29 Second International Conference on Computer and Electrical Engineering Two Hierarchical Dynamic Load Balancing Algorithms in Distributed Systems Iman Barazandeh Dept. of Computer Engineering IAU, Mahshahr branch Mahshahr, Iran barazandeh_i@yahoo.com Seyed Saeedolah Mortazavi Dept. of Electrical Engineering Shahid Chamran University Ahwaz, Iran moratazavi_s@scu.ac.ir Abstract- In this paper two new methods for load balancing in distributed systems are proposed. Both methods are based on hierarchical structure. Hierarchical structure provides better load management because it allows algorithms to balance the loads in two levels of groups and nodes. Since they have centralized load balancing mechanism, they have lower communication overheads too. These methods are dynamic and have simple implementation. The first method use biasing process to allocate weights called biases. Biases are determined based on the current load state of the groups and nodes. Second method improves algorithm in a way that the group or node with minimum load state have priority on others to receive tasks from load balancers in a specific time slice. Comparative study of these methods is done for both homogeneous and heterogeneous nodes and it shows better performance than existing conventional algorithms. Keywords: Load Balancing; Distributed Systems; Biasing. I. INTRODUCTION One of the primary goals of distributed systems is effective use of their resources. An imbalance load distribution on the existing nodes in a distributed system reduces the performance of the system. Load balancing is a strategy to direct an arriving task to the specific node to improve the performance of the whole of the system. Recently, in order to reduce communication overheads researchers use hierarchical structures. The hierarchical approach is necessary for large distributed systems for the following reasons: i) excessive message overheads may be incurred for each node of a large distributed system to collect global state information; and ii) state information from remote nodes will be imprecise due to significant message propagation delays []. In this structure several nodes form a group. For instance, all or some of the nodes in a LAN may form a group. Joining and leaving groups can be done in several ways [4-5, ]. Depends on what is the base of decisions for load balancing, these algorithms are categorized into two major types. If the load balancing method uses the current state information of the system in its decisions, it will be called dynamic method. Otherwise it will be called static method [3-6, 9]. Simple algorithms like Random and don t use any information of current state of nodes for load balancing. They only distribute arrival loads randomly or orderly on the nodes. These two algorithms are belonged to Static category. The advantages of static methods are simplicity in implementation and low communication overheads. Since, static methods don t need to maintain and process system state information; they have Low communication overheads []. The main disadvantage of static methods is that they don t adapt themselves with the changes of the entire system. But in dynamic methods, the decision making for load distribution is done based on the current state of the system. Hence, in order to receive and update this information in load balancer, exchanging of information of system is necessary. It is obvious that dynamic methods have better performance than static methods because it changes the distribution configurations based on the changes in the system state. But their better performance causes more complicated algorithm structure and more communication overheads. Load balancing methods could be classified in different ways [3]. If the load balancing mechanism handled by one node and only that node do load balancing, method is called Centralized method. But, if the load balancing mechanism handled by several nodes (and those nodes do load balancing), method is called Distributed (Decentralized) method. Centralized methods can support larger systems and have lower communication overheads but they are less reliable [9]. Many different algorithms are proposed in load balancing literatures. Another way of classification is to divide load balancing algorithms to Classic and Intelligent algorithms. Classic algorithms include all methods that do not use artificial intelligence. e.g. simple algorithms like Random and, and more complicated algorithms like Bidding [7] and Drafting [8]. Designers of intelligent 978--7695-3925-6/9 $26. 29 IEEE DOI.9/ICCEE.29.253 56 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.
