Performance and Resource Analysis on the JavaScript Runtime for IoT Devices

Sin, Dongig; Shin, Dongkun

doi:10.1007/978-3-319-42085-1_50

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Despite an extensive search, we did not find publicly available sets of IoT-specific JavaScript benchmarks to evaluate our system. Prior work 14 has built their own IoT-specific JavaScript benchmarks 2 . We followed a similar approach and built two IoT-specific benchmarks: (1) a factorial application, which computes the factorial of a very large number and uses closures to store the computed digits (i.e., in a very large expanding array), and (2), a regulator application, which models an IoT edge component which receives temperature measurement data from different sensors 3 over a Pub/Sub interface, keeps the previous n values for m sensors, and periodically computes an optimal power adjustment to be sent to an actuator.…”

Section: Methodsmentioning

confidence: 99%

“…Note that the memory usage of the regulator is similar to the memory usage of typical IoT-specific benchmarks. 14 In addition, for experiments 1 and 2, we also used some benchmarks from the Chromium Octane 57 suite, which were originally designed to stress-test the performance of the V8 JavaScript engine in the Chrome web browser. They run synchronously and hence are not representative of IoT applications, but we nevertheless use them to assess the universality of our framework, and for performance testing of ThingsMigrate under intense workload.…”

Section: Methodsmentioning

confidence: 99%

“…After instantiating a LexicalScope object, the algorithm proceeds to instrument the function body. We iterate through the nodes in the function body (lines [11][12][13][14][15][16] and invoke instrumentNode on each AST node recursively, some of which return an injection. The injection is a piece of code that is placed immediately after the expression that was instrumented (line 14).…”

Section: Code Instrumentationmentioning

confidence: 99%

“…Next, the functions (closures) defined in the scope are injected, again wrapped with the Scope.addFunction call (lines 8-10). Then the local variables of the scope are injected, each initialized with the same value it had at the time of snapshot (lines [12][13][14][15][16][17][18][19]. For some variables during this step, it might happen that the scopes they depend on have not been reconstructed yet.…”

Section: Code Restorationmentioning

confidence: 99%

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ThingsMigrate: Platform‐independent migration of stateful JavaScript Internet of Things applications

et al. 2020

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The Internet of Things (IoT) has gained wide popularity both in academic and industrial contexts. Unlike traditional embedded devices with specialized firmwares, modern IoT devices accommodate general‐purpose operating systems, allowing developers to run more sophisticated applications written in high‐level languages like JavaScript. Because IoT devices are subject to resource constraints like available battery power, we need to dynamically migrate a running process between different devices to prevent losing state. However, it is challenging to apply migration techniques using memory snapshots across the heterogeneous pool of IoT devices. We present ThingsMigrate, a middleware providing platform‐independent migration of JavaScript processes across IoT devices. Prior to execution, ThingsMigrate instruments the source code of a given program to expose its internal state. During run‐time, the transformed program produces on demand a JSON snapshot of its current state, from which new code is generated to resume execution. Thus, ThingsMigrate enables process migration entirely in the application space without any modifications to the underlying virtual machine (VM), providing VM‐independence. We present three versions of ThingsMigrate, each building on the previous to optimize for run‐time latency and memory consumption. We report on the experience of building each successive version and discuss the insights gained and the learning outcomes. We evaluated ThingsMigrate against standard benchmarks, over two IoT platforms and a cloud‐like environment. We show that it can migrate even highly CPU‐intensive applications, with average run‐time latency overhead of 33% and memory overhead of 78%. ThingsMigrate supports multiple subsequent migrations without introducing additional overhead over each subsequent migration.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Code Instrumentationmentioning

confidence: 99%

Section: Code Restorationmentioning

confidence: 99%

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ThingsMigrate: Platform‐independent migration of stateful JavaScript Internet of Things applications

et al. 2020

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“…Table 1 summarises the workloads used in our evaluation. We construct the first three workloads that implement common IoT scenarios as proposed in [13] and extract a purely computing-intensive workload 'scheduler' from Octane. We divide the total energy of running each workload into two parts.…”

Section: Design and Implementationmentioning

confidence: 99%

Energy analysis for internet of things software: A simulator approach

2020

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This Letter reports a method to obtain fine-grained energy consumption analysis of programs running on the low-power internet of things (IoT) devices. In the energy estimation domain, most state-ofthe-art solutions focus on the coarse-grained approach to monitor the energy consumption of a device or an application. However, few solutions addressed energy monitoring to analyse fine-grained energy consumption of a program, especially for IoT appliances. Therefore, the authors present a fine-grained gem5-based energy profiling tool to help developers to evaluate the energy efficiency of hot spots in their programs. The authors demonstrate its usability by applying it to profiling garbage collection and property lookup in Jerryscript, a JavaScript engine specifically designed for resource-constrained IoT devices, and discover that these two operations are consuming, as they account for 17.67 and 12.26% of total energy, respectively. Therefore, this tool can aid the developers to gain insight into the energy efficiency of their software.

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Abnormality Diagnosis from Ambient Data: IoT Data Sequences in Real Time

Samanta

Banerjee

2021

Computationally Intensive Statistics for Intelligent IoT

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Performance and Resource Analysis on the JavaScript Runtime for IoT Devices

Cited by 7 publications

References 9 publications

ThingsMigrate: Platform‐independent migration of stateful JavaScript Internet of Things applications

ThingsMigrate: Platform‐independent migration of stateful JavaScript Internet of Things applications

Energy analysis for internet of things software: A simulator approach

Abnormality Diagnosis from Ambient Data: IoT Data Sequences in Real Time

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