Performance and security in parallel and distributed systems

分布式缓存涉及将数据存储在网络内的战略位置，并且在文件访问、存储器访问和媒体内容访问的上下文中被很好地理解，其中它已经被研究了多年。在每个实现缓存的新环境中，必须回答四个主要问题：应该存储多少信息，应该存储在哪里，一次应该保存多少副本，以及系统如何确定哪些是有价值的？根据系统功能、分布类型、系统规模和并行度的不同，答案会有所不同。在本研究计划中，我将调查几个具有代表性的新兴系统，以探索这些缓存原则的成本/收益（如果适用）。复制、定位和评估策略预期不同的系统的四个示例是1）网络入侵检测系统中的恶意软件签名/流量配置文件，2）多核处理器中的数据和指令缓存，3）媒体内容分发网络中的视频对象/元数据，以及4）多层客户端-服务器应用中的数据库/web内容。预期的成果是一般原则以及特定于上下文的分析工具和原型系统，这将使设计人员在构建高效和安全的分布式系统。 作为一个具体的例子，系统用性能来支付安全性，因为加密数据或检查恶意软件所需的额外处理是从系统的主要任务中提取的。 对于使用台式计算机的网络入侵检测来说，这是一个小问题，但对于资源紧张的大型系统（如无线网状网络和传感器网络）来说，这是一个严重的问题，因为这些系统缺乏所有节点执行复杂流量过滤的资源。缓存数据、恶意代码配置文件和/或流量统计信息限制了原始数据暴露于不安全的网络，并启用了选择性流量过滤，从而提供每字节的高保护能力。我相信，将我以前缓存经验中的想法应用到这个新领域，有可能产生新的结果。

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. As a concrete example, systems pay for security with performance, since the extra processing needed to encrypt data or to check for malware is taken from the system's primary tasks. This is a minor issue for network intrusion detection using desktop computers, but is crippling to resource-challenged, large-scale systems, such as wireless mesh and sensor networks that lack resources for all nodes to perform sophisticated traffic filtering. Caching data, malicious code profiles and/or traffic statistics limits exposure of raw data to the insecure network and enables selective traffic filtering that provides high protection power per byte. I believe that ideas from my previous caching experience applied to this new area has the potential to yield novel results.