algorithms [4-6, 9] believe that an effective load balancing scheme requires correct and timely knowledge on the global system state. However the global state of a large scale distributed computing system changes swiftly and dynamically. Intelligent algorithms sometimes benefits from fuzzy logic or genetic algorithm to take uncertainty into consideration in decision making. Implementation of classic algorithms is sometimes easier than intelligent algorithms. But the common goals of both of them are effective load distribution, lower communication overheads and therefore maximum throughput, utilization and minimum response time. It is a challenging question to chose. Intelligent load balancing algorithms show better performance of as Ref. [4-6, 9], and they are more complex. Now, is it worthy to prefer complex intelligent algorithms to classic algorithms or not. It is beyond of this paper subject. In this paper we proposed methods that are amalgamation of the two advantages of static and dynamic methods: simplicity of static methods and adaptiveness of dynamic methods. Centralized load balancing is employed; hence, they have low communication overheads. The structure used in this method is hierarchical. Usage of the hierarchical structure has two advantages. The first is reduction of the communication overheads and the other is doing load balancing in two levels of groups and nodes [2]. The first method distributes the loads with giving specific weights to groups and nodes, based on current state of system. This method is called Dynamic Biasing. The second method finds the nodes with minimum load and transmits tasks to those nodes. This method is called Minimum Load State (MLSRR). This paper organized as follows. Section II describes underlying distributed system structure used for proposed algorithms. Section III presents two new biasing methods: and Minimum Load State (MLSRR). Section IV presents the results of comparison study of proposed methods. Section V summarizes this paper. II. SYSTEM MODEL In this paper, it is supposed that underlying distributed system has hierarchical structure. Fig. shows this structure. This structure is near to structures that are used in ref. [4,5]. In the designed structure, load balancing is done in two levels: group level, which is done by Global Load Balancer (GLB), and node level, that is done by Local Load Balancer (LLB). This case cause significantly decrease communication overheads that aroused from collecting of the state information. Several nodes make one group. In any group, there is one node as designated representative (DR). DRs do local load balancing and communicate with the GLB. Nodes of each group are connected only to their group s DR. Task that is allocated to any group and node is processed in that group and node, and there is no other replacement. There is no connection between groups. GLB is connected to each group s DR. Load balancing mechanism is centralized in both levels. Clients G L B Fig.. Underlying Distributed System structure. III. PROPOSED METHODS A. Algorithm In this algorithm, load balancers use the idea of determining weights for groups and nodes. Each weight is called bias and the process of determining biases is called biasing. In this paper current load state of groups and nodes are used to determine biases. Therefore each node in the specific times called state checking times sends its current load state to its group s DR that does local load balancing in that group and is connected with the global load balancer. Each local load balancer (DR of each group) computes its group global state from information it receives from nodes and sends it to the global load balancer. The global load balancer does biasing based on the information that it has about the state of each group. Each local balancer does biasing on the nodes based on information it receives from nodes. In biasing process in each local load balancer, the nodes are sorted based on their load states. Then, according to the number of tasks in the load balancer and the group global load state, some percent of tasks pertain to each node as bias. Biases are determined based on nodes load state and tasks are sent to them. Global load balancer determines groups biases, based on the groups state information it receives from DRs and applies them on the tasks reside in its memory. The time amount between two biasing is called biasing interval. Increased biasing interval makes algorithm ineffective, because it causes increase in response time. On the other hand, when the global load state of the system is light or if biasing intervals are low, biasing process make several zero biases. So tasks remain in the load balancers memory and response time increases. To solve this Group Group Group DR DR DR 57 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.
problem, algorithm designed in the way that if tasks were lower than fixed threshold (e.g. here number of nodes), nodes sorted according to their load state from minimum to maximum. Then loads are divided between them based on their state. This significantly improves response time. However, when bias is selected as usual, some tasks remained in the balancer because of mathematic computations like omitting decimal point. In order to fix this problem, these tasks get added to the groups or nodes that have better load state. Pushing policy is employed for exchanging current state information of the groups and nodes in system. It means that load balancers information in the system is updated without sending any request for state information to groups or nodes. In this method, each node sends its load state to its group s DR at state checking time. DR of each group sends global state of its own group to global load balancer based on the information that it receives from nodes. This method omits useless requests from the system and reduces communication overheads. The time between state checking times must be selected carefully. High time interval amount causes old information which makes decisions invalid. On the other hand, low time interval amount causes large number of state information messages that should be sent continuously by groups and nodes. So, communication overheads increase and it affects the system performance. Hence, optimal biasing interval allocation is required. B. Minimum Load State Algorithm In this algorithm, each node sends its current load state to its group