serve the received request. Many policies introduced in Cloud Simulation s Service brokerage to choose the appropriate Datacenter based on proximity region, response time, or cost. However, considering one parameter comes at the expense of the other parameters. This paper proposes two algorithms to consider region, response time and cost parameters in order to choose best data center to lower the cost and response time. Two testing scenarios are considered to compare the proposed algorithms with three existing algorithms. The results show enhancements up to 30% in total cost, up to 10% in DC processing time and up to 8% in response time. 1. Introduction Keyword: Cloud Computing; CloudAnalyst; Service Broker. In the era of cloud computing, clients will pay only for the services they need whether it is hardware renting or using an application [1]. Many service providers now upgrade to Cloud Computing technology to lower their foundation and installation costs in addition to providing a fast and guaranteed services to their customers with the lowest price. Cloud services must lower the response time and the overall cost for any user request. Many algorithms were proposed to lower the Datacenter s response time. However to achieve that; tests need to be repeatedly applied to different proposed algorithms and methodologies which highly consumed costs and time in the real cloud environment. Cloud Simulation comes in hand in these situations, as it allows to test cloud computing different algorithms in many areas, control the environment, and totally free of charge[9,10]. Finally, the tested cloud will be ready to deploy in the real life. CloudAnalyst simulation which is based on Cloudsim toolkit is commonly used in cloud simulation [2]. This paper focuses on one main component in cloud architecture; called service broker. Service broker keeps a list of the available data centers with some information about each data center (like region, costs.. etc.) and helps in choosing the suitable data center to serve any received request. Choosing the right data center helps in saving response time and money. Many algorithms discuss how to choose the appropriate datacenter to serve a certain user request based on different parameters like the closest in region, the best response, or the cost[3]-[5]. Algorithms that consider only one of these parameters affect the others. So, an algorithm that considers most of these parameters is needed to select the most effective datacenter. This paper proposes two algorithms to select the data center with the best possible response and yet the lowest in Virtual Machine cost (VmCost) and Data transfer cost to serve the coming user request. The first algorithm chooses the best response time data center, and then finds the data center with the lowest cost in its region to serve the user request. The second algorithm chooses the best data center based on considering many of the data center parameters all together as it considers the total cost (VmCost and Data transfer cost) as well as network delay cost. Two scenarios were conducted to introduce heavy loads (during rush hours or in case of emergencies when a data center drops down) and light loads. These scenarios tested the proposed paper algorithms along with three former algorithms. The proposed algorithms show an enhancement in total cost, response time, and processing time comparing to other algorithms results. International Journal of Advancements in Computing Technology(IJACT) Volume 6, Number 1, January 2014 37

The rest of this paper is as follows, section II discusses the CloudAnalyst main components, section III discusses the current CloudAnalyst algorithms along with some proposed algorithms, section IV discusses the new proposed algorithm, section V investigates scenarios and their results, and section VI reveals the conclusion and the future work. 2. CloudAnalyst CloudAnalyst can model a real world cloud solutions to help cloud providers in testing their proposed environment before going live. CloudAnalyst is built directly over CloudSim toolkit [7]; it extends and adds many features to CloudSim like Internet component, CloudAppServiceBroker Component, GUI interface, etc. A brief discussion of these components and other is coming next to help in understanding later algorithms and scenarios of creating and handling the user requests. As shown in figure 1 the UserBase demonstrates a certain number of users creates and sends their requests, in the form of InternetCloudlet to the Internet component. The Internet component models the internet network around the globe in six regions by introducing transmission latency and data transfer delays. The internet component generates requests out of the InternetCloudlet, then it asks service broker used algorithms (CloudAppServiceBroker), for the selected data center ID. When Internet receives the ID of the chosen Datacenter from Service Broker, it sends the InternetCloudlet (Request) along with the serving data center ID (Chosen Datacenter) to DataCenterController. DataCenterController handles all the activities of one data center, including creating and destroying Vms, and also routes the user requests to the appropriate Vm according to the applied load balancer algorithm. The DataCenterController asks the used VmLoadBalancer algorithms for the appropriate Vm ID to serve the user request. DataCenterController submits the Cloudlet with the chosen data center ID and Vm ID to the simulation environment. Users (UserBase) Generates InternetCloudlet Receives Requests Response Internet Exchange Requests & Response Service Broker - Equal Distributer Service Broker - Cost Performance Service Broker Get Destination Response with Chosen DC/Vm (VmLoadBalancer) Get Vm DataCenter (DataCenterController) 3. Service Broker Algorithms Response with chosen Vm Figure 1. User Request Process Service Broker selects the appropriate Datacenter to serve the coming requests from UserBases. Thus, service broker effects on response time and cost of every Datacenter. Currently, there are only three implemented service broker algorithms in CloudAnalyst simulation [2]. 3.1. Service Proximity based routing Service Proximity selects the closest data center based on the network latency. The network latency comes from the predefined delay metric between different regions. In case there are two closed data centers in region, the algorithm selects a data center randomly [3]. The problem with this algorithm is being not considered the data center cost and it can overload a data center because it is the nearest one. 38

