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Edge Computing: Vision and Challenges

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Abstract

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of Edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative Edge to materialize the concept of Edge computing. Finally, we present several challenges and opportunities in the field of Edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

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... Consequently, they are likely to have a hard time with processing of large or complex data sets in the process of: machine learning; big data analytics; etc. This edge computing can undermine the efficiency edge computing provides by requiring edge devices to offload tasks to cloud or near systems in many cases [22]. One other issue is the problem of security and privacy. ...
... Poor or inconsistent connectivity can slow down performance, they might create synchronization issues, or in some worst cases, it can also cause system failures if essential data isn't transferred in time. Therefore, as edge computing has huge benefits, overcoming these resource, security, and connectivity limitation becomes fundamental in exploiting all it has to offer [22][23]. ...
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... requirements while complementing cloud computing (Cui et al., 2023;Shi et al., 2016). These challenges have inspired the idea of maximizing network resources at the edge in a dynamic and distributed manner. ...
... The explosive growth in IoT applications for real-time data processing, storage, and location-dependent missions has necessitated innovations that target addressing the challenges of latency, energy consumption, and limited bandwidth in cloud computing with edge computing technology (Rodrigues et al., 2017;Shi et al., 2016;Xu et al., 2023). Computation offloading on edge computing architecture for real-time and contextdependent execution for multi-domain operations that guarantees minimum latency is an NP-hard problem . ...
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In the Internet of Things (IoT) networks, sensors, gateways, and services interoperate at different levels to provide services to the end users. IoT networks are deployed in different domains for specific tasks that can be monitored from remote locations. The increase in the number of IoT-connected devices and their notable limited computational power calls for resource-efficient and in-between layers of task processing on the network. In this study, we utilized deep reinforcement learning to intelligently model the offloading policies as Markov Decision Process (MDP) for IoT devices in a distributed manner by considering IoT devices as agents that make offloading decisions taking into account the environmental dynamics. To attain optimal policy in the learning process that caters to high dimensionality, deep Q-network was employed to model the agents’ interaction in a dynamic and environment-sensitive manner. The architecture allows local decision-making by IoT edge nodes for tasks offloading to edge servers based on connectivity, resource availability, and proximity. Extensive simulation under different learning rates, batch sizes, and memory sizes shows that the proposed scheme with the utilization of a CNN approximator generates optimal policy and higher convergence performance with lower latency than the conventional Q-learning model and several other existing algorithms.
... This local data processing can offer faster response times and more efficient ROM assessment and ensures sensitive information related to joint movements. And ROM is managed at the edge and can lead to better user privacy protection [11]. ...
... Localized data processing: Edge computing does not rely on a constant internet connection, allowing raw data to be securely stored and processed locally. This ensures sensitive information related to joint movements and ROM is managed at the edge and can lead to better user privacy protection [11]. Moreover, local data processing significantly reduces latency, resulting in faster response times and more efficient ROM assessment. ...
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... Distributed edge computing systems face challenges [18] such as resource fragmentation, lack of centralized control, and issues with latency and data consistency, especially in environments with numerous edge nodes. Hierarchical edge computing [21] addresses these issues by organizing resources across layered levels, from local edge devices to centralized cloud systems. ...
Preprint
The increasing demand for Intelligent Transportation Systems (ITS) has introduced significant challenges in managing the complex, computation-intensive tasks generated by modern vehicles while offloading tasks to external computing infrastructures such as edge computing (EC), nearby vehicular , and UAVs has become influential solution to these challenges. However, traditional computational offloading strategies often struggle to adapt to the dynamic and heterogeneous nature of vehicular environments. In this study, we explored the potential of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) frameworks to optimize computational offloading through adaptive, real-time decision-making, and we have thoroughly investigated the Markov Decision Process (MDP) approaches on the existing literature. The paper focuses on key aspects such as standardized learning models, optimized reward structures, and collaborative multi-agent systems, aiming to advance the understanding and application of DRL in vehicular networks. Our findings offer insights into enhancing the efficiency, scalability, and robustness of ITS, setting the stage for future innovations in this rapidly evolving field.
