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1. WRENCH: A Framework for Simulating Workflow Management Systems

   https://doi.org/10.1109/WORKS.2018.00013  

   Casanova, Henri; Pandey, Suraj; Oeth, James; Tanaka, Ryan; Suter, Frederic; Ferreira da Silva, Rafael (October 2018, 13th Workshop on Workflows in Support of Large-Scale Science (WORKS'18))

   Scientific workflows are used routinely in numerous scientific domains, and Workflow Management Systems (WMSs) have been developed to orchestrate and optimize workflow executions on distributed platforms. WMSs are complex software systems that interact with complex software infrastructures. Most WMS research and development activities rely on empirical experiments conducted with full-fledged software stacks on actual hardware platforms. Such experiments, however, are limited to hardware and software infrastructures at hand and can be labor- and/or time-intensive. As a result, relying solely on real-world experiments impedes WMS research and development. An alternative is to conduct experiments in simulation. In this work we present WRENCH, a WMS simulation framework, whose objectives are (i) accurate and scalable simulations; and (ii) easy simulation software development. WRENCH achieves its first objective by building on the SimGrid framework. While SimGrid is recognized for the accuracy and scalability of its simulation models, it only provides low-level simulation abstractions and thus large software development efforts are required when implementing simulators of complex systems. WRENCH thus achieves its second objective by providing high-level and directly re-usable simulation abstractions on top of SimGrid. After describing and giving rationales for WRENCH’s software architecture and APIs, we present a case study in which we apply WRENCH to simulate the Pegasus production WMS. We report on ease of implementation, simulation accuracy, and simulation scalability so as to determine to which extent WRENCH achieves its two above objectives. We also draw both qualitative and quantitative comparisons with a previously proposed workflow simulator. 

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2. WRENCH: Workflow Management System Simulation Workbench

   Casanova, H; Pandey, S; Oeth, J.; Tanaka, R.; Suter, F.; Ferreira da Silva, R. (November 2018, Workshop on Workflows in Support of Large-Scale Science)

   Scientific workflows are used routinely in numerous scientific domains, and Workflow Management Systems (WMSs) have been developed to orchestrate and optimize workflow executions on distributed platforms. WMSs are complex software systems that interact with complex software infrastructures. Most WMS research and development activities rely on empirical experiments conducted with full-fledged software stacks on actual hardware platforms. Such experiments, however, are limited to hardware and software infrastructures at hand and can be labor- and/or time-intensive. As a result, relying solely on real-world experiments impedes WMS research and development. An alternative is to conduct experiments in simulation. In this work we present WRENCH, a WMS simulation framework, whose objectives are (i)~accurate and scalable simulations; and (ii)~easy simulation software development. WRENCH achieves its first objective by building on the SimGrid framework. While SimGrid is recognized for the accuracy and scalability of its simulation models, it only provides low-level simulation abstractions and thus large software development efforts are required when implementing simulators of complex systems. WRENCH thus achieves its second objective by providing high-level and directly re-usable simulation abstractions on top of SimGrid. After describing and giving rationales for WRENCH's software architecture and APIs, we present a case study in which we apply WRENCH to simulate the Pegasus production WMS. We report on ease of implementation, simulation accuracy, and simulation scalability so as to determine to which extent WRENCH achieves its two above objectives. We also draw both qualitative and quantitative comparisons with a previously proposed workflow simulator. 

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3. FreePart: Hardening Data Processing Software via Framework-based Partitioning and Isolation

   https://doi.org/10.1145/3623278.3624760  

   Ahad, Ali; Wang, Gang; Kim, Chung Hwan; Jana, Suman; Lin, Zhiqiang; Kwon, Yonghwi (March 2024, ASPLOS)

   Data processing oriented software, especially machine learning applications, are heavily dependent on standard frameworks/libraries such as TensorFlow and OpenCV. As those frameworks have gained significant popularity, the exploitation of vulnerabilities in the frameworks has become a critical security concern. While software isolation can minimize the impact of exploitation, existing approaches suffer from difficulty analyzing complex program dependencies or excessive overhead, making them ineffective in practice. We propose FreePart, a framework-focused software partitioning technique specialized for data processing applications. It is based on an observation that the execution of a data processing application, including data flows and usage of critical data, is closely related to the invocations of framework APIs. Hence, we conduct a temporal partitioning of the host application’s execution based on the invocations of framework APIs and the data objects used by the APIs. By focusing on data accesses at runtime instead of static program code, it provides effective and practical isolation from the perspective of data. Our evaluation on 23 applications using popular frameworks (e.g., OpenCV, Caffe, PyTorch, and TensorFlow) shows that FreePart is effective against all attacks composed of 18 real-world vulnerabilities with a low overhead (3.68%). 

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4. Intent to share: enhancing Android inter-component communication for distributed devices

   https://doi.org/10.1145/3197231.3197245  

   Cruz, Breno Dantas; Tilevich, Eli (January 2018, Proceedings of the 5th International Conference on Mobile Software Engineering and Systems)

   Data-intensive applications in diverse domains, including video streaming, gaming, and health monitoring, increasingly require that mobile devices directly share data with each other. However, developing distributed data sharing functionality introduces low-level, brittle, and hard-to-maintain code into the mobile codebase. To reconcile the goals of programming convenience and performance efficiency, we present a novel middleware framework that enhances the Android platform's component model to support seamless and efficient inter-device data sharing. Our framework provides a familiar programming interface that extends the ubiquitous Android Inter-Component Communication (ICC), thus lowering the learning curve. Unlike middleware platforms based on the RPC paradigm, our programming abstractions require that mobile application developers think through and express explicitly data transmission patterns, thus treating latency as a first-class design concern. Our performance evaluation shows that using our framework incurs little performance overhead, comparable to that of custom-built implementations. By providing reusable programming abstractions that preserve component encapsulation, our framework enables Android devices to efficiently share data at the component level, providing powerful building blocks for the development of emerging distributed mobile applications. 

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5. SoK: Practical Foundations for Software Spectre Defenses

   https://doi.org/10.1109/SP46214.2022.9833707  

   Cauligi, Sunjay; Disselkoen, Craig; Moghimi, Daniel; Barthe, Gilles; Stefan, Deian (May 2022, 2022 IEEE Symposium on Security and Privacy (SP))

   Holz, Thorsten; Ristenpart, Thomas (Ed.)

   Spectre vulnerabilities violate our fundamental assumptions about architectural abstractions, allowing attackers to steal sensitive data despite previously state-of-the-art countermeasures. To defend against Spectre, developers of verification tools and compiler-based mitigations are forced to reason about microarchitectural details such as speculative execution. In order to aid developers with these attacks in a principled way, the research community has sought formal foundations for speculative execution upon which to rebuild provable security guarantees.This paper systematizes the community’s current knowledge about software verification and mitigation for Spectre. We study state-of-the-art software defenses, both with and without associated formal models, and use a cohesive framework to compare the security properties each defense provides. We explore a wide variety of tradeoffs in the expressiveness of formal frameworks, the complexity of defense tools, and the resulting security guarantees. As a result of our analysis, we suggest practical choices for developers of analysis and mitigation tools, and we identify several open problems in this area to guide future work on grounded software defenses. 

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