3.2. Performance Optimized routing When this service broker receives a new request it chooses the best response data center and send the request to it, the data center with the lowest total delay is considered the best response data center. The total delay is calculated using the following formula: T total =T latency + T transfer (1) Where T latency : is the network delay and it is calculated from the latency metrix. T transfer : is the time taken to transfer the size of data of a single request (D) from source location to the destination. T transfer = D / BW per user (2) BW peruser =BW total / Nr (3) And Nr is the number of user requests currently in transmission [2]. BW total is the total available bandwidth. This algorithm performs very well and gets the best performance if and only if all the data centers costs the same, which is not the case in the real world so algorithms that consider the cost were mostly needed [3]. 3.3. Dynamically reconfiguring router Scaling up and down the cloud environment helps in handling the variant workload user s requests [8]. Based on that a dynamic service broker policy is introduced as it extends any other service broker policy adding to them two options increasing and decreasing the number of the running Vms. In other words, this algorithm monitors the response time of the entire data center and if it exceeds a certain threshold a number of Vms created. Alternatively, reduce the number of Vms if the response time decreased by a certain threshold [2]. The current results are not promising and therefore this algorithm introduced so far by concept only. 3.4. VmCost based Service Broker Policy A new algorithm was proposed in [5] that consider the Vm cost in every datacenter, so the closest datacenter with the less cost will be chosen. The results from this algorithm were compared with the Service Proximity based routing algorithm in Cloud-Analyst. This algorithm gets lower cost but in the expense of response time. 3.5. Cost Effective Selection Cost Effective selection algorithm proposed an enhanced proximity-based routing policy that avoids the direct selection of nearest data center. If more than one data center is located in a region, the data center having less cost (considering only Data transfer cost) will be selected [4]. 3.6. Modified Cost Effective Selection This algorithm chooses the most two cost effective data centers, to have better performance [3]. There are no more information about algorithm implementation and no explanations how the algorithm sends the same user request to two data centers [3]. 4. A hybrid Cost Performance Service Broker algorithms Unlike the mentioned algorithms, the proposed algorithms will consider two parameters (Cost & Network /delay) instead of only one parameter in the developed algorithms. Combining these two parameters is carried out in a way to give a lower cost with acceptable response. Two algorithms 39

