Placement Strategy for Replicated Servers in CDN

Study of Content Distribution Networks(CDNs) attracts increasing attentions recently. Deploying a set of servers across the Internet containing replicated content will certainly provide users with better experience by reducing request latency and balancing the load. However, due to the large-scale and high-volume requests from the users, quantities of proxy servers are needed resulting in high cost of facilities. Therefore, it is necessary to find an optimal replicated-server placement that can improve Internet performance while controlling the budget. To solve this problem, we first propose a new model, C FLPEC, based on which the nature of Internet can be described. In addition, we analyze the feasibility and compare several lower bound algorithms of the new model. Based on C FLPEC, we testify the performance of the network-segmentation algorithm. The proposed algorithm uses K-means algorithm to cluster the network and for each cluster, greedy algorithm is applied to find out an approximate placement in this area. As we obtain the solutions for all the clusters respectively, we merge them to constitute the final solution for the whole network. Instead of directly stitching them together, possible situations that will occur on the boundaries between two clusters are also taken into consideration. Finally, we learn from the natural characteristics of the Internet to construct the simulation model, and simulation results illustrate the feasibility and effectiveness of the proposed algorithm.

[1]  Jon M. Kleinberg,et al.  A Microeconomic View of Data Mining , 1998, Data Mining and Knowledge Discovery.

[2]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[3]  Sanjoy Dasgupta,et al.  Random projection trees for vector quantization , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[4]  I. Lazar,et al.  Exploring content delivery networking , 2001 .

[5]  BERNARD M. WAXMAN,et al.  Routing of multipoint connections , 1988, IEEE J. Sel. Areas Commun..

[6]  Dorit S. Hochbaum,et al.  Heuristics for the fixed cost median problem , 1982, Math. Program..

[7]  Paul S. Bradley,et al.  Clustering via Concave Minimization , 1996, NIPS.

[8]  George Pallis,et al.  Insight and perspectives for content delivery networks , 2006, CACM.

[9]  Novella Bartolini,et al.  Optimal dynamic replica placement in content delivery networks , 2003, The 11th IEEE International Conference on Networks, 2003. ICON2003..

[10]  James B. Orlin,et al.  Max flows in O(nm) time, or better , 2013, STOC '13.

[11]  Nong Xiao,et al.  A Quantitative Survey on QoS-Aware Replica Placement , 2008, 2008 Seventh International Conference on Grid and Cooperative Computing.

[12]  Anthony A. Maciejewski,et al.  Robust CDN replica placement techniques , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[13]  G. Cornuéjols,et al.  A comparison of heuristics and relaxations for the capacitated plant location problem , 1991 .

[14]  FaloutsosMichalis,et al.  On power-law relationships of the Internet topology , 1999 .

[15]  Weifa Liang,et al.  Efficient Algorithms for Capacitated Cloudlet Placements , 2016, IEEE Transactions on Parallel and Distributed Systems.

[16]  Ying Zhang,et al.  NetClust: A Framework for Scalable and Pareto-Optimal Media Server Placement , 2013, IEEE Transactions on Multimedia.

[17]  Balachander Krishnamurthy,et al.  On the use and performance of content distribution networks , 2001, IMW '01.

[18]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[19]  Pierre Hansen,et al.  NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.

[20]  Lili Qiu,et al.  On the placement of Web server replicas , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[21]  Allan Grønlund Jørgensen,et al.  Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D , 2017, ArXiv.

[22]  Shi Li A 1.488 approximation algorithm for the uncapacitated facility location problem , 2013, Inf. Comput..

[23]  Bo Li,et al.  On the optimal placement of web proxies in the Internet , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[24]  Jaroslaw Byrka,et al.  An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem , 2006, SIAM J. Comput..

[25]  Meena Mahajan,et al.  The Planar k-means Problem is NP-hard I , 2009 .

[26]  Hans-Peter Kriegel,et al.  The (black) art of runtime evaluation: Are we comparing algorithms or implementations? , 2017, Knowledge and Information Systems.

[27]  Samir Khuller,et al.  Greedy strikes back: improved facility location algorithms , 1998, SODA '98.

[28]  Chiara Petrioli,et al.  Distributed Dynamic Replica Placement and Request Redirection in Content Delivery Networks , 2007, 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[29]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[30]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[31]  Alexander Schrijver,et al.  On the history of the transportation and maximum flow problems , 2002, Math. Program..

[32]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[33]  Bin Liu,et al.  Clustered K-Center: Effective Replica Placement in Peer-to-Peer Systems , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[34]  Michael Randolph Garey,et al.  The complexity of the generalized Lloyd - Max problem , 1982, IEEE Trans. Inf. Theory.