Beyond the Fog: Bringing Cross-Platform Code Execution to Constrained IoT Devices

Pisani, Flávia; Brunetta, Jeferson Rech; Rosário, Vanderson Martins do; Borin, Edson

doi:10.1109/sbac-pad.2017.10

Cited by 13 publications

(16 citation statements)

References 9 publications

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“…Architectural models [42], SPEs [43,13], and engines for certain application scenarios such as IoT are emerging. Architecture that mixes elements deployed on edge computing resources and the cloud is provided in the literature [43,44,42].…”

Section: Second: Distributed Executionmentioning

confidence: 99%

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Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

Assuno

Veith

Buyya

2018

Journal of Network and Computer Applications

254

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Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them.

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Section: Second: Distributed Executionmentioning

confidence: 99%

“…More recently, software frameworks [12,13] and architectures have been proposed for carrying out data stream processing using constrained resources located at the edge of the Internet. This scenario introduces additional challenges regarding application scheduling, resource elasticity, and programming models.…”

Section: Introductionmentioning

confidence: 99%

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

Assuno

Veith

Buyya

2018

Journal of Network and Computer Applications

254

View full text Add to dashboard Cite

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“…Then, we perform a custom code operation (cost t). We are particularly interested in filtering procedures, as these can be simple enough to run on resource-constrained devices [23] and have the potential to decrease the amount of data sent to the cloud drastically, a situation that is posed to become a problem with the large-scale adoption of technologies such as the Internet of Things. For that reason, in this investigation, we consider that our custom operations are filters, that is, operations that decide whether or not each stream value should be sent to the cloud.…”

Section: General Equationsmentioning

confidence: 99%

“…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%

“…MinMax is a simple filter, which only allows numbers outside of a certain range to pass, and Outlier is a procedure that executes Tukey's test [25] and Outlier 256, the same program, but with a window of 256 values instead. The parameters of each benchmark were selected due to previous work that showed that these four instances filter out very different percentages of stream values [23].…”

Section: Analyzing Test Casesmentioning

confidence: 99%

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

Pisani¹,

Rosário²,

Borin³

2019

Future Internet

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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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SpecK: Composition of Stream Processing Applications over Fog Environments

Battulga

Miorandi

Tedeschi

2021

Distributed Applications and Interoperable Systems

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Stream Processing (SP), i.e., the processing of data in motion, as soon as it becomes available, is a hot topic in cloud computing. Various SP stacks exist today, with applications ranging from IoT analytics to processing of payment transactions. The backbone of said stacks are Stream Processing Engines (SPEs), software packages offering a high-level programming model and scalable execution of data stream processing pipelines. SPEs have been traditionally developed to work inside a single datacenter, and optimised for speed. With the advent of Fog computing, however, the processing of data streams needs to be carried out over multiple geographically distributed computing sites: Data gets typically pre-processed close to where they are generated, then aggregated at intermediate nodes, and finally globally and persistently stored in the Cloud. SPEs were not designed to address these new scenarios. In this paper, we argue that large scale Fog-based stream processing should rely on the coordinated composition of geographically dispersed SPE instances. We propose an architecture based on the composition of multiple SPE instances and their communication via distributed message brokers. We introduce SpecK, a tool to automate the deployment and adaptation of pipelines over a Fog computing platform. Given a description of the pipeline, SpecKcovers all the operations needed to deploy a stream processing computation over the different SPE instances targeted, using their own APIs and establishing the required communication channels to forward data among them. A prototypical implementation of SpecK is presented, and its performance is evaluated over Grid'5000, a large-scale, distributed experimental facility.

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Beyond the Fog: Bringing Cross-Platform Code Execution to Constrained IoT Devices

Cited by 13 publications

References 9 publications

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

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

SpecK: Composition of Stream Processing Applications over Fog Environments

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