A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm

Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Available at http://www.joics.com A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm Xin LU a,, Jing ZHOU a, Dong LIU b a School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China b Sichuan Chang Hong Electric Co., Mianyang 621000, China Abstract One of the core problems which cloud resources scheduling need to solve is the load balance. In the cloud resources scheduling process, if load changes suddenly, this may cause resources scheduling tilt. This paper takes real-time load parameters (CPU occupancy rate, memory occupancy rate, network bandwidth, the process occupancy rate, service response time) from the server cluster nodes as decision variables of resources scheduling model, and uses the improved adaptive genetic algorithm to search the optimal solution, in order to realize the load balancing scheduling of cloud resource, and make the each index change smoothly. The experimental result shows that, using the improved load balancing scheduling strategy to solve the problems of the load balancing scheduling of cloud resource, not only makes the each index of the system change smoothly, but also improves the performance of the system efficiently. Keywords: Cloud Computing; Genetic Algorithm; Resource Scheduling; Load Balance 1 Introduction In the cloud computing applications, cloud infrastructure system provides users with computing resources used as needed, storage resources, network resources and other services[1]. In certain cloud applications, user access to service resources with the abrupt and uncertainties often leads to some server resource scheduling imbalance, resulting in some of the cloud application response time is too long. In order to adapt to the abrupt change of the number of user access requests, the industry generally uses scalable server cluster resources to meet. However, in the server cluster resource scheduling, we need to address an important issue, which is how to make cloud resource scheduling along with load fluctuations, dynamic and balanced [2]. In the industry s- tudy, some researchers put forward scheduling strategy such as Round-Robin Scheduling(RRS), Weighted Round Robin Scheduling(WRRS), Least-Connection Scheduling(LCS), Source Hashing Corresponding author. Email address: luxinmail@uestc.edu.cn (Xin LU). 1548 7741/ Copyright 2012 Binary Information Press December 1, 2012

4802 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Scheduling(SHC) and so on[3-4]. Document[4] contrasts and analyses these load balance scheduling algorithms, and puts forward a method for finding an optimal solution in various schemes. Using traditional scheduling algorithm to search for the optimal solution can easily produce error peaks. Zomaya A.Y has proposed using Genetic Algorithm [5] for the optimal scheduling search. However, due to the use of fixed parameter settings in the search for the evolutionary process, parameter values of the method did not change along with the search status. This algorithm can t reach equilibrium in the search process, and easily getting into the regional optimal solution, and cause premature phenomenon to appear. In order to overcome this defect, we need to dynamically set the parameters, and ensure that parameter values change the status along with the search process. The algorithm can be avoided into a regional convergence. Further taking into account species adaptation, but also need the algorithm to adaptively adjust. Therefore, this paper will combine the characteristics of adaptive genetic algorithm which can search the optimal solution in multi-objective problem, learn from the document [6] suggested load balancing ideas on the server to study the optimal scheduling strategy and to solve the load balance problems of server nodes in the cloud resources scheduling. 2 The Problem of Cloud Resource Scheduling In the cloud computing environment, the cloud resource scheduling controller, through the changing load of real-time monitor servers nodes, assigns resources to the corresponding node dynamically to meet some needs, such as high utilization of resources, excellent performance, fast response. The topology of the server group nodes that involved in the cloud resources scheduling can be described in Fig. 1....... CC4 virtual machine CC5 V1 CC3...... CCn Vk CC1...... CC2...... NC1 NC2 NCm NC1 NC2 NCm Fig. 1: Nodes topology of cloud server cluster The above topology can be described as an G(V,E) undirected graph, G represents the node set in the chart, E represents the set of connection between these nodes. CC i represents cluster control server node. NC i represents node controller, which is a separate physical machine. Each CC i node manages m node controller NC i s, you can run k virtual machines on each NC i, and use V i to represent virtual machine, So the node which will be mentioned in this paper is V i. In the cloud service environment, application, DBMS and other software are deployed in several V i nodes to run. Difficult issues of cloud resource scheduling need to be addressed is: when the

