Fog-Assisted Translation: Towards Efficient Software Emulation on Heterogeneous IoT Devices

Rosário, Vanderson Martins do; Pisani, Flávia; Gomes, Alexandre Rodrigues; Borin, Edson

doi:10.1109/ipdpsw.2018.00196

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…We will now put our theory to practice by applying it to some test cases used in previous studies [23,24]. Namely, the MinMax and Outlier benchmarks.…”

Section: Analyzing Test Casesmentioning

confidence: 99%

“…For the sake of simplicity, we did not include all possible combinations between datasets and applications, as some of the graphs are very similar to the ones shown. All figures employ the same values for stream size (z = 65,536) and custom execution code cost (t, shown in Tables A2 and A3 in Appendix B), but Figures 2-5 use the measured time of 7.3 ms [24] as the cost of sending data to the cloud (s), while Figures 6-9 show what the costs would be like if s was ten times smaller (0.73 ms). In the first case, the results are fog-prone, that is, the fog is more likely to be profitable, as s is about one order of magnitude larger than t. On the other hand, the results in the second case are cloud-prone, as the cloud is more likely to be profitable when we have close values for s and t.…”

Section: Choosing An Approach To Estimate Fmentioning

confidence: 99%

“…As seen in Section 3, we only depend on the variables s, t, and f for our calculations, so we start by finding these values. Tables A2 and A3 in Appendix B show the values for t in milliseconds measured for each benchmark and dataset, and we use the measurement of s = 7.3 ms [24] in all fog-prone test cases and s = 0.73 ms for the cloud-prone ones. Having these values, we can now calculate C f 0 and C c0 for the worst-case scenario from the fog point of view, that is, when f = 1.…”

Section: Deciding Between Fog and Cloud Considering Execution Timementioning

confidence: 99%

See 2 more Smart Citations

Fog vs. Cloud Computing: Should I Stay or Should I Go?

Pisani¹,

Rosário²,

Borin³

2019

Future Internet

Self Cite

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In this article, we work toward the answer to the question “is it worth processing a data stream on the device that collected it or should we send it somewhere else?”. As it is often the case in computer science, the response is “it depends”. To find out the cases where it is more profitable to stay in the device (which is part of the fog) or to go to a different one (for example, a device in the cloud), we propose two models that intend to help the user evaluate the cost of performing a certain computation on the fog or sending all the data to be handled by the cloud. In our generic mathematical model, the user can define a cost type (e.g., number of instructions, execution time, energy consumption) and plug in values to analyze test cases. As filters have a very important role in the future of the Internet of Things and can be implemented as lightweight programs capable of running on resource-constrained devices, this kind of procedure is the main focus of our study. Furthermore, our visual model guides the user in their decision by aiding the visualization of the proposed linear equations and their slope, which allows them to find if either fog or cloud computing is more profitable for their specific scenario. We validated our models by analyzing four benchmark instances (two applications using two different sets of parameters each) being executed on five datasets. We use execution time and energy consumption as the cost types for this investigation.

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“…We will now put our theory to practice by applying it to some test cases used in previous studies [23,24]. Namely, the MinMax and Outlier benchmarks.…”

Section: Analyzing Test Casesmentioning

confidence: 99%

Section: Choosing An Approach To Estimate Fmentioning

confidence: 99%

Section: Deciding Between Fog and Cloud Considering Execution Timementioning

confidence: 99%

See 1 more Smart Citation

Fog vs. Cloud Computing: Should I Stay or Should I Go?

Pisani¹,

Rosário²,

Borin³

2019

Future Internet

Self Cite

View full text Add to dashboard Cite

show abstract

“…DT techniques can be used to improve execution time and space efficiency of programs [2] and to support programming language versatility with High-Level Language Virtual Machines (HLLVM) [3]. They can also be used to maintain support for legacy code by the industry [4,5] or to support new architectures such as RISC-V [6,7]. In dynamic high-level languages, DT can be used to emulate their intermediate representation, such as the Facebook Hip-Hop Virtual Machine [3], the Java HotSpot [8], and Firefox's IonMonkey JavaScript JIT [9].…”

Section: Introductionmentioning

confidence: 99%

Employing Simulation to Facilitate the Design of Dynamic Code Generators

Rosario¹,

Zinsly²,

Rigo³

et al. 2020

Preprint

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Dynamic Translation (DT) is a sophisticated technique that allows the implementation of high-performance emulators and high-level-language virtual machines. In this technique, the guest code is compiled dynamically at runtime. Consequently, achieving good performance depends on several design decisions, including the shape of the regions of code being translated. Researchers and engineers explore these decisions to bring the best performance possible. However, a real DT engine is a very sophisticated piece of software, and modifying one is a hard and demanding task. Hence, we propose using simulation to evaluate the impact of design decisions on dynamic translators and present RAIn, an open-source DT simulator that facilitates the test of DT's design decisions, such as Region Formation Techniques (RFTs). RAIn outputs several statistics that support the analysis of how design decisions may affect the behavior and the performance of a real DT. We validated RAIn running a set of experiments with six well known RFTs (NET, MRET2, LEI, NETPlus, NET-R, and NETPlus-e-r) and showed that it can reproduce well-known results from the literature without the effort of implementing them on a real and complex dynamic translator engine.

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A Novel Sleep Scheduling Strategy on RISC-V Processor

Zhou

Xiao

et al. 2020

J. Phys.: Conf. Ser.

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With the development of the Internet of Things, low-power technology has gradually become a primary factor in processor design. Processor sleeping mode is considered as an effective low-power technique. However, operation errors may occur if the long cycle instruction has not been completed during sleeping mode switch. To solve this problem, a novel sleep scheduling strategy based on RISC-V instruction set architecture is proposed. In this paper, the structure of RISC-V processor with task dispatching mechanism is described firstly. Then, WFI instruction and gating clock technology are adopted to realize the sleep scheduling strategy. Finally, hardware simulation is executed to demonstrate the feasibility of the novel sleep scheduling strategy.

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Fog-Assisted Translation: Towards Efficient Software Emulation on Heterogeneous IoT Devices

Cited by 4 publications

References 23 publications

Fog vs. Cloud Computing: Should I Stay or Should I Go?

Fog vs. Cloud Computing: Should I Stay or Should I Go?

Employing Simulation to Facilitate the Design of Dynamic Code Generators

A Novel Sleep Scheduling Strategy on RISC-V Processor

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