# An Optimized Load-balancing Scheduling Method Based on the WLC Algorithm for Cloud Data Centers

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3 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) algorithm of LVS(Linux Virtual System). The main idea is that the processing capability of each server is represented by a corresponding weight. A server s load is indicated by the number of the connections connected to that server. When a new request arrives, the algorithm computes the ratio of each server s current connections and weight and assigns the request to the server with the least ratio. This algorithm is suitable for the situation where the processing capabilities of the servers are different. Suppose there are a group of servers S = {S, S,, Sn, W (Si) represents the weight number of server Si, its default value is. C(Si) represents the number of connections that are currently connected to server Si. Csum = C(Si) (i =,,...n ) represents the sum of all the connections that are currently connected to all the servers. The newly arrived request will be assigned to server Sm with the following condition: (C(Sm)/Csum)/W (Sm) = min{(c(si)/csum)/w (Si) (i =,,...n ), wherein W (Si) is not zero. Csum is a constant in one round, so the condition can be simplified to: C(Sm)/W (Sm) = min{c(si)/w (Si) (i =,,...n ), where in W (Si) is not zero. The computation overhead of division is much bigger than multiplication, and floating-point division is not allowed in the Linux kernel, so in order to achieve a better performance we further optimize the judge condition C(Sm)/W (Sm) > C(Si)/W (Si) to C(Sm) W (Si) > C(Si) W (Sm) under the assumption that the weight of a server is greater than zero. Meanwhile, the algorithm should ensure not to schedule a server when its weight is zero. The detailed algorithm is as follows. for(m=;m<n;m++){ if(w (Sm)>){ for(i=m+;i<n;i++){ if(c(sm) W (Si) > C(Si) W (Sm)) m=i; return Sm; return ULL; 2.2 Deficiency of the WLC scheduling algorithm The WLC scheduling algorithm considers the processing capacity of each server using a corresponding weight. It can achieve a higher load balancing degree than the LC algorithm. However, it still has the following shortcomings. () The weight of each server is not set reasonably and accurately. In most cases, the weight of a server is preset by the system administrator based on the hardware configuration and the administrator s personal experience. It is not dynamically adjusted based on the actual load of a server, therefore cannot reflect the real-time processing capability of a server during the scheduling

6 6824 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) Furthermore, the algorithm should ensure that the server will not be scheduled when its weight is zero. The detailed algorithm is as follows. for(m=;m<n;m++){ if(w (Sm)>){ for(i=m+;i<n;i++){ if(( 4 C ij P j ) W (S m ) < ( 4 C mj P j ) W (S i )) m=i; if(m==m) Cm + +; if(m==m2) S i + +; if(m==m3) Cm3 + +; if(m==m4) Cm4 + +; return Sm; return ULL; The weight of server S i is: W (Si) =.6 V c(si) +.4 V m(si), and the weight of server Sm is: W (Sm) =.6 V c(sm) +.4 V m(sm). The flowchart of the WLC scheduling algorithm is shown in Figure. 4 Simulation and Performance Analysis 4. Simulation tools We use the open-source platform Cloudsim[] to simulate our proposed algorithm and compare its performance with the existing scheduling algorithms. 4.2 Design of simulation experiment We simulated three kinds of scheduling algorithms, namely LC scheduling algorithm, WLC scheduling algorithm and the DWLC scheduling algorithm in three groups with different number of tasks. In each group there are 5, 5 and 5 tasks respectively. All the tasks are generated randomly with various sizes. There are 5 servers in each group. Comparison analyses of these three algorithms according to the simulation results were given. Mean value stands for the average task completion time of all the servers in the group; it represents the system efficiency. While standard deviation stands for the load balancing degree of the system. 4.3 Simulation results and analysis () 5 tasks We simulated the above three algorithms on 5 randomly generated tasks. The performance comparison is shown in Fig. 2.

7 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) m= m<n return ull m++ W(S m )> i=m+ i++ i<n M=M M=M 2 condition M=M 3 m=i C m ++ C m2 ++ C m3 ++ C m4 ++ return S m condition:(( 4 Cij P j) W (Sm)) < (( 4 Cmj P j) W (Si)) Fig. : The flowchart of the WLC scheduling algorithm As we can see from Fig. 2, the load balancing degree of the DWLC scheduling algorithm is the best, followed by the WLC scheduling algorithm. And the load balancing degree of the LC scheduling algorithm is the worst. We further compared the mean values and standard deviations of these three algorithms. The result is shown in Fig. 3. As we can see from Fig. 3, the DWLC scheduling algorithm can guarantee higher efficiency compared with the LC scheduling algorithm and the WLC scheduling algorithm. And the standard deviation of the DWLC scheduling algorithm is the minimum, showing that the load balancing degree of this algorithm is the best, followed by the WLC scheduling algorithm. The standard deviation of the LC scheduling algorithm is the highest, indicating that there has been apparent imbalance among all the servers. (2) 5 tasks We simulated the above three algorithms when the number of tasks increases to 5, and the performance comparison is shown in Fig. 4. As we can see from Fig. 4, when cloud computing servers receive 5 randomly generated tasks of different weights, the system efficiency of the DWLC scheduling algorithm is the best compared