X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 4803 user requests the use of cloud resources services, cloud resources scheduling controller should adopt what kind of resources scheduling model and strategy, this makes each node of the whole system deal with tasks in load balance way, and prevent scheduling tilt of the cloud services platform system resources. 3 Load-balancing Cloud Resource Scheduling Model In order to solve the above problems, this paper proposes a model program which is multi-objective optimization and load balancing of cloud resource schedule, the model is shown in Fig.2. request cloud controller Load balancing scheduling strategy user virtual resource pool cluster server n cluster server 2 [29] cluster server 1 V 1 V m V2 [29] V 3 V 4 request quene... cluster controller real-time load monitoring multi-objective parametes calculate fitness cluster n cluster 2 The optimal solution Improved Adaptive Genetic Algorithm fitness of cluster 2 fitness of cluster 1 Fig. 2: Load-balancing cloud resource scheduling model When the user makes a request, the cloud controller receives the request, then it will arrange the request in the queue of the server cluster. There have more than one cluster controller below the cloud controller, cluster controllers real-time monitor the running load parameters of every V i in virtual resource pool of this cluster, such as CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate and so on, Therefore, this is a multi-objective problem which searches for the optimal solution. According to monitoring the multi-objective parameter, cluster controller will calculate the fitness of each V i node, then for the whole cluster, use Improved Adaptive Genetic Algorithm to search the best solution in the multi-objective question, which is the lighter node by use algorithm to solve. Finally, according to cloud resource scheduling strategy of load balancing, service resource will be assigned to the best server node. By using this method proposed in this paper, the whole system can make load balance. 3.1 Fitness calculation function This paper chooses several key indexes (such as CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate) of a service node, which as decision variables of multi-objective optimization problem. Respectively, use CPU, memory, net, response and process to express the decision variables, w i represents load information weighting coefficient of these variables. Sub-objective function f i (x) is set up different weight, due to the different

4804 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 applications require different parameter values, so w i expresses the important degree of decision variable in the optimization problem, and w i = 1.When using cluster for the first time, we can set the initial weights of each node, and then with the change of these node load. The system will dynamical adaptively adjust load changes. In this paper, we do not consider the configuration of servers. Assuming that configuration of all servers are the same. Therefore, the objective function can be described as follow. n m t(f(x)) = (w i f i (x)/c j ) (1) i=1 j=1 In formula (1), n is the number of decision variable, m stands for the number of nodes, c j is task proportion which server be assigned. The weight of each nodecan express for the expansions of formula(1). It can be distributed as follow. t(f(j)) = w 1 cpu[j] + w 2 memory[j] + w 3 net[j] + w 4 response[j] + w 5 process[j], 0 < cpu[j] 1, 0 < memory[j] 1, 0 < net[j] 1, 0 < response[j] 100ms, 0 process[j] 1 (2) The objective function is used to calculate the weight of the nodes. So it is more appropriate that choosing the objective function to determine the fitness calculation function. The fitness function is shown in the formula (3). We can seek the optimal chromosomes of feasible solution, namely the optimal solution. { n m n } F (x) = c (w i f i (x)), c max w i f i (x) (3) i=1 j=1 For multi-objective optimization problem, to improve a target may reduce the performance of another one. For example, there is an inverse proportion between server response time and CPU occupancy rate, network bandwidth. The relationship can be described as follow. i=1 response = c/(cpu net), c is constant (4) Therefore, we only need to acquire these four parameters which are CPU and memory occupancy rate, network bandwidth, processes occupancy rate, and these four parameters are introduced into the algorithm, then through the calculation of these parameters to obtain the optimal solution. 3.2 The design of improved adaptive genetic algorithm In the upper segment, decision variables of problems which need to be solved in this paper have been set, and the fitness function has been set up. The multi-objective optimization problem will be solved by the design of the Improved Adaptive Genetic Algorithm. The flowchart of the algorithm is shown in Fig.3. The main improvement of Improved Adaptive Genetic Algorithm is taking advantage of the constant change of population diversity to avoid the regional optimal solution which is common in standard adaptive algorithm. This section will code the decision variables and establish initial population, and then will compute the population fitness and repeated use selection operator, commuting operator and mutation operator. During evolution, along with the change of the