s DR at times called state checking time. Each local load balancer (DR of each group) computes its group global state based on the information that it receives from nodes and sends it to the global load balancer. Global balancer based on the state information of the groups decides for transmitting of the arrival tasks. Each of local load balancers distribute received loads from global load balancer to the nodes in their own groups, based on the state information of the nodes. That is, all Load balancers send arrival tasks to the groups and nodes in the determined time slices. In this method, group and node with minimum load state are determined and arrival tasks in a time slice are sent to those group or node. This process is done in the specific times called biasing time. In order to avoid overloading of one group or node which works with a suitable load and in order to reduces response time when global load state of the system is light, this algorithm is designed in a way that the group or node that is selected as minimum in one biasing, may not be selected as minimum in the next biasing time. This reduces response time significantly when the global load state of the system is light. Like the first proposed algorithm, pushing policy is employed here to reduce communication overheads also. Moreover, the time intervals that groups and nodes send their state information is set to the value that it consider both validation of state information and reduction of communication overheads. IV. PERFORMANCE EVALUATION In a comparative study, the performance of conventional Round Robin, and the proposed and MLSRR algorithms are compared by measuring three criteria: Drop Rate,, and Response Time in computer simulation. Comparisons are done in two ways. One is with variable number of nodes in each group and constant number of tasks and the other is with the constant number of nodes in each group and variable number of tasks. A. Algorithm In this algorithm global load balancer allocates random weights called biases to each group. Arrival tasks are distributed based on these random biases on the groups. Local load balancers in each group do biasing on the nodes of their group and distribute arrival tasks based on determined biases. Neither global load balancer nor local load balancers have prospect of current load state of groups and nodes. The only criteria for task distribution are random biases. B. Algorithm In this algorithm, global load balancer distributes arrival tasks orderly on the groups at the specific time slices. Also each local load balancer distributes arrival tasks of group to the nodes at specific time slices. Neither global load balancer nor local load balancers have prospect about current load state of the groups and nodes. The only criteria for task distribution are specific time slices and the order of groups and nodes. C. Comparative study of Proposed Algorithms To evaluate proposed methods in a comparative study, several simulations for different situation were developed. The results of this simulation study are shown in following figures. They compare performances of above mentioned four algorithms. Comparisons are done in two forms. With the homogeneous nodes, witch all nodes have same memory and same service rate. With the heterogeneous nodes, witch all nodes have completely different memory and different service rate. At both forms, one is with variable number of nodes in each group and constant number of tasks and the other is with the constant number of nodes in each group and variable number of tasks. General specifications of the groups are the same. It causes Random and algorithms have better performance. 58 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.
As it can be seen in fig., there are three task generators on the global load balancer in the system. The communication links have some delay. When the system has homogeneous nodes, there is a constant task arrival rate. When the system nodes are heterogeneous, in order to shows the differences of the four algorithms, simulated task generators are set to have variable and unpredictable arrival rates. Hence, each one of the three task generators can generate to tasks at a time in the system. It means that, arrival task may vary from to 3. This may clarify the difference between static and dynamic algorithms. Clearly, arrival rates are not constant and vary as time varies in real systems. Simulation time is set one hour. In the simulation with homogeneous nodes, the varying number of nodes is for 5 tasks in the system, while the varying number of tasks is for 6 nodes in each group, respectively. With the heterogeneous nodes, the varying number of nodes is for 22 tasks in the system, while the varying number of tasks is for 7 nodes in each group, respectively. As it can be seen in fig. 2, fig 3, and fig. 4, when the nodes are homogeneous, except algorithm witch is the least efficient algorithm, other algorithms have same performance in terms of drop rate and throughput. When the system is fully overloaded, all of the algorithms have same performance. However, in terms of response time MLSRR outperformed other algorithms when the system is not fully overloaded. When the system has completely heterogeneous nodes, performance is completely different. As it can be seen in the fig. 5, fig. 6 and fig. 7, the system performance using and MLSSR algorithms in the three criteria outperforms other two algorithms. Fig. 5 shows that the drop rate using and MLSRR algorithms is lower than other algorithms. This indicates that system is more stable using and MLSRR. Fig. 6 shows that the throughput using the proposed algorithms is higher than other two algorithms especially when the system is not fully overloaded. Fig. 7 shows the comparison of response time for these four algorithms. When the system is fully overloaded, response time increases using and MLSRR algorithms. But as it can be seen in fig. 5 and fig. 6, in this situation, the drop rate and throughput in proposed algorithms are better than other two algorithms. When the system is not fully overloaded, proposed algorithms outperform other two algorithms..4.35.3.25.2.5..5.5.45.4.35.3.25.2.5..5 5 8 2 24 27 3 39 Number of Servers 72 9 5 22 Fig. 2. with varying number of Nodes and Tasks..8.6.4.2.8.6.4.2 5 8 2 24 27 3 39 72 9 5 22 Fig. 3. with varying number of Nodes and Tasks. 59 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.
