SLA-aware load balancing for cloud datacenters

1. SLA-aware load balancing for cloud datacenters 指導教授：王國禎 學生：黎中誠 國立交通大學資訊工程系 行動計算與寬頻網路實驗室

2. Outlines • Introduction • Related works • Propose architecture • Dynamic weighted round-robin scheduling algorithm • Experimental environment • Experimental result • Conclusion • References

3. Introduction • Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet) [1] Fig. 1 The cloud scales: Amazon S3 growth [2]

4. Introduction (Cont.) • Fig. 2 illustrates a general diagram of the load balancer, all of user requests will be connected to the load balancer, and then forward to appropriate server • The function of load balancing is to distribute the excess local workload evenly to the whole cloud federation, aims to realize a high ratio of user satisfaction and facility utilization in the cloud [3] • Improper allocation rules might cause the inefficiency of the system

5. Introduction (Cont.) Fig. 2 A general load balancer diagram

6. Introduction (Cont.) • In this paper we focus on two issues • load balancing control • centralized or distributed • load balancing algorithm • static or dynamic

7. Related work Fig. 3 Classification of load balancing

8. Related work (Cont.) Fig. 4 Centralized load balancer architecture [4] Centralized load balancer architecture means there is a manager which will receive every incoming request When system grows to exceed the capacity of manager, it will cause “manage the manager” problem

9. Related work (Cont.) Fig. 5 Structure of Distributed Load Balancer [5]

10. Proposed Architecture We propose a new architecture to support dynamic load balancing in cloud data center There are two levels in our load balancer design: global balancer and local balancer Each global balancer connects with one local balancer that forms a virtual zone

11. Proposed Architecture (Cont.) • Local balancer • Monitoring local VM’s status which are in the same virtual zone • Choosing proper VM to handle request by scheduling algorithm • Global balancer • Global balancers connected to each other via P2P connection • When global balancer responsible area overload, it will redirect requests to other virtual zone

12. Proposed Architecture (Cont.) Fig. 5 Proposed system architecture

13. Proposed Architecture (Cont.) Fig. 6 Module diagram

14. Dynamic weighted round-robin scheduling algorithm Fig. 7 Experimental environment

15. Dynamic weighted round-robin scheduling algorithm The critical resource is different when cloud datacenter provides varies services Load Monitor module collects four load metrics. These four load metrics are utilizations of CPU, memory, network bandwidth, and disk I/O Capacity index

16. Dynamic weighted round-robin scheduling algorithm Artificial Neural Network (ANN) has the availability of optimization and prediction We use delta learning rule in our neural network design (see Fig. 8) In our ANN design, we consider avoiding violation of the response time which is provisions in SLA.

17. Dynamic weighted round-robin scheduling algorithm Fig. 8 Neural network architecture

18. Dynamic weighted round-robin scheduling algorithm Capacity index Neural index Weight

19. Experimental environment Table 1 Experimental setup Table 2 Configuration capability of each VM

20. Experimental result Table 3 Measurement results

21. Experimental result (Cont.) Fig. 10 four weight adjustment methods result

22. Conclusions We propose architecture based on distributed load balancer which is different from general centralized balancer Combination of system performance monitoring and neural network This load balancing algorithm can avoid SLA violations The experiments support that our proposed algorithm has faster response time than other scheduling algorithm

23. References [1] V. Nae, A. Iosup, and R. Prodan, "A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment“, in Parallel and Distributed Systems, IEEE Transactions on , 2010, pp. 380 - 395. [2] R. Suselbeck, G. Schiele, and C. Becker, "Towards a Load Balancing in a Three-level Cloud Computing Network," in Network and Systems Support for Games (NetGames), 2009, pp. 1 - 2. [3] Shu-Ching Wang, Kuo-Qin Yan, Wen-Pin Liao, and Shun-Sheng Wang, "A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation," in IEEE ICCSIT, 2010, pp. 108 - 113. [4] RajkumarRajavel, "A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing," in IEEE INCOCCI, Erode, 2010, pp. 419 - 424.