X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 4805 Describe the problem of solving the optimal solution Set decision variables According to the actual situation, set up the objective function and fitness function To code for variable, create initial population Use fitness function to calculate individual fitness in the population Design genetic operator, to determine operation parameters with diversity Use algorithm to calculate The optimal solution in the algrothim as the best individual in the current population Fig. 3: Improved adaptive genetic algorithm flowchart population, Improved Adaptive Genetic Algorithm dynamically adjusts the commuting rates and mutation rates until finally seeks out the optimal solution. Genetic algorithms usually use binary code, because it makes encoding/decoding and genetic operations simple. But the disadvantage of this method is that it can t fully reflect the specific knowledge of the target problem, such as the optimization problem of some continuous functions. Considering the defects of binary code, this paper chooses real-number-coding. The real-numbercoding uses a real number in a certain range to represent each gene value of the individual. Since the length of coding for the individual is equal to the number of decision variables that the individual has, real-number-coding usually utilizes the real value of decision variables. The optimization problem in this paper contains four decision variables x i (i = 1, 2, 3, 4), and each decision variable is in [0,1], so the improved Adaptive Genetic Algorithm can use any four values in [0,1] to represent the genotypes of individuals. Selection operation, also known as copy operation, can pass the individuals which have high fitness in the current group to the next generation of population with some rules by calculating the fitness with fitness function. It mimics biological genetic and evolutionary process, and genetic algorithms can implement individual survival of the fittest by the selection operators or copy operators. This paper chooses fitness proportional model and elite selection algorithms to select and copy individuals. In this strategy, the probability of an individual with higher fitness is chosen to pass to the next generation is proportional to the value of individual fitness. Formula (5) shows the relationship between them. n p i = F i / F j (5) In this formula, p i is the selective probability of the ith individual, F i is the fitness value of the ith individual, n is the size of population. As can be seen from the formula, p i reflects the j=1

4806 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 proportions of the fitness of an individual to the total fitness of all individuals in the entire population. So the higher the fitness of an individual and the higher the probability of this individual can be chosen. Since the regional optimum is common in standard adaptive genetic algorithm and leads to the premature phenomenon, the Improved Adaptive Genetic Algorithm improves P c and P m to make them increase properly when the fitness of population tends to the same value, and decrease properly when the fitness of population is dispersed. So that, Improved Adaptive Genetic Algorithm increase the mutation probability and decrease the crossover probability of individual whose fitness is lower than the average, and protect the solution to let the individual whose fitness is higher than the average can be passed to next generation. The formula to calculate the improved crossover probability P c and mutation probability P m is as following: p c = { pc1 (p c1 p c2 )(f f) f max f, f f p c1, f < f (6) p m = { pm1 (p m1 p m2 )(f max f) f max f, f f p m1, f < f (7) In formula(6) and (7), f is the fitness value of the individual with higher fitness of the two cross individuals, f is the fitness average value of each generation, f max is the maximum fitness value in the entire population, f is the fitness value of variation individuals. p c1 is the upper limit of crossover probability and p c2 is the amplitude of crossover probability. Generally speaking, p c1 =0.9 or 1, the value of p c2 is in the range of [0.5,1]. p m1 is the upper limit of mutation probability and p m2 is the amplitude of mutation probability. The value of p m1 is always 0.1, and the value of p m2 is in the range of [0.01,0.05]. Improved Adaptive Genetic Algorithm proposed for the optimal solution is an iterative process, in this paper, we set iteration time T as 500, that is to say, Improved Adaptive Genetic Algorithm terminates after 500 iteration times, and returns to the optimal solution of the problem. The above process is used for searching the optimal solution, so through this algorithm we find out the lightest load node of the whole cluster, then the requests of the tail taken out from the request queue of users are processed by the free node, so balance the load of cluster, and make the cluster not show up that some nodes are special busy, some are special idle. 3.3 Cloud resource load balancing strategy based on adaptive genetic algorithm When the requests of users arrived, how to distribute these requests to the relative idle server to balance the load of the server cluster is the key issue of adaptive cloud resource load balancing model. In the cloud resource positioning, when the request queue is not empty, Improved Adaptive Genetic Algorithm based on the decision variables calculates the population fitness, and dynamically adjusts the crossover and mutation rate along with the changes of fitness, thus gives rise to a new population, and then calculates the fitness of new population, adjusts the rate of crossover and mutation, repeats this iteration until searching out the global optimal solution which is the

X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 4807 lightest load node, then the request which removed from the tail of request queue, is allocated to the lightest load node. The problem of the unbalanced load in the cloud resource is solved, and the server can handle requests of users with the best efficiency. The adaptive cloud resource load balancing scheduling strategy of this paper is as follows: this paper takes CPU occupancy rate, memory occupancy rate, the network bandwidth and process occupancy rate and service response time of V i node in cluster server as variables of measuring the server load multi-objective problems, these parameters are the decision variables of the Adaptive Genetic Algorithm, and use these variables to determine fitness function. In the cloud service environment, with the process of searching and evolving, population will gradually appear in a single case, so the crossover and mutation probability needs to be adaptively adjusted for the search in order to avoid the regional convergence. By improving Adaptive Genetic Algorithm we can find the optimal solution, namely the node has the lightest load, and the node will handle the current request. So that subsequence request can be assigned to load lighter server, this algorithm balances the system load. By using Improved Adaptive Genetic Algorithm, the system can converge to the global optimal solution with the fastest speed to load balance. 4 Simulate Experiment To demonstrate load balancing scheduling model process performance based on Improved Adaptive Genetic Algorithm in this paper, the experiment uses five PCs to build cluster server platform environment. In this experiment, network environment is 100M bit/s. Tomcat 6.0 is chosen as the web server. In the five servers, one is the client machine, one is the cluster management machine, and the rest are the node machines. Cluster management machine and node machine must place in a regional area network. Cloud resource load balancing scheduling program and HTTP agent need to be installed in the cluster management machine. Two virtual machines are respectively created by using Xen in two of node machines, and a virtual machine is created in another node. Every virtual machine can deploy and operate Web applications and probe monitoring tool. After experimental environment is successfully set up, firstly, we test the convergence performance of the algorithm. Fig.4 is the comparison on convergence degree between the Adaptive Genetic Algorithm and Improved Adaptive Genetic Algorithm in this paper. Fig. 4: Convergence degree contrast

