Performance and security in parallel and distributed systems

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Distributed caching involves storing data in strategic locations within a network and is well-understood in the context of file access, memory access and media content access where it has been studied for many years. In each new environment where caching is implemented, four main questions must be answered: how much information should be stored, where should it be stored, how many copies should be kept at one time, and how does the system determine what is valuable? Depending on system functionality, the type of distribution, system scale, and the amount of parallelism, the answers differ. In this research program, I will investigate several representative emerging systems to explore the costs/benefits of these caching principles, as they apply. Four examples of systems where the replication, location and valuation strategies are expected to differ are 1) malware signatures/traffic profiles in network intrusion detection systems, 2) data and instruction caches in multi-core processors, 3) video objects/metadata in media content distribution networks, and 4) database/web content in multi-tier client-server applications. The expected outcomes are general principles as well as context-specific analysis tools and prototype systems that will give insight to designers in building efficient and secure distributed systems.