Programming asynchronous layers with CLARITY

Chandrasekaran, Prakash; Conway, Christopher L.; Joy, Joseph; Rajamani, Sriram K.

doi:10.1145/1287624.1287636

Cited by 17 publications

(12 citation statements)

References 34 publications

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“…For example, constructs to wait for a device condition safely, with internally implemented timeouts, can reduce the problem of hung systems due to devices. Past work on language support for concurrency in drivers has investigated pro- viding similar language features to avoid correctness violations [8] .…”

Section: Analysis Resultsmentioning

confidence: 99%

Tolerating hardware device failures in software

Kadav

Renzelmann

Swift

2009

Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles

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Hardware devices can fail, but many drivers assume they do not. When confronted with real devices that misbehave, these assumptions can lead to driver or system failures. While major operating system and device vendors recommend that drivers detect and recover from hardware failures, we find that there are many drivers that will crash or hang when a device fails. Such bugs cannot easily be detected by regular stress testing because the failures are induced by the device and not the software load.This paper describes Carburizer, a code-manipulation tool and associated runtime that improves system reliability in the presence of faulty devices. Carburizer analyzes driver source code to find locations where the driver incorrectly trusts the hardware to behave. Carburizer identified almost 1000 such bugs in Linux drivers with a false positive rate of less than 8 percent. With the aid of shadow drivers for recovery, Carburizer can automatically repair 840 of these bugs with no programmer involvement.To facilitate proactive management of device failures, Carburizer can also locate existing driver code that detects device failures and inserts missing failure-reporting code. Finally, the Carburizer runtime can detect and tolerate interrupt-related bugs, such as stuck or missing interrupts.

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Section: Analysis Resultsmentioning

confidence: 99%

Tolerating hardware device failures in software

Kadav

Renzelmann

Swift

2009

Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles

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“…The column titled FlowTalk executable indicates the size of the applications 8 . The value 1532+46 means that the application CounterToLeds consumes 1532 bytes of ROM and 46 bytes of RAM.…”

Section: Memory and Battery Consumptionmentioning

confidence: 99%

“…The SensorToLeds application provided with the TinyOS distribution runs 8. The nesC version of those applications are CntToLeds, CntToLedsAndRfm, SenseToLeds, SenseToRfm, RfmToLeds.…”

Section: Memory and Battery Consumptionmentioning

confidence: 99%

FlowTalk: Language Support for Long-Latency Operations in Embedded Devices

Bergel

Harrison

Cahill

et al. 2011

IIEEE Trans. Software Eng.

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Abstract-Wireless sensor networks necessitate a programming model different from those used to develop desktop applications. Typically, resources in terms of power and memory are constrained. C is the most common programming language used to develop applications on very small embedded sensor devices. We claim that C does not provide efficient mechanisms to address the implicit asynchronous nature of sensor sampling. C applications for these devices suffer from a disruption in their control flow. In this paper, we present FlowTalk, a new object-oriented programming language aimed at making software development for wireless embedded sensor devices easier. FlowTalk is an object-oriented programming language in which dynamicity (e.g., object creation) has been traded for a reduction in memory consumption. The event model that traditionally comes from using sensors is adapted in FlowTalk with controlled disruption, a light-weight continuation mechanism. The essence of our model is to turn asynchronous long-latency operations into synchronous and blocking method calls. FlowTalk is built for TinyOS and can be used to develop applications that can fit in 4 kB of memory for a large number of wireless sensor devices.

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“…Several large industrial concurrent systems and Internet services [28] including Ajax-based scripts, routers, and web servers use this model of concurrent computation. A number of libraries, e.g., Intel's threading building blocks [19] as well as languages [3] are available to support the design of concurrent systems with tasks.…”

Section: Introductionmentioning

confidence: 99%

Static data race detection for concurrent programs with asynchronous calls

Kahlon

Sinha

Kruus

et al. 2009

Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Found

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A large number of industrial concurrent programs are being designed based on a model which combines threads with event-based communication. These programs consist of several threads which perform computation by dispatching tasks to other threads via asynchronous function calls. These asynchronous function calls are implemented using function objects, which are essentially wrappers containing a pointer to the function that should be executed on a particular thread with the corresponding arguments. In many cases, the arguments, in turn, contain function objects which serve as callbacks. Verifying such programs which involves reasoning about complex concurrency constructs comprising function pointers and callback functions is extremely tricky especially in the presence of recursion. In this paper, we present a fast and accurate static data race detection technique for multi-threaded C programs with asynchronous function calls and demonstrate its application to real-life software.

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Programming asynchronous layers with CLARITY

Cited by 17 publications

References 34 publications

Tolerating hardware device failures in software

Tolerating hardware device failures in software

FlowTalk: Language Support for Long-Latency Operations in Embedded Devices

Static data race detection for concurrent programs with asynchronous calls

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