4808 X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 As Fig.4 shows, Improved Adaptive Genetic Algorithm proposed in this paper has an experimental result of relatively stable, fast convergence, and does not appear phenomenon of immature convergence. In contrast, Adaptive Genetic Algorithm has slow speed in convergence, and which appears obvious premature convergence phenomenon after 50 times and 100 times of iteration. The experimental results show that Improved Adaptive Genetic Algorithm can effectively prevent premature phenomenon compared with Adaptive Genetic Algorithm, and search the optimal solution fast, and it has obvious advantages. Finally, we test the cloud resource load balancing scheduling strategy proposed in this paper. This experiment chooses the smallest connection strategy (LCS) which compares with load balancing scheduling strategy (LBS) of this paper, and check and record the running situation of CPU and memory of the cluster nodes. The experimental result displays CPU and memory occupancy rate which the system runs in the 45th minutes, each is shown in Fig.5 and Fig.6. Fig. 5: CPU occupancy rate of each node in cluster Fig. 6: Memory occupancy rate of each node in cluster Fig.5 and Fig.6 indicate that LBS proposed in this paper can eliminate the larger load differences in each node and serious load imbalance, is better than LCS strategy. The results show that LBS can reasonably assign tasks, improve the process performance of the whole cluster, therefore LBS proposed in this paper is feasible.

X. Lu et al. /Journal of Information & Computational Science 9: 16 (2012) 4801 4809 4809 5 Conclusions In this paper, by real-time monitoring CPU and memory occupancy rate, network bandwidth, response time, processes occupancy rate of Vi nodes in cluster server, the fitness of each node is determined, and using the Improved Adaptive Genetic Algorithm searches for the optimal solution according to fitness value, namely load lighter node, then the request will be allocated to this node. This method largely eliminates the serious load imbalance between nodes, makes each index of the node smoothly change, so that this can achieve the load balancing of cloud resource. Using this strategy to handle the problem of load balancing scheduling of cloud resource, not only prevent the tilt of the cluster scheduling, but also improve the running performance of the whole cluster. References [1] Michal Armbrust, Armando Fox, and Rean Griffith, et al. Above the Clouds: A Berkeley View of Cloud Cpmputing[J], mimeo, UC Berkeley, RAD Laboratory, 2009, 2: 1-14. [2] Shijue Zheng, Wanneng Shu, Guangdong Chen. A Load Balanced Method Based on Campus Grid [C]. Beijing: International Symposium on Communication and Information Technologlies (ISCTT), 2005. 12-14. [3] V. Cardellini, M. Colajanni. Dynamic load balancing on Web-server systems[j]. IEEE Internet Computing, 1999, 3(3): 28-39. [4] Clandio Casetti, Renato Lo Cigno. A New Class of QoS RoutingStrategies Based on Network Graph Reduction[J]. Computer Net-works, 2003, 41(4): 475-487. [5] Xiaofeng Xue, Yuesheng Gu.Global Optimization Based on Hybrid Clonal Selection Genetic Algorithm for Task Scheduling[J]. Journal of Computational Information Systems, 2010, 6(1): 253-261. [6] Wanneng Shu, Shijue Zheng. A Real-course-based Load Balanced Algorithm of VOD Cluster[C]. Ningbo: International Symposium on Computer Science and Technology(ISCST), 2005. 20-24. [7] Dong Wang, Xinqing Wang, Jian Tang, Chunsheng Zhu, Ting Xu. Fault Diagnosis of the PT Fuel System Based on Improved Adaptive Genetic Algorithm[J]. Journal of Computational Information Systems, 2012, 8(9): 3651-3658.