CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT

81 CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT 5.1 INTRODUCTION Distributed Web servers on the Internet require high scalability and availability to provide efficient services to millions of users on the Web. A collection of Web servers is used as a pool of replicated resources to provide concurrent services to the users. The Web servers can be deployed at different locations over WAN to share information or services and collectively serve the clients from different locations. Distributed Web servers provide high availability (Jianniong cao 2003), that is, when a server encounters failure, other servers sustain the service. The load balancing in WAN environments is more time consuming and involves the interactions among remote servers for gathering load information, negotiating load reallocation and transporting the workload (Jianniong cao 2003). In load balancing, different approaches are used for process migration with different optimal flavors. Process migration is not an easy job, that is, it imposes a lot of burden and processing effort in order to track each of the processes in the servers (Kashif Bilal et al 2005). All load balancing approaches for distributed Web servers involve frequent message exchanges between the request distributor and the servers or clients to detect and exchange load information. The message exchanges increase the network traffic in a Web service system (Jianniong cao 2003). Mobile agent paradigm seems to be promising for developing applications in open, distributed and heterogeneous environments such as the

82 Internet and supports load balancing in parallel and distributed computing. In WAN, mobile agents need to spend enormous time to traverse all the servers. Moreover, the load information that is collected becomes obsolete by the time the mobile agents report to the source server (Jianniong cao 2003). When a source server transfers a job to another server, the latter probably has been overloaded and redirects the job to another server. Due to the stale load information, the job redirection is transferred across a chain of servers over a long distance until it reaches an appropriate server to accept it. Thus, the response time is greatly prolonged, which is undesirable. To resolve this problem, a new decision making approach for load balancing is proposed and designed for WAN which uses Decentralized Mobile Agent s framework (WLDMA). In WLDMA, each of the servers processes the client requests independently and interacts with the other servers periodically to share the workload. A client can have access to the Web server which is located geographically closer so as to minimize the WAN delay. It is a dynamic load balancing algorithm which redistributes the client requests among the less loaded servers during the execution time. To model these load balancing problems, several features from the parallel and distributed computation environments are captured: These include the workload of awaiting processes at each server (i.e. queue size); the performance of the servers relative to the other servers in its WAN cluster the computational requirements of each of the workload components. the delays and bandwidth constraints of the servers and network components involved in the exchange of workloads and

83 the delay demanded by the servers and the network on the message exchange (Ghanem et al 2004, Majeed Hayat 2003). This chapter describes the proposed WLDMA load balancing algorithm, its frame work, operation and performance in WAN Web server's environment. 5.2 THE WLDMA FRAMEWORK Another new proposal is introduced to redistribute the requests to the less loaded Web server in WAN. The overall architecture of the WLDMA framework is as shown in Figure 5.1. The architecture considers the Web servers to be widely separated from each other in WAN environment. There is a physical or virtual connection between the servers so that, any Web server can communicate with any other Web servers using the mobile agents. The clients usually send requests to the Web server that is located geographically closer. Still, the client requests may be re-distributed among the Web servers (according to the WLDMA algorithm) to ensure better response time to clients. Though the architecture shown below has only three Web servers in a WAN, the WLDMA architecture is perfectly scalable to employ n Web servers in WAN.

84 Figure 5.1 WLDMA framework Generally, the load on the overloaded server is transferred to less loaded server to enhance the system's throughput. Thus, the system resources get the maximum possible utilization. The servers can be heterogeneous in terms of hardware configuration, operating systems, and the processing power. The capacity of a server may change at runtime due to the variation of the workloads. In our discussion, all the Web servers are considered as equivalent in their capabilities. In the WLDMA scheme, the job redistribution decisions are taken by the individual Web servers depending on the status of the jobs in the queue. 5.3 THE WLDMA LOAD BALANCING SCHEME The client requests arrive at the Web servers according to a Poisson distribution (Ghanem et al 2004). The Web servers process the client requests

85 and respond to the clients. But, in order to share the loads among the servers almost equally, the mobile agents are sent from/to the Web servers to distribute the workload. Different functions in this scheme can be defined and encapsulated in mobile agents. The mobile agents carry the functions to other servers and execute them on the servers. A mobile agent can be proprietary to a server where it is created and perform dedicated operations for the owner. The mobile agents can interact with each other by direct data exchange. They can interact using the stigmergy technique in which the mobile agents can collect the information from the traces left in the environment by one another (Minar.N et al 1999).The stigmergy is an indirect method for the interaction between the mobile agents, which can reduce network traffic and achieve quick decision making (Jianniong cao 2003).A mobile agent can gather the information placed on a server by other mobile agents who have previously visited here. The WLDMA frame work specifies four types of mobile agents: Load Status Agent (LSA). Load Gathering Agent (LGA) Job Reallocation Agent (JRA) Threshold Agent (TA) The LSA constantly monitors the queue size of the Web server. Each server has its own LSA. It is a stationary agent that motionlessly sits at the server and responsible for monitoring the workload on local servers. Each server deploys and sends an LGA (consisting of queue size of the source server) to all the other Web servers every 150ms. The LGA travels around the servers, collects the load information from the servers and propagates it to visiting servers. The TA is a decision making agent that also motionlessly sits at the server and collects all the LGA

86 from other servers. The TA calculates the adaptive threshold after receiving the LGA from the servers connected in the WAN cluster. It compares its queue size with the queue size of the other Web servers in the WAN cluster, selects the Web server with the minimum queue size at that time with the updated information. The JRA is activated by the TA and transfers jobs to the selected server which satisfies the condition (5.1).The JRA aids in job redirection. A server can dispatch the JRA to the other system if it is required. The mobile agent approach can minimize the network traffic and enhance the flexibility of a load balancing mechanism. The functional architecture of the WLDMA is as shown in Figure 5.2. The heavily loaded server attempts to transfer the job to lightly loaded server in sender-initiated policy. This policy is incorporated in WLDMA strategy. Figure 5.2 WLDMA functional architecture

87 5.4 THE WLDMA MATHEMATICAL MODEL Let A, B, C, be the Web servers located at different locations in WAN. Let queuelength A, queuelength B, queuelength C, be the queue sizes of the Web servers A, B, C, respectively, at a given point of time. The mobile agents are sent from A, B, C, to each other. The Web server which sends a mobile agent to other Web servers is called as the Source and the Web server where those mobile agents are received and manipulated is called as the Destination. The mobile agents carry the queue size of the Source. This value is compared with its value by the Destination, and job re-direction is performed based on the following algorithm: Step 1: If queuelength Destination queuelength Source2 & & queuelength Sourcen ) then, > (queuelength Source1 & Step 2: Compute q x such that q x = min (queuelength Source1, queuelength Source2 queuelength Sourcen ) Step 3: Compute n s uch that n = (queuelength Destination q x )/2 Step 4: Transfer the last n jobs from queuelength Destination to q x server, if it holds condition (5.1) A job 'j' on server 'x' is reallocated to a remote server 'y' only when: j i = 1 P ix > j rt x + i = 1 j _ rp xy + Pi y + P iy (5.1) i = n

88 Where, p ix = Processing time of i th request at Web server x. j_rt x = Transmission time of j th request by Web server x. j_rp xy = Propagation time of j th request from Web server x to Web server y. p iy = Processing time of i th request at Web server y. p jy = Processing time of j th request at Web server y. The purpose of computing 'n' is to redistribute the jobs in the order of their entry at the server side. In the existing WAN load balancing schemes, the job reallocation is done only when the workload on a server exceeds the local threshold value (Majeed Hayat et al 2003). In the WLDMA, the job reallocation is based on the adaptive threshold where the node knows which node has the minimum load and decides to send a process to this node, unless its load after transferring the process becomes larger than the threshold in the distributed Web server system. 5.5 PERFORMANCE EVALUATION To study the performance of the WLDMA scheme in WANs, simulation software was developed in C++. The simulator simulates the work of the mobile agents enabled, distributed Web server on a single PC. The environment lets the users to specify the parameters of the Web server system during simulation and displays the performance of the load balancing scheme on the simulated system. The performance metrics such as throughput, average response time and load deviation for WLDMA scheme are analyzed. In this simulation, client requests are generated and sent to the servers. The server receives the client requests independently. If a server is overloaded, the requests are redirected to less loaded server according to the

89 WLDMA scheme. The performance metrics of the WLDMA scheme is compared with the scheme without load balancing. The parameters and their default values used in WLDMA simulation model are summarized in the Table 5.1. Table 5.1 Simulation parameters and their default values used in WLDMA scheme S.No Simulation parameter Default value 1 Number of Web servers n 2 Task processing time 10 ms 3 Propagation delay 50 ms 4 Average transmission delay 20 ms 5 Mobile agents Round trip delay Time 150 ms 6 Data transmission rate 10Mbps The simulation parameters governing the events are summarized below Web Servers are the number of servers in a network that can process the requests from the clients. These servers are widely separated across the WAN environment. Task processing time is the time taken only for executing the request. Propagation delay is the time required for a request to travel from one point to another. Transmission delay is the time taken from the start of request reception to the end of request reception. Mobile agent round trip delay time is the time taken by the Mobile agent to travel between the servers on the network.

90 In this simulation, the performance of a load balancing scheme is assessed using the following criteria Load distribution: The load on the server is denoted by the number of requests processed by the server. The load deviation is defined as the difference between average workload and actual workload on the replica. The deviation on the load distribution is calculated to show the effect of load balancing. Throughput: The overall throughput of the Web server cluster is measured by the number of requests processed per second. Average Response Time: The average response time is the time taken by the server to process the client requests. The load distribution generated by the WLDMA scheme and the scheme without load balancing on three servers for different total number of requests at different moment is shown in Table 5.2. The minimum load deviation indicates that the workload is distributed equally among the replicas The WLDMA scheme has lower average load deviation and distributes client requests more evenly onto the Web servers. Simulation result shows that the average load deviation of WLDMA compared to the without load balancing scheme is less. Throughput and response times of WLDMA are also better than the scheme without load balancing. The Figure 5.3 shows the comparison of the WLDMA throughput with the scheme without load balancing. It shows that the WLDMA scheme can obviously improve the throughput, when the number of servers in the WAN cluster is increased. The rise and fall in the throughputs are related with the variations in the processing time for the requests. The Figure 5.4 shows the comparison of the WLDMA response times with the scheme without load balancing.

91 Figure 5.3 Comparison of WLDMA throughputs with the scheme without Load balancing Figure 5.4 Comparison of WLDMA average response time with the scheme without load balancing

92 The load distribution generated by the WLDMA and the scheme without load balancing on four servers at different moment is shown in Table 5.3. The overall average deviation among the Web servers becomes less when the number of servers in the WAN cluster is increased. The performance of load distribution on five servers using WLDMA and the scheme without load balancing is shown in the Table 5.4 Table 5.2 Load distribution on three servers Total no of requests 100 200 300 400 500 600 700 800 900 1000 Load balancing using WLDMA Server1 41 101 89 145 200 274 255 282 264 244 Requests/ Server2 20 60 68 123 122 182 189 238 312 315 server Server3 39 39 143 132 178 144 256 280 324 441 Average deviation 8.89 22.89 28.67 7.78 29.78 49.33 29.56 19.11 24.0 71.78 Overall average deviation 29.18 Total no of requests 100 200 300 400 500 600 700 800 900 1000 Without load balancing Server1 52 132 62 156 200 320 306 316 234 145 Requests/ Server2 9 60 68 112 92 136 103 181 312 315 server Server3 39 8 170 132 208 144 291 303 354 540 Average deviation 16.22 43.56 46.67 15.11 49.78 80.0 86.89 57.11 44.0 137.78 Overall average 57.71 deviation

93 Table 5.3 Load distribution on four servers Total no of requests 100 200 300 400 500 600 700 800 900 1000 Server1 38 64 62 98 128 225 206 230 198 145 Load balancing using WLDMA Requests/ Server Server2 Server3 22 26 60 39 55 77 92 112 92 148 106 124 143 161 162 213 169 260 285 350 Server4 14 37 106 98 132 145 138 240 280 220 Average deviation 7 12 16.5 17.5 16.5 37.5 34.5 35 44.75 67.5 Overall average deviation Total no of requests 28.88 100 200 300 400 500 600 700 800 900 1000 Without Load balancing Server1 50 81 62 146 180 290 206 330 198 145 Requests/ Server2 10 60 55 92 40 106 143 161 114 285 Server Server3 26 39 40 112 148 124 291 169 260 505 Server4 14 20 143 50 132 80 60 140 328 65 Average deviation 13.5 20.523.75 29 42.5 70 86 65 76.5 145 Overall average deviation 57.18

94 Table 5.4 Load distribution on five servers Total no of requests 100 200 300 400 500 600 700 800 900 1000 Server1 29 49 52 98 122 215 166 230 198 175 Requests/ Server2 18 50 63 72 84 85 143 169 134 220 Load balancing using WLDMA Server Server3 26 39 58 102 118 114 193 175 202 356 Server4 14 32 82 60 122 81 97 104 238 105 Server5 13 30 45 68 54 105 101 122 128 144 Average deviation 6 7.6 10 16 24.8 38 36.8 35.8 37.2 70.4 Overall average deviation 28.26 Total no of requests 100 200 300 400 500 600 700 800 900 1000 Server1 37 61 52 126 148 260 166 289 198 175 Without Load balancing Requests/ Server Server2 Server3 10 26 50 39 63 30 72 102 58 118 85 114 143 236 169 175 134 202 220 425 Server4 14 20 110 60 122 81 97 104 238 105 Server5 13 30 45 40 54 60 58 63 128 75 Average deviation 11.5 12.4 18.2 27.2 35.2 56 54 59.4 39.2 98 Overall average deviation 41.11

95 The comparison of average response times of the locally fixed threshold with adaptive threshold on three servers in the WAN cluster is shown in the Figure 5.5. Simulation result shows that the adaptive threshold provides better performance than the locally fixed threshold. Each server has fixed with 75 requests as threshold for testing purpose. Each server maintains log information. It gives details about the request_id, the status of the allocated and reallocated Web requests to that server. The rise and fall in the response times related with the variations in the processing time for the requests. Figure 5.5 Comparison of average response times of the locally fixed threshold with adaptive threshold scheme on three Web servers. 5.6 CONCLUSION The WLDMA framework provides a foundation to develop efficient load balancing schemes on a wide range of Web server systems from

96 clusters to the Internet. It is a dynamic load balancing scheme which redistributes the client requests among the Web servers during execution. This redistribution is done by transferring the tasks from the heavily loaded processors to the lightly loaded processors with an aim to minimize the response times of the requests. The WLDMA load balancing approach possesses several advantages. The decision making process is decentralized, and the response times improve as the number of Web servers increases. The use of mobile agents rewards with the merits of high flexibility, low network traffic and high asynchrony. As the mobile agents can travel host to host in a network and because of their ability to survive network disconnections, they offer an interesting approach to meet the goal of load balancing. None of the Web servers remains idle at any time while other replicas are busy processing requests. The WLDMA system is perfectly scalable. The adaptive threshold provides a better performance than the locally fixed threshold. The resource estimation policy in the WLDMA is decentralized, which provides an infrastructure for exchange of the nodes' state information. Still, this method has some limitations. The issue of job transfer from an overloaded Web server to another Web server still persists, which is tedious to handle. The WLDMA algorithm redirects jobs only when the mentioned equation holds good, and all the servers remain busy for an equal duration. The presence of random delays in inter-node communication and load transfers significantly alter the expected performance of the load balancing strategies. The WLDMA load balancing policy is under fixed delay assumptions, so, the policy will not perform as expected when the delays are random. In future work, the size of the jobs will be considered as an essential factor for selecting the processes for migration. This algorithm works more effectively for all the servers that have an equal capacity.