... Within this context, new paradigms that allow for better use of computational resources are emerging. One such approach is edge computing (Shi et al. 2016), which seeks to perform calculations as close as possible to the data source, on the devices themselves or in the end nodes of the network. Although this brings great benefits, including increased security and less bandwidth consumption, it is essential to address the computational performance limitations of IoT devices themselves. ...
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... For example, applications for mobile gaming, autonomous vehicles or smart cities can benefit from the edge computing capabilities. (Gedeon et al., 2019;Gupta, 2022;Shi et al., 2016) Advance distributed systems: Edge computing needs a deep understanding of distributed systems, so it allow engineers to improve their skills in building, managing, and scaling distributed systems. such as expertise in data synchronization, fault tolerance, and managing complex interactions between distributed components (Baktir et al., 2017) Optimizing resource management: Software engineers can enhance their skills to optimize resource management for resource-constrained edge devices, exploring techniques such as model compression, energy-efficient algorithms, and strategies to resource allocation (Li & Huang, 2021;Xu et al., 2020) Improvement and develop new technologies: Edge computing create opportunities for engineers to gain experience and expertise in these emerging domains, often by using new technologies like software-defined networking (SDN), network function virtualization (NFV), and AI/machine learning (Gedeon et al., 2019;Li & Huang, 2021). ...
Chapter
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The edge computing is changing the way applications are designed and developed and deployed. and this changing , provides innovation in various sectors by improving users experience by faster responses , and also enhancing data security . As the edge computing continues growing, software developers face some new challenges and opportunities. They have been encouraged to ensure that they utilize up-to-date tools, framework and best practices in order to provides best software solutions. The purpose of this study is to give an exhaustive analysis of how edge computing affects software engineering by highlighting the opportunities, challenges and emerging patterns faced in this dynamic field.
... Where applicable, edge computing nodes preprocess data near its source, reducing latency and bandwidth requirements for time-critical operations. [7] provides a comprehensive overview of edge computing architectures and their potential applications in various domains, including the Internet of Things and realtime data analytics. ...
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... By 2025, approximately 45% of end-user/IoT data will be processed/analyzed/classified at the net-work's edge [3] [4]. In recent years, edge computing has emerged as a popular paradigm for offering services close to IoT end users without requiring high-speed Internet connectivity, thus decreasing network congestion and serving real-time IoT applications [5]. In edge computing environments, data is processed/analyzed partially in the same device that generated it or in a separate device located at the network's edge. ...
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... When a think about needs the leading parts of both approaches to completely get it a inquire about issue, this way is supportive. For instance, qualitative perceptions can offer assistance with the arranging of a quantitative overview, and conversations can be utilized to memorize more around the comes about of the quantitative study [13]. Mixed methods are especially valuable for inquire about questions that need to discover out both how enormous an issue is and what causes it. ...
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... In FL scenarios with untrusted clients and server, where the server is unnecessary to collect a small amount of public To achieve this, we group all clients into groups of size group n = 3. Based on the idea of edge computing [26], these gradients from 3 clients are locally aggregated into a group gradient, which is then uploaded to the server by one client. However, untrusted clients and server may leverage gradients to expose the training data of other clients. ...
Preprint
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... As a result, it becomes challenging to meet the high-performance requirements of tasks. To further improve the user's quality of experience (QoE), mobile edge computing (MEC) [5] has emerged as a promising computing paradigm, which integrates edge servers into base stations or Wi-Fi access points at the network edge to provide adequate computing resources. A typical MEC system consisting of some user equipment, some edge servers, and a cloud server has been widely studied [6][7][8][9][10]. ...
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... These innovations will include enhanced and enriched processing of Big Data in a shorter duration hence reducing insecurity in predictive models (Shi et al., 2016). With more and more organizations embracing Internet of Things and cloud in their operations, integration between these systems will define the key to enhancing the maintenance functions and asserting leaders in the field. ...
... [53]. By processing data closer to where it is generated, edge computing reduces the need for data transmission