Evaluating an Adaptive Web Traffic Routing Method for the Cloud

The low maintenance requirement, capacity scalability, and pay-as-you-go properties of cloud computing are attractive for the virtualized deployment of diverse web services. Web traffic is typically handled by multiple server mirrors that are spatially dispersed to satisfy the expectations of a large number of worldwide users. Since the energy consumption of each server depends on its workload, the use of web routing opens the possibility of reducing operational costs through the exploitation of the regional and temporal differences in energy pricing at the mirroring sites. On the downside, the shared nature of the cloud and the network brings potential latency issues that could impact the quality of service of many applications. In this paper, we report on experimental results obtained from a web service system that uses learning automata, a reinforcement learning approach to make dynamic routing decisions based on a cost and quality-of-service criteria in the cloud. The experiments were conducted using a network of 24 nodes running in the CloudLab with time-varying energy prices that were modeled from real data.

[1]  Xue Liu,et al.  MEC-IDC: joint load balancing and power control for distributed Internet Data Centers , 2010, ICCPS '10.

[2]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[3]  Gang Quan,et al.  On-Line Real-Time Service Allocation and Scheduling for Distributed Data Centers , 2011, 2011 IEEE International Conference on Services Computing.

[4]  B. John Oommen,et al.  Learning automata processing ergodicity of the mean: The two-action case , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  MengChu Zhou,et al.  Time-Aware Multi-Application Task Scheduling With Guaranteed Delay Constraints in Green Data Center , 2018, IEEE Transactions on Automation Science and Engineering.

[6]  Mahmoud Al-Ayyoub,et al.  Resilient service provisioning in cloud based data centers , 2018, Future Gener. Comput. Syst..

[7]  P. R. Srikantakumar,et al.  A LEARNING MODEL FOR ROUTING IN TELEPHONE NETWORKS , 1982 .

[8]  B. John Oommen,et al.  Recent advances in Learning Automata systems , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[9]  S. Lakshmivarahan,et al.  Learning Algorithms Theory and Applications , 1981 .

[10]  B. John Oommen,et al.  Dynamic algorithms for the shortest path routing problem: learning automata-based solutions , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Athanasios V. Vasilakos,et al.  Ergodic discretized estimator learning automata with high accuracy and high adaptation rate for nonstationary environments , 1992, Neurocomputing.

[12]  Ricardo Lent,et al.  Experimental Evaluation of an Energy-Delay Aware Web Routing Method , 2018, 2018 IEEE 43rd Conference on Local Computer Networks (LCN).

[13]  B. John Oommen,et al.  Absorbing and Ergodic Discretized Two-Action Learning Automata , 1986, IEEE Trans. Syst. Man Cybern..

[14]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[15]  Ricardo Lent,et al.  Dynamic Cost-Aware Routing of Web Requests , 2018, Future Internet.

[16]  B. John Oommen,et al.  Multiaction learning automata possessing ergodicity of the mean , 1985, Inf. Sci..