Data Flow Tracking Research Articles

Low Overhead Online Data Flow Tracking for Intermittently Powered Non-Volatile FPGAs

Energy harvesting is an attractive way to power future Internet of Things (IoT) devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable. While Field-programmable Gate Array (FPGAs) have been widely adopted in various embedded systems, it is hard to survive unstable power since all the memory components in FPGA are based on volatile Static Random-access Memory (SRAMs). The emerging non-volatile memory-based FPGAs provide promising potentials to keep configuration data on the chip during power outages. Few works have considered implementing efficient runtime intermediate data checkpoint on non-volatile FPGAs. To realize accumulative computation under intermittent power on FPGA, this article proposes a low-cost design framework, Data-Flow-Tracking FPGA (DFT-FPGA), which utilizes binary counters to track intermediate data flow. Instead of keeping all on-chip intermediate data, DFT-FPGA only targets on necessary data that is labeled by off-line analysis and identified by an online tracking system. The evaluation shows that compared with state-of-the-art techniques, DFT-FPGA can realize accumulative computing with less off-line workload and significantly reduce online roll-back time and resource utilization.

Data Usage Control for Distributed Systems

Data usage control enables data owners to enforce policies over how their data may be used after they have been released and accessed. We address distributed aspects of this problem, which arise if the protected data reside within multiple systems. We contribute by formalizing, implementing, and evaluating a fully decentralized system that (i) generically and transparently tracks protected data across systems, (ii) propagates data usage policies along, and (iii) efficiently and preventively enforces policies in a decentralized manner. The evaluation shows that (i) dataflow tracking and policy propagation achieve a throughput of 21--54% of native execution and (ii) decentralized policy enforcement outperforms a centralized approach in many situations.

Network protocol reverse parsing technique based on dataflow analysis

Reverse parsing unknown network protocol is of great significance in many network security applications.Most of the existing protocol reverse parsing methods can not handle the encryption protocol or get the semantic information of the protocol field.To solve this problem,a network protocol parsing technique based on dataflow analysis was proposed.According to the data flow recording tool developed on Pin platform,it could parse the network protocol with the aid of the dependence analysis based data flow tracking technology,as well as obtain the protocol format and semantic information of each protocol field.The experimental results show that the technique can parse out the protocol format correctly,especially for the encryption protocol,and extract the program behavior semantics of each protocol field.

Libdft

Dynamic data flow tracking (DFT) deals with tagging and tracking data of interest as they propagate during program execution. DFT has been repeatedly implemented by a variety of tools for numerous purposes, including protection from zero-day and cross-site scripting attacks, detection and prevention of information leaks, and for the analysis of legitimate and malicious software. We present libdft, a dynamic DFT framework that unlike previous work is at once fast, reusable, and works with commodity software and hardware. libdft provides an API for building DFT-enabled tools that work on unmodified binaries, running on common operating systems and hardware, thus facilitating research and rapid prototyping. We explore different approaches for implementing the low-level aspects of instruction-level data tracking, introduce a more efficient and 64-bit capable shadow memory, and identify (and avoid) the common pitfalls responsible for the excessive performance overhead of previous studies. We evaluate libdft using real applications with large codebases like the Apache and MySQL servers, and the Firefox web browser. We also use a series of benchmarks and utilities to compare libdft with similar systems. Our results indicate that it performs at least as fast, if not faster, than previous solutions, and to the best of our knowledge, we are the first to evaluate the performance overhead of a fast dynamic DFT implementation in such depth. Finally, libdft is freely available as open source software.

ParaLog

Instruction-grain lifeguards monitor the events of a running application at the level of individual instructions in order to identify and help mitigate application bugs and security exploits. Because such lifeguards impose a 10-100X slowdown on existing platforms, previous studies have proposed hardware designs to accelerate lifeguard processing. However, these accelerators are either tailored to a specific class of lifeguards or suitable only for monitoring singlethreaded programs. We present ParaLog, the first design of a system enabling fast online parallel monitoring of multithreaded parallel applications. ParaLog supports a broad class of software-defined lifeguards. We show how three existing accelerators can be enhanced to support online multithreaded monitoring, dramatically reducing lifeguard overheads. We identify and solve several challenges in monitoring parallel applications and/or parallelizing these accelerators, including (i) enforcing inter-thread data dependences, (ii) dealing with inter-thread effects that are not reflected in coherence traffic, (iii) dealing with unmonitored operating system activity, and (iv) ensuring lifeguards can access shared metadata with negligible synchronization overheads. We present our system design for both Sequentially Consistent and Total Store Ordering processors. We implement and evaluate our design on a 16 core simulated CMP, using benchmarks from SPLASH-2 and PARSEC and two lifeguards: a data-flow tracking lifeguard and a memory-access checker lifeguard. Our results show that (i) our parallel accelerators improve performance by 2-9X and 1.13-3.4X for our two lifeguards, respectively, (ii) we are 5-126X faster than the time-slicing approach required by existing techniques, and (iii) our average overheads for applications with eight threads are 51% and 28% for the two lifeguards, respectively.

ParaLog

Instruction-grain lifeguards monitor the events of a running application at the level of individual instructions in order to identify and help mitigate application bugs and security exploits. Because such lifeguards impose a 10-100X slowdown on existing platforms, previous studies have proposed hardware designs to accelerate lifeguard processing. However, these accelerators are either tailored to a specific class of lifeguards or suitable only for monitoring singlethreaded programs. We present ParaLog, the first design of a system enabling fast online parallel monitoring of multithreaded parallel applications. ParaLog supports a broad class of software-defined lifeguards. We show how three existing accelerators can be enhanced to support online multithreaded monitoring, dramatically reducing lifeguard overheads. We identify and solve several challenges in monitoring parallel applications and/or parallelizing these accelerators, including (i) enforcing inter-thread data dependences, (ii) dealing with inter-thread effects that are not reflected in coherence traffic, (iii) dealing with unmonitored operating system activity, and (iv) ensuring lifeguards can access shared metadata with negligible synchronization overheads. We present our system design for both Sequentially Consistent and Total Store Ordering processors. We implement and evaluate our design on a 16 core simulated CMP, using benchmarks from SPLASH-2 and PARSEC and two lifeguards: a data-flow tracking lifeguard and a memory-access checker lifeguard. Our results show that (i) our parallel accelerators improve performance by 2-9X and 1.13-3.4X for our two lifeguards, respectively, (ii) we are 5-126X faster than the time-slicing approach required by existing techniques, and (iii) our average overheads for applications with eight threads are 51% and 28% for the two lifeguards, respectively.

Kaleidaseq: a Web-based tool to monitor data flow in a high throughput sequencing facility.

Tracking data flow in high throughput sequencing is important in maintaining a consistent number of successfully sequenced samples, making decisions on scheduling the flow of sequencing steps, resolving problems at various steps and tracking the status of different projects. This is especially critical when the laboratory is handling a multitude of projects. We have built a Web-based data flow tracking package, called Kaleidaseq, which allows us to monitor the flow and quality of sequencing samples through the steps of preparation of library plates, plaque-picking, preparation of templates, conducting sequencing reactions, loading of samples on gels, base-calling the traces, and calculating the quality of the sequenced samples. Kaleidaseq's suite of displays allows for outstanding monitoring of the production sequencing process. The online display of current information that Kaleidaseq provides on both project status and process queues sorted by project enables accurate real-time assessment of the necessary samples that must be processed to complete the project. This information allows the process manager to allocate future resources optimally and schedule tasks according to scientific priorities. Quality of the sequenced samples can be tracked on a daily basis, which allows the sequencing laboratory to maintain a steady performance level and quickly resolve dips in quality. Kaleidaseq has a simple easy-to-use interface that allows access to all major functions and process queues from one Web page. This software package is modular and designed to allow additional processing steps and new monitoring variables to be added and tracked with ease. Access to the underlying relational database is through the Perl DBI interface, which allows for the use of different relational databases. Kaleidaseq is available for free use by the academic community from http://www.cshl.org/kaleidaseq.