2 Average Response time 8 6 4 2 5 8 2 24 27 3 39.8.6.4.2 5 8 2 24 27 3 39 2 Average Rasponse Time 8 6 4 2 72 9 5 22.8.6.4.2 72 9 5 22 3 Fig. 4. Response Time with varying number of Nodes and Tasks. Fig. 6. with varying number of Nodes and Tasks..6.5.4.3.2. 5 8 2 24 27 3 39.4.35.3.25.2.5..5 72 9 5 22 3 Fig. 5. with varying number of Nodes and Tasks. Average Response Time Average Response Time 9 8 7 6 5 4 3 2 7 6 5 4 3 2 5 8 2 24 27 3 39 72 9 5 22 3 Fig. 7. Response Time with varying number of Nodes and Tasks. 52 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.
V. CONCLUSION In this paper two new load balancing algorithms are proposed called and MLSRR. They have the simplicity of static methods and adaptativeness of dynamic methods simultaneously. These methods are based on the hierarchical structure. They balance the loads in two levels of groups and nodes. First method distributes the arrival loads on the groups and nodes with specific biases based on their current load state. Second method selects the group and node with minimum load state and transmits arrival loads to it. Four typical load balancing approaches;, conventional, and the proposed Dynamic Biasing and MLSRR were compared. The simulation results show that the proposed algorithms are more stable and efficient than the existing approaches in terms of drop rate, throughput and response time for various numbers of nodes and tasks especially when the system is not fully overloaded. The results also show that the dynamic algorithm is more efficient especially when the nodes are heterogeneous in the system. REFERENCES [] D. L. Eager, E. D. Lazowska. and J. Zahojan. A Comparison of Receiver-Initiated and Sender-Initiated Adaptive Load Sharing. Proc. of the 985 ACM SIGMETRICS Conference on Measurement and Modelling of Computer Systems. August 985. pp. -3. [2] R. Gupta and P. Gopinath. A Hierarchical Approach to Load Balancing in Distributed Systems. Proceedings of the Fifth Distributed Memory Computing Conference, 99. vol. 2, pp. -5. [3] Sandeep Singh Waraich. Classification of dynamic load balancing strategies in a Network of Workstations. Fifth International Conference on Information Technology: New Generations. April 28. pp. 263-265. [4] Ming-Chang Huang, S. Hossein Hosseini and K. Vairavan. A Receiver- Initiated Load Balancing Method In Computer Networks Using Fuzzy Logic Control. IEEE. GLOBECOM 23. vol. 7. Dec. 23. pp. 428 433. [5] Hyo Cheol Ahn, Hee Yong Youn, Kyu Yeong Jeon, and Kyu Seol Lee. Dynamic Load Balancing for Large-scale Distributed System with Intelligent Fuzzy Controller. IEEE International Conference on Information Reuse and Integration. Aug 27. pp.576 58. [6] Seong-hoon Lee, Tae-won Kang, Myung-sook KO, Gwang-sik Chung, Joon-min Gil and Chong-sun Hwang. A Genetic Algorithm Me hod for Sender-based Dynamic Load Balancing Algorithm in Distributed Systems. 997 First Intemational Conference on Knowledge-Based Intelligent Electronic Systems, May 997. vol.. pp. 32 37. [7] L.M. Ni, C-W. Xu, and T.B. Gendreau. A Distributed Drafting Algorithm for Load Balancing. IEEE Transactions on Software Engineering. Oct. 985. vol.. pp. 53-6. [8] J.A. Stankovic and IS. Sidhu. An Adaptive Bidding Algorithm for Processes, Clusters and Distributed Groups. In Fourth International Conference on Distributed Computing Systems. May 984. pp. 49-59. [9] Albert Y. Zomaya and Yee-Hwei Teh. Observations on Using Genetic Algorithms for Dynamic Load-Balancing. IEEE Transaction on Parallel and Distributed Systems. vol. 2. no. 9, September 2. pp.899-9. [] Chulhye, P., and J. G. Kuhl, A Fuzzy-Based Distributed Load Balancing Algorithm for Large Distributed Systems, Proc. 2nd Int l Sym. Autonomous Decentralized Systems, pp.266-273, April 995. 52 Authorized licensed use limited to: UNIVERSITY PUTRA MALAYSIA. Downloaded on March 9,2 at 4:26:5 EST from IEEE Xplore. Restrictions apply.