This paper describes a novel approach to event correlation in networks based on coding techniques. Observable symptom events are viewed as a code that identifies the problems that caused them; correlation is performed by decoding the set of observed symptoms. The coding approach has been implemented in SMARTS Event Management System (SEMS), as a server running under Sun Solaris 2.3. Preliminary benchmarks of the SEMS demonstrate that the coding approach provides a speedup at least two orders of magnitude over other published correlation systems. In addition, it is resilient to high rates of symptom loss and false alarms. Finally, the coding approach scales well to very large domains involving thousands of problems.
This paper introduces a novel approach to distributed computing based on delegation-agents, and describes its applications to decentralize network management. Delegation agents are programs that can be dispatched to remote processes, dynamically linked and executed under local or remote control. Unlike scripted agents, delegation agent programs may be written in arbitrary languages, interpreted or compiled. They can thus be more broadly applied to handle such tasks as real-time monitoring, analysis and control of network resources. Distributed management by delegation (MbD) uses this to manage remote elements and domains. MbD provides a paradigm for distributed, flexible, scalable and robust network management that overcomes the key limitations of current centralized management schemes.
This note proposes a statistical perturbation scheme to protect a statistical database against compromise. The proposed scheme can handle the security of numerical as well as nonnumerical sensitive fields or a combination of fields. Furthermore, knowledge of some records in a database does not help to compromise unknown records. We use Chebyshev's inequality to analyze the trade-offs among the magnitude of the perturbations, the error incurred by statistical queries, and the size of the query set to which they apply. We show that if the statistician is given absolute error guarantees, then a compromise is possible, but the cost is made exponential in the size of the database.
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