An adaptive control mechanism for access control in large-scale distributed systems

The highly scalable infrastructure of large-scale distributed systems is very attractive for network services. However, data access is unpredictable in this environment for the reasons of loosely coupled nature and large-scale data storage of such systems. Today, an increasing number of network applications require not only considerations of computation capacity of servers but also accessibility for adequate job allocations. An effective and adaptive mechanism of access control is important in this environment. In our study, the client clustering is used to describe the behaviors of clients and the adaptive server clustering is used to divide the large-scale distributed system into relevant small-scale systems. Since the clients which are assigned to one server cluster have the similar behaviors, we can use the stochastic control and passive measurement to do reliable and adaptive accessibility estimation and client allocation in such a small-scale system. We call this adaptive mechanism of access control based on accessibility estimation and client clustering as ACEC, and the experimental results show that ACEC can significantly reduce the data access cost and guarantee the load balance and controllability of large-scale distributed systems.

[1]  Minrui Fei,et al.  Modelling and stability analysis of MIMO networked control systems withmulti-channel random packet losses , 2013 .

[2]  Alan Weiss,et al.  An Introduction to Large Deviations for Communication Networks , 1995, IEEE J. Sel. Areas Commun..

[3]  Jian Yang,et al.  Online Buffer Fullness Estimation Aided Adaptive Media Playout for Video Streaming , 2011, IEEE Transactions on Multimedia.

[4]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[5]  Balachander Krishnamurthy,et al.  On network-aware clustering of Web clients , 2000, SIGCOMM.

[6]  Henning Schulzrinne,et al.  Client clustering for traffic and location estimation , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[7]  Anne-Marie Kermarrec,et al.  Probabilistic Reliable Dissemination in Large-Scale Systems , 2003, IEEE Trans. Parallel Distributed Syst..

[8]  Indranil Gupta,et al.  Smart Gossip: An Adaptive Gossip-based Broadcasting Service for Sensor Networks , 2006, 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[9]  Srinivasan Seshan,et al.  SPAND: Shared Passive Network Performance Discovery , 1997, USENIX Symposium on Internet Technologies and Systems.

[10]  T. Kempowsky,et al.  Classification as an aid tool for the selection of sensors used for fault detection and isolation , 2006 .

[11]  Aravind Srinivasan,et al.  Clustering and server selection using passive monitoring , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[12]  Wansheng Tang,et al.  Parameters and structure identification of complex delayed networks via pinning control , 2013 .

[13]  Hui Zhang,et al.  Measurement-based optimization techniques for bandwidth-demanding peer-to-peer systems , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[14]  Shuang-Hua Yang,et al.  Time delay and data loss compensation for Internet-based process control systems , 2005 .

[15]  Lei Zhang,et al.  An efficient trajectory-clustering algorithm based on an index tree , 2012 .

[16]  Jinoh Kim,et al.  Using Data Accessibility for Resource Selection in Large-Scale Distributed Systems , 2009, IEEE Transactions on Parallel and Distributed Systems.

[17]  Jinoh Kim,et al.  Passive Network Performance Estimation for Large-Scale, Data-Intensive Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.