EAGER: Quality of Configuration in Large Scale Data Centers

新兴数据中心的管理涉及大量复杂性，因为必须在各个级别（从单个设备到整个系统和服务）正确配置大量资源。配置管理的复杂性导致了许多错误配置和攻击的机会。据估计，在当前的计算机系统中，错误配置造成了62%的停机时间和65%的安全漏洞。由于当前广泛的虚拟化、体系结构异构性和规模不断增加的趋势，这些已经很高的百分比预计将继续增加。该项目试图设计质量指标来表征给定配置的脆弱性，以坏参数值（意外或由恶意实体设置）。质量指标还有望为评估拟议或所需配置变更的替代方案提供指导。描述配置质量的主要挑战是，在实践中，正确配置的规范是不可用的，配置的可行性必须通过分析应用程序的行为来确定。在设计质量度量时要考虑的关键问题与配置管理结构及其所隐含的直接/间接依赖关系有关。该项目将研究从发现配置结构到源代码流分析的方法沿着将发现的信息关联起来的方法。在许多情况下，依赖关系不是确定的，因此质量度量需要考虑模糊值及其组成。 该项目将评估这些指标在Apache Web服务器等当前开放源代码软件环境中的实用性。减少错误配置并快速检测错误配置，预计将对计算系统的可用性、功能正确性、可用性和抵御黑客攻击能力产生重大影响。 在这个项目中探索的指标预计将提供重要的见解，以改善系统的配置在各个阶段，包括设计，部署和动态重新配置。

The management of the emerging data centers involves substantial complexity due to numerous resources that must be properly configured at all levels from individual devices to entire systems and services. The complexity of configuration management leads to numerous opportunities for misconfigurations and attacks. It has been estimated that misconfigurations are responsible for 62% of downtime and 65% of security exploits in current computer systems. These, already high percentages, are expected to continue increasing due to current trends of extensive virtualization, architectural heterogeneity, and increasing size. This project attempts to devise quality metrics to characterize the vulnerability of the given configuration to bad parameter values (set accidentally or by a malicious entity). The quality metrics are also expected to provide guidance for evaluating alternatives to proposed or required configuration changes. The main challenge in characterizing configuration quality is that in practice specifications of correct configuration are not available and the viability of the configuration must be determined by analyzing the application behavior. The key issue to consider in devising the quality metrics relates to the configuration management structure and the direct/indirect dependencies that it implies. The project will examine methods ranging from discovering the configuration structure to flow analysis of the source code along with methods to correlate the discovered information. In many cases, the dependencies are not definitive and hence the quality metrics need to consider fuzzy values and their composition. The project will evaluate the usefulness of the metrics in the context of current open-source software such as Apache web server.Reducing misconfigurations and detecting them quickly is expected to have a substantial impact on computing systems with respect to their availability, functional correctness, usability, and resistance against hacker attacks. The metrics explored in this project are expected to provide important insights into improving the configuration of systems at various stages including design, deployment, and dynamic reconfiguration.