proposed; one divides the load equally on the best performance data center and lowest cost one. The second considers both cost and performance parameters when choosing the datacenter (DC). 4.1. Equal Distributer Service Broker Equal Distributer algorithm equally distributes the load between two data centers, the best performance and the lowest cost data center. Once the service broker receives a request, it searches for the best performance DC (BRDC) to serve that request, then uses the Vm cost parameter, it search for the DC with the lowest cost (LCDC) with one condition to be in BRDC region as shown in step (4) in the algorithm. In other words, obtain the best performance DC (BRDC) region and then find the DC with the lowest cost within the BRDC region. Figure 2 shows the algorithm flowchart. Next, the algorithm step by step: Algorithm 1: Equal Distributer Service Broker. 1: When Service Broker receives a new user request 2: Using equation (1) get Best Response Data center (BRDC) 3: SET dcregion = BRDC region 4: SET LCDC = Lowest Cost Data Center in dcregion 5: IF LCDC = = NULL 6: SET chosendc = BRDC 7: ELSE 8: SET chosendc = LCDC 9: END IF 10: RETURN chosendc; 4.2. Cost Performance Service Broker This algorithm modifies the former algorithm, and merges between the two parameters so the selection of the appropriate data center will be on both the total cost and the total latency. Cost parameter here differs from the former algorithm as it considers both the Vm cost and data transfer cost using equation (4), the first part of the equation calculates the time taken by VM to process the total number of requests instructions assigned to it (MIPS total / VM MIPS ), multiply it by VmCost to get the total cost. The second part, calculates the data transfer cost by multiplying the request s data size by data transfer cost. C Total = (MIPS total / VM MIPS ) * VmCost + Cl Datasize * DTCost (4) Where, C Total : Total cost, contains total data processing cost and total data transfer cost. MIPS total :Total number of instructions (MIPS) assigned to the data center. VM MIPS : Average assigned processing power to any Vm in the data center. Using average processing power helps in both cases if the Vms assigned a fixed resources or dynamic configuration. VmCost: is the cost of using the Vm per hr. Cl Datasize :User request data size. DTCost: Data transfer Cost. Performance parameter will be based network delay from equation (1). However, delay is measured in time unit and cost in money, so it is assumed that the delay will cost fixed rate for example 0.1 dollar per second. Figure 3 shows the algorithm flowchart. The algorithm is illustrated step by step as follows; Algorithm 2: Cost Performance Service Broker, Figure 3 1: When Service Broker receives a new user request. 2: FOR all DataCenters 3: SET totalmips += Requests MIPS 4: Using equation (1) to calculate the network delay and multiply it by 0.1 the predefined 40

value to get cost per sec. 5: Using equation (4) to calculate total cost of the current dc. 6: IF totalcost < mincost 7: SET mincost = totalcost 8: SET chosendc = currentdc 9: END IF 10: END FOR 11: RETURN chosendc; Setting the mincost to the max value available will make sure that the first chosendc will never be null and will take at the first round the value of first data center in the list. NewRequest NewRequest Get BRDC Set Mincost = integer.maxvalue Set dcregion = BRDC sregion Loop on Datacenter s list Get LCDC in dcregion Is it the last DC in list? False If LCDC is not Null False Set ChosenDc = BRDC Calculate TotalCost in Equ (4) True Set ChosenDc =LCDC False If totalcost<m incost True mincost = totalcost ChosenDc = CurrentDC ReturnChosenDC ReturnChosen DC Figure 2: Equal Distributer Service Broker Figure 3: Cost Performance Service Broker 41

Both algorithms considers the two parameters cost, and performance. The first one equally distributes the load between the best performance and lowest cost DC. The second put two parameters in one equation and get the best performance with the lowest cost. The reason for using two algorithms is to clarify that even when considering the same parameters, different scenarios need different ways of choosing the best datacenter, to get the best results possible. 5. Results and Discussion Two tests will be carried out to demonstrate two different scenarios in real world, light scenario and Heavy one. The simulated light scenario has two data centers serve five UserBases. The simulation of the heavy one has three datacenters serves twelve userbases. This second scenario is very heavy and choosing the wrong DC may cost a lot. 5.1. Scenario 1 Two DataCenters and Five UserBases The first scenario demonstrates two datacenters serve five UserBases (UB). It is designed as to be a sample of light load for testing. 5.1.1. Scenario 1 Configurations UserBases (UB) have different loads (Instruction per request), different peak hours and different data sizes per request. UBs are all in the same region as shown in Table 1. The data centers serving them will be in different regions with different VmCost and data transfer cost as shown in Table 2. Simulation duration is 1 day. Name Table 1. Scenario 1 UserBases Configuration Data size/hr Peak hrs MIPS per req. (Bytes) UB1 200 1-2 100 UB2 250 3-5 500 UB3 300 5-7 250 UB4 100 7-9 150 UB5 500 9-11 300 Table 2. Scenario 1 Datacenters Configuration Name Region Vm Cost Data Transfer Cost DC1 0 0.2 0.2 DC2 5 0.1 0.1 All the UserBases are located in region 2, simulate 1000 users in peak hours and 100 in non-peak hours, each user with 60 request per hour. Each Data center will host 5 VMs with memory 512 MB, BW 1000 Mbits/s, and storage 10000 MB. 5.1.2. Experiments Using Scenario 1 The results compare the two proposed algorithms with three other algorithms as shown in table 3, as it compare the overall cost, Data centers max processing time, and max overall response time. Figure 4, demonstrates the results in table 3. Three algorithms performed almost the same Service Proximity Based, Performance Optimized routing, and Cost-Performance service broker. Because the first algorithm, Service Proximity Based, chooses only the first data center DC1 and sends the entire requests to it, Performance Optimized routing, and Cost-Performance service broker send only 1 or 2% of the load to DC2 leaving DC1 very loaded. However in Vmcost based algorithm it chooses DC2 to serve all the users requests to achieve lowest price but with highest processing and best response time. 42

Table 3. Performance with light load; Scenario 1 Overall Cost ($) D.C Processing Time (Max in ms) Service Proximity Based 719.46 38.52 Performance Optimized routing 718.11 39.96 VmCost based Service 376.53 106.84 Equal Distributer Service Broker Cost Performance Service Broker 509.48 718.09 48.25 39.91 Overall response time (Max in ms) 416.98 514.38 588.72 546.50 512.63 Equal Distributer Service Broker stands in the middle between cost and response/processing time, it lowered the overall cost by 30% than the other three algorithms. It lowers the processing time by 65% and response time by 8% than Vm Cost Based algorithm. Cost Performance Service Broker didd not work well in this scenario as with light loads less calculations algorithms will act fast and better than complex one. 800 600 400 200 0 Service Proximity Based Performancee Optimized routing VmCost based Service Equal Distributer Servicee Broker Cost Performance Service Broker Overall Cost ($) DC Proc. Time (ms) Resp. Time (ms) Figure 4. Performance of the five service broker algorithms. Figures 5-7 show the Datacenters hourly usage for the five algorithms. Almost all the algorithms act the same except Equal Distributer Service Broker it distributes the load on the two datacenters as in Figure 5 and by this way lowering the cost and performance. Figure 7, shows the Datacenters hourly usage for three algorithms, Service Proximity Based, Performance Optimized routing, and Costor the Performance Service Broker, as they load only one DC (as DC1), as it is either the closest DC best performance DC. In Figure 6 VmCost algorithms used DC2 with the lowest cost. 600 Usage Per Hr 400 200 0 DC 1 DC 2 1 3 5 7 hrs 111 15 20 23 Figure 5. DataCenters Hourly Usage for Equal Distributer Service Broker. 43

Usage Per Hr 1000 500 0 DC 2 1 3 5 7 11 15 hrs 20 23 DC 2 Figure 6. DataCenters Hourly Usage for Vm Cost Based Service Algorithm Usage Per Hr 1000 500 0 DC 1 1 3 5 7 11 hrs 15 20 23 DC 1 Figure 7. DataCenters Hourly Usage for Three algorithms Putting the two Data Centers DC1 and DC2 in the same region (Scenario 1 \ ) willl even enhance the performance of the proposed algorithms as shown in table 4. Both algorithms lowered their total costs and response time, high processing time willl not quite affect the end user s requests however it serve in lowering the response time. Table 4. A Performance comparison between Scenario 1 & Scenario Overall Cost D.C Processing Overall response ($) Time (Max in ms) time (Max in ms) Equal Distributer Service Broker 509.48 48.25 546.50 Equal Distributer Service Broker Same Region 376.53 49.51 479.33 Cost Performance Service Broker Cost Performance Service Broker Same Region 718.09 509.53 39.91 59.70 512.63 423.44 5.2.. Scenario 2 Three DataCenters and Twelve UserBase The second scenario has heavier weight. It has three data centers (DCs) to servee twelve userbases (UBs). It is designed as to be a sample of heavy load for testing 1 \ 44

5.2.1. Scenario 2 Configurations UBs configurations are illustrated in table 5. DCs locate in region 0 and 3. DCs configurations are shown in table 6. Table 5. Scenario 2 Userbases Configurations Name Region Data size/hr Peak hrs MIPS per req. (Bytes) UB1 0 3000 1-3 500 UB2 2 5000 3-6 400 UB3 3 6000 6-9 100 UB4 4 1000 9-12 200 UB5 5 2000 12-15 250 UB6 0 3000 15-18 250 UB7 1 9000 18-21 100 UB8 2 4000 3-9 200 UB9 3 5000 21-24 300 UB10 4 3000 5-7 250 UB11 5 2000 11-13 300 UB12 1 1000 21-23 100 5.2.2. Experiments Using Scenario 2 Table 6. Scenario 2 Data centers Configuration Name Region Vm Cost Data Transfer Cost DC1 0 0.2 0.15 DC2 3 0.1 0.1 DC3 3 0.15 0.15 Although, the results in table 7 clarify the targeted enhancement the proposed algorithms, the Vm Cost algorithm still gives a very high response/processing time. Service Proximity ignores the cost factor; the performance optimizer comes in a second after Service Proximity. Cost-Performance and Equal Distributer proposed algorithms give the best possible response/processing time with low cost, lowering percentage is 6% in total cost, and 10% in response/processing time. The tested scenarios show that when comparing the performance of the two algorithms with three former algorithms, the paper algorithms give the best possible response/processing time with low total cost as targeted. Table 7. Performance with heavy load; Scenario 2 Overall Cost ($) D.C Processing Time (Max in ms) Overall response time (Max in ms) Service Proximity Based 1271.47 91.45 660.82 Performance Optimized routing 1268.71 92.89 670.26 VmCost based Service 1195.37 137.68 1340.31 Equal Distributer Service Broker 1250.09 97.68 660.26 Cost Performance Service Broker 1240.08 83.50 660.54 6. Conclusions and future work Both algorithms consider the two parameters cost, and performance. The first one equally distribute the load between the best performance and lowest cost DC. The second put two parameters in one equation and get the best performance with the lowest cost. The Hybrid Cost - Performance Service Broker proposed algorithms are proposed to select the destination datacenter by considering two important parameters, the response time and total Cost. Examining the proposed algorithms versus the 45

existed algorithms with two different scenarios proved the best response possible with the lowest cost of the proposed ones. Results show enhancements with a percentage up to 30% in total cost, 8% in response time, and 10% in processing time comparing to other already developed algorithms. In future two further steps can be done; first add possibility of introducing different types of user requests and handle them dynamically by proposing a dynamic configuration algorithm that increase or decrease the resources allocated to each Vm running in the data centers to handle those different types. Second, propose a new optimized load balancer to lower the response time for any datacenters. 7. References [1] Wei Zhao, Yong Peng, Feng Xie, Zhonghua Dai, Modeling and Simulation of Cloud Computing: A Review, IEEE Asia Pacific Cloud Computing Congress (APCloudCC2012), pp. 20-24, 2012. [2] B. Wickremasinghe, CloudAnalyst: A CloudSim-based Toolfor Modelling and Analysis of LargeScale Cloud Computing Environments, MEDC Project Report University of Melbourne, 2009. [3] D. Limbani, B. Oza, A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation, International Journal of Computer Technology and Applications, IJCTA, Vol 3 Issue 3, 2012. [4] D. Chudasama, N. Trivedi, R. Sinha, Cost effective selection of Data center by Proximity-Based Routing Policy for Service Brokering in Cloud Environment, International Journal of Computer Technology and Applications, IJCTA, Vol 3 Issue 6, 2012. [5] J. Park, D. Lee, B. Kim, J. Huh, S. Maeng, Locality-Aware Dynamic VM Reconfiguration on Map Reduce Clouds, 21st international symposium on High-Performance Parallel and Distributed Computing,HPDC 12, pp. 27-36, 2012. [6] Cloud Analyst, link at: http://www.cloudbus.org/cloudsim. [7] R. Buyya, R. Ranjan, and R. N. Calheiros, Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities, Processing of the 7thHigh Performance Computing and Simulation Conference (HPCS09), IEEE Computer Society, pp. 1 11, 2009. [8] RajkumarBuyya, Rajiv Ranjan, Rodrigo N. Calheiros, InterCloud: utility-oriented federation of cloud computing environments for scaling of application services, 10th international conference on Algorithms and Architectures for Parallel Processing, pp. 13 31, 2010. [9] G. Sakellari, G., Loukas, A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing, Simul. Modell. Pract. Theory 2013, in press. [10] M. Aggarwal, Introduction of Cloud Computing and Survey of Simulation Software for Cloud, Research Journal of Science & IT Management, RJSITM, Vol 3, No 01, 2013. 46