Efficient Dissemination of Information in Computer Networks

Many distributed applications require that information be propagated in the system efficiently. Propagation of information, as soon as it becomes known, via direct messages, is prohibitively expensive even in relatively small networks. Fortunately, in many applications, one can use lazy propagation of information, where each site eventually obtains all the information in the network. A common method for spreading information is by exchanging information logs amongst sites in the network. Several mechanisms for log propagation have already been proposed. All these mechanisms require that information log must be accompanied with temporal information, which is the state of the site sending the message, when the message is sent. In this paper, we eliminate the need for sending the log and temporal components together. We demonstrate that the separation of these two components leads to a more efficient and flexible solution of the log propagation problem. Also, our solution is more scalable, which makes it more suitable for large and geographically dispersed networks.

[1]  Richard D. Schlichting,et al.  Fail-stop processors: an approach to designing fault-tolerant computing systems , 1983, TOCS.

[2]  Divyakant Agrawal,et al.  Storage Efficient Replicated Databases , 1990, IEEE Trans. Knowl. Data Eng..

[3]  Joseph Y. Halpern,et al.  Knowledge and common knowledge in a distributed environment , 1984, JACM.

[4]  Divyakant Agrawal,et al.  A Nonblocking Quorum Consensus Protocol for Replicated Data , 1991, IEEE Trans. Parallel Distributed Syst..

[5]  Kenneth P. Birman,et al.  Low cost management of replicated data in fault-tolerant distributed systems , 1986, TOCS.

[6]  Hector Garcia-Molina,et al.  Consistency in a partitioned network: a survey , 1985, CSUR.

[7]  Michael J. Fischer,et al.  Sacrificing serializability to attain high availability of data in an unreliable network , 1982, PODS.

[8]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[9]  Philip A. Bernstein,et al.  The failure and recovery problem for replicated databases , 1983, PODC '83.

[10]  Amr El Abbadi,et al.  Integrating Security with Fault-Tolerant Distributed Databases , 1990, Comput. J..

[11]  Doug Terry,et al.  Epidemic algorithms for replicated database maintenance , 1988, OPSR.

[12]  Arthur J. Bernstein,et al.  Efficient solutions to the replicated log and dictionary problems , 1984, PODC '84.

[13]  Fred B. Schneider,et al.  Synchronization in Distributed Programs , 1982, TOPL.

[14]  Michael Stonebraker,et al.  Concurrency Control and Consistency of Multiple Copies of Data in Distributed Ingres , 1979, IEEE Transactions on Software Engineering.

[15]  Laura M. Haas,et al.  Distributed deadlock detection , 1983, TOCS.

[16]  Robert H. Thomas,et al.  A Majority consensus approach to concurrency control for multiple copy databases , 1979, ACM Trans. Database Syst..

[17]  Meichun Hsu,et al.  Two Pase Gossip: Managing Distributed Event Histories , 1989, Inf. Sci..

[18]  Ophir Frieder Communications issues in data engineering: 'have bandwidth-will move data' , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

[19]  David K. Gifford,et al.  Weighted voting for replicated data , 1979, SOSP '79.

[20]  Barbara Liskov,et al.  Highly available distributed services and fault-tolerant distributed garbage collection , 1986, PODC '86.