9 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) x 4 Least-Connection Scheduling Algorithm 2 x 4 Weighted Least-Connection Scheduling Algorithm 4 Dual Weighted Least-Connection Scheduling Algorithm :.83e+4 2 :.226e :.792e : : 83.6 : Fig. 4: The performance of these three algorithms with 5 tasks x 4 The number of tasks:5 Least-Connection Weighted Least-Connection Dual Weighted Least-Connection Task CompletionTime(s) Fig. 5: Comparison of the mean values and standard deviations for these three algorithms with 5 tasks algorithm, showing that the difference of the task completion time for all servers is very small. And the load balancing degree of the LC scheduling algorithm is the worst. Mean values and standard deviations of these three algorithms were compared, as shown in Fig. 7. As we can see from Fig. 7, the standard deviation of the DWLC scheduling algorithm is the minimum, showing that the load balancing degree of this algorithm is the best, followed by the WLC scheduling algorithm. The standard deviation of the LC scheduling algorithm is still the highest, and the imbalance among the servers is more obvious than that of the first two groups. The figure also shows that as for the system efficiency, the DWLC scheduling algorithm still achieves the best performance. To sum up, the DWLC scheduling algorithm shows preferable performance not matter the number of tasks is small or big. The load balancing degree and the system efficiency of the DWLC scheduling algorithm has improved a lot compared with the WLC scheduling algorithm and the LC scheduling algorithm.

10 6828 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) x 5 Least-Connection Scheduling Algorithm 2 x 5 Weighted Least-Connection Scheduling Algorithm 4 x 4 Dual Weighted Least-Connection Scheduling Algorithm :.834e+5 2 :.33e :.662e : 6.82e : 2525 : Fig. 6: The performance of these three algorithms with 5 tasks x 5 The number of tasks:5 Least-Connection Weighted Least-Connection Dual Weighted Least-Connection Task CompletionTime(s) Fig. 7: Comparison of the mean values and standard deviations for these three algorithms with 5 tasks 5 Conclusions This paper explored the existing scheduling algorithms in cloud data centers and proposed the DWLC scheduling algorithm, which is an improved algorithm based on the WLC algorithm. The DWLC algorithm adopted a more reasonable dynamic assignment strategy to determine the weight of each server compared to the WLC algorithm. It took both the weight differences of servers and tasks into consideration, therefore is a dual weighted scheduling algorithm. The DWLC scheduling algorithm makes it possible to achieve load balancing and high efficiency even in a system where both cloud servers and tasks are diverse. We also simulated the improved algorithm using the open source CloudSim simulation platform. And a comparison was made among the performance of the LC scheduling algorithm, the WLC scheduling algorithm and the DWLC scheduling algorithm. The analysis shows that the DWLC algorithm can achieve better load balancing degree and higher system efficiency than existing scheduling algorithms and thus can better satisfy the requirements of cloud data centers. References [] Peng LIU. Cloud Computing [M], Second Edition. Beijing: Electronic Industry Press, 2. [2] Xiaoqian LIU. Research on Data Center Structure and Scheduling Mechanism in Cloud Computing

11 L. Zhou et al. /Journal of Computational Information Systems 9: 7 (23) [D]. Hefei: University of Science and Technology of China, 2. [3] Wenhong TIA, ong ZHAO, uanliang ZHOG et al. Dynamic and Integrated Load-Balancing Scheduling Algorithm for Cloud Data Centers [J]. China Communicatins, 2, (6): [4] Wenhong TIA, ong ZHAO. Cloud Computing: Resource scheduling management [M]. Beijing: ational Defense Industry Press, 2. [5] iqiu FAG, Daohong TAG, Junwei GE. Enerygy-aware Schedule Strategy Based on Dynamic Migration of Virtual Machines in Cloud Computing [J]. Journal of Computational Information Systems, 22, 8(): [6] A.SIGH, M.KORUPOLU, D.MOHAPATRA, Server-Storage Virtualization: Integration and Load Balancing in Data Centers, in the proceedings of the 28 ACM/IEEE conference on Supercomputing (28), pp. -2. [7] Xiu MIAO. Design and Load Balancing of Mobile IPTV Based on Cloud Computing Platform [D]. Beijing: Beijing University of Posts and Telecommunications, 2. [8] Chuang KA. A ew Dynamic Loading Balance Algorithm Based On LVS Cluster [D]. Ocean University of China, 28. [9] Song WE. Load Balancing of LVS [EB/OL]. [28]. [] Buyya R., Ranjan R., Calheiros R.. Modeling and Simulation of Scalable Cloud Computing Environments and the Cloudsim Toolkit: Challenges and Opportunities [C] Proc. of International Conference on High Performance Computing & Simulation. Kochi, India: [s. n.], 29: -.

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