Computation Offloading for Machine Learning Web Apps in the Edge Server Environment

Jeong, Hyuk-Jin; Jeong, InChang; Lee, Hyeon-Jae; Moon, Soo-Mook

doi:10.1109/icdcs.2018.00154

Cited by 68 publications

(42 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, new functionality was added to other FAs. Inference was included to the 'Interfacing and visualization'-FA based on a prototype, where a pre-trained model was used for inference in a web browser [27]. Model packaging comprises adding of model(s) into executable or loadable file(s) (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…When the location of architectural elements is analysed in detail, it can be seen that Data transformation and Serving functionality was executed in the edge server (transformation and serving of end users with augmented video streaming content [32]). Models were trained mainly in private/public clouds [36,37,39] or in edge devices [35,46] while the results were inferred from models in all environments [27,28,30,31,45]. Also, resource demanding Deep analytics and Machine learning, and Job scheduling tasks were executed mostly in private/public clouds, and/or edge environments.…”

Section: Discussionmentioning

confidence: 99%

“…Distribution of models was scheduled within the private cloud [31] or end user device [28]. Subsequently, models were distributed either from edge [27], from private cloud [31], or from end user devices [28]. Finally, models were loaded to memory in mobile devices [27] or in edge servers [28,32].…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, models were distributed either from edge [27], from private cloud [31], or from end user devices [28]. Finally, models were loaded to memory in mobile devices [27] or in edge servers [28,32]. Figure 5 provides a vision how development and deployment of models may affect AIOps [25] and application SW development processes at the business layer.…”

Section: Discussionmentioning

confidence: 99%

“…One alternative is to offload model computation/inference [27][28][29] to the edge/cloud or partition neural network (NN) computation [29][30][31] to the edge/cloud. Offloading of NN inference to the edge achieved best performance in the context of image recognition in web browsers [27]. Also, when inferring the first NN layer (of the model) at the client, and offloading the rest of the NN-layers to the edge server achieved best performance (in terms of latency) in partial inference due to the large size of the first NN layer.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen

Pakkala

2020

J Big Data

View full text Add to dashboard Cite

IntroductionMany big data systems have been developed and realised to provide end user services (Netflix, Facebook, Twitter, LinkedIn etc.). Also, underlying architectures and technologies of the enabling systems have been published [1-3], and RAs have been designed and proposed [4][5][6]. Edge/5G computing is an emerging technological field [7], and the first products are being shipped to the markets. However, the utilisation of machine learning (ML) as part of the edge computing infrastructure is still an area for further research [8]. Particularly, it should be understood, how data is collected, and how models are Abstract Background: Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging. Findings:The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment. Conclusions:The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen

Pakkala

2020

J Big Data

View full text Add to dashboard Cite

show abstract

Coalition theory based task scheduling algorithm using DLFC‐NN model

Mishra,

Mohapatra,

Sahu

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryResource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large‐scale requests, co‐located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co‐located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel “CTA using DLFC‐NN model” is proposed. This proposed approach combines the coalition theory and DLFC‐NN techniques by including IRT‐OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC‐NN model is used to reduce resource consumption, span time, and be highly accurate and energy‐efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make‐span time for task scheduling. However, the DLFC‐NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques.

show abstract

An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks

Qin

Zhou

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

As the rapid evolution of smart devices and real‐time applications, many new kinds of computation‐intensive services have been emerged successively and the corresponding requirements have been growing dramatically. Extended from cloud computing, mobile edge computing (MEC) is a novel technology which can provide powerful computing resource at the proximity of resource‐restrained mobile devices. Thus, it enables collaboration between edge server and mobile device, which can improve the quality of experience for users. In this article, we propose an intelligent collaborative inference (ICI) approach for real‐time computation‐intensive services in MEC network, which can achieve intelligent service partitioning and partial task offloading. Since machine learning algorithms have been applied in many applications with the advancement of big data and computing power, we focus on the services based on deep‐learning. Particularly, we research a service based on Pose‐Net model to achieve human pose estimation in the field of computer vision. And we design relevant ICI algorithm to achieve fine‐grained video stream processing in consideration of video service requirement, deep neural network (DNN) model structure, mobile device capability, wireless network condition, and cooperative server workload. Based on Python programming language and TensorFlow library, we test the ICI approach with some practical simulation parameters on real hardware platforms. The experiment results show that the presented ICI approach have superior performance in terms of service frame rate and client energy consumption than other benchmark approaches.

show abstract

Computation Offloading for Machine Learning Web Apps in the Edge Server Environment

Cited by 68 publications

References 18 publications

Extending reference architecture of big data systems towards machine learning in edge computing environments

Extending reference architecture of big data systems towards machine learning in edge computing environments

Coalition theory based task scheduling algorithm using DLFC‐NN model

An intelligent collaborative inference approach of service partitioning and task offloading for deep learning based service in mobile edge computing networks

Contact Info

Product

Resources

About