An Adaptive Application Framework with Customizable Quality Metrics

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.

Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.

Toward Trustworthy Web Services Coordination

In this chapter, the authors present an overview of recent works on enhancing the trustworthiness of web services coordination for business activities and transactions. The approach is based on what they call application-aware Byzantine fault tolerance. They argue that it is impractical to apply general-purpose Byzantine fault tolerance algorithms for such systems in a straightforward manner. Instead, by exploiting the application semantics, much lighter weight solutions can be designed to enhance intrusion tolerance and, hence, the trustworthiness of systems that require web services coordination.

Toward Trustworthy Web Services Coordination

In this article, we present an overview of our recent works on enhancing the trustworthiness of Web services coordination for business activities and transactions. The approach is based on what we call application-aware Byzantine fault tolerance. We argue that it is impractical to apply general-purpose Byzantine fault tolerance algorithms for such systems in a straightforward manner. Instead, by exploiting the application semantics, much lighter weight solutions can be designed to enhance intrusion tolerance, and hence the trustworthiness of systems that require Web services coordination.