Efficient off-board deployment and customization of virtual machine-based embedded systems

Courbot, Alexandre; Grimaud, Gilles; Vandewalle, Jean-Jacques

doi:10.1145/1698772.1698779

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…The virtual abstraction layer integrates various resources through resource virtualization technology to achieve unified management. Although there is currently no resource virtualization method for satellites, the virtualization methods for the cloud platform, such as virtual machines or containers, have been used in embedded systems [24,25]. These technologies will be used to implement the virtualized packaging of onboard devices.…”

Section: The Architecture and Application Scenarios Of Satellite Ementioning

confidence: 99%

Satellite Edge Computing for the Internet of Things in Aerospace

Wang

Yang

Guo

et al. 2019

Sensors

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As one of the information industry’s future development directions, the Internet of Things (IoT) has been widely used. In order to reduce the pressure on the network caused by the long distance between the processing platform and the terminal, edge computing provides a new paradigm for IoT applications. In many scenarios, the IoT devices are distributed in remote areas or extreme terrain and cannot be accessed directly through the terrestrial network, and data transmission can only be achieved via satellite. However, traditional satellites are highly customized, and on-board resources are designed for specific applications rather than universal computing. Therefore, we propose to transform the traditional satellite into a space edge computing node. It can dynamically load software in orbit, flexibly share on-board resources, and provide services coordinated with the cloud. The corresponding hardware structure and software architecture of the satellite is presented. Through the modeling analysis and simulation experiments of the application scenarios, the results show that the space edge computing system takes less time and consumes less energy than the traditional satellite constellation. The quality of service is mainly related to the number of satellites, satellite performance, and task offloading strategy.

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Section: The Architecture and Application Scenarios Of Satellite Ementioning

confidence: 99%

Satellite Edge Computing for the Internet of Things in Aerospace

Wang

Yang

Guo

et al. 2019

Sensors

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“…One common offline method is to first translate the Java code to C as an intermediate language, and take advantage of the high quality C compilers available [26]. Courbot et al describe a different approach, where code size is reduced by partly running the application before it is loaded onto the node, allowing them to eliminate code that is only needed during initialisation [8]. Although the initialised objects are translated to C structures that are compiled and linked into a single image, the bytecode is still interpreted.…”

Section: Related Workmentioning

confidence: 99%

Improved Ahead-of-time Compilation of Stack-based JVM Bytecode on Resource-constrained Devices

Reijers¹,

Shih²

2019

ACM Trans. Sen. Netw.

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Many virtual machines exist for sensor nodes with only a few KB RAM and tens to a few hundred KB flash memory. They pack an impressive set of features, but suffer from a slowdown of one to two orders of magnitude compared to optimised native code, reducing throughput and increasing power consumption.Compiling bytecode to native code to improve performance has been studied extensively for larger devices, but the restricted resources on sensor nodes mean most modern techniques cannot be applied. Simply replacing bytecode instructions with predefined sequences of native instructions is known to improve performance, but produces code several times larger than the optimised C equivalent, limiting the size of programmes that can fit onto a device. This paper identifies the major sources of overhead resulting from this basic approach, and presents optimisations to remove most of the remaining performance overhead, and over half the size overhead, reducing them to 69% and 91% respectively. While this increases the size of the VM, the break-even point at which this fixed cost is compensated for is well within the range of memory available on a sensor device, allowing us to both improve performance and load more code on a device.

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“…Then, it uses the built stack of alive objects to perform a static analysis, as described in Section 6.3, with concrete type information. Java in The Small (JITS) [CGV10] uses a hybrid approach to select the used parts of a program, and then loads them inside a binary image. A dedicated VM loads the binary image at startup.…”

Section: Hybrid Analysis-based Techniquesmentioning

confidence: 99%

Run-Fail-Grow: Creating Tailored Object-Oriented Runtimes.

Polito¹,

Fabresse²,

Bouraqadi³

et al. 2017

JOT

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Producing a small deployment version of an application is a challenge because static abstractions such as packages cannot anticipate the use of their parts at runtime. Thus, an application often occupies more memory than actually needed. Tailoring is one of the main solutions to this problem i.e., extracting used code units such as classes and methods of an application. However, existing tailoring techniques are mostly based on static type annotations. These techniques cannot efficiently tailor applications in all their extent (e.g., runtime object graphs and metadata) nor be used in the context of dynamically-typed languages.We propose a run-fail-grow technique to tailor applications using their runtime execution. Run-fail-grow launches (a) a reference application containing the original application to tailor and (b) a nurtured application containing only a seed with a minimal set of code units the user wants to ensure in the final application. The nurtured application is executed, failing when it founds missing objects, classes or methods. On failure, the necessary elements are installed into the nurtured application from the reference one, and the execution resumes. The nurtured application is executed until it finishes, or until the developer explicitly finishes it, for example in the case of a web application. resulting in an object memory (i.e., a heap) with only objects, classes and methods required to execute the application.To validate our approach we implemented a tool based on Virtual Machine modifications, namely Tornado. Tornado succeeds to create very small memory footprint versions of applications e.g., a simple objectoriented heap of 11kb. We show how tailoring works on application code, base and third-party libraries even supporting human interaction with user Technology, vol. 16, no. 3, 2017Technology, vol. 16, no. 3, , pages 2:1-36. doi:10.5381/jot.2017 2 · G. Polito, L. Fabresse, N. Bouraqadi, S. Ducasse interfaces. These experiments show memory savings ranging from 95% to 99%.

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Efficient off-board deployment and customization of virtual machine-based embedded systems

Cited by 7 publications

References 28 publications

Satellite Edge Computing for the Internet of Things in Aerospace

Satellite Edge Computing for the Internet of Things in Aerospace

Improved Ahead-of-time Compilation of Stack-based JVM Bytecode on Resource-constrained Devices

Run-Fail-Grow: Creating Tailored Object-Oriented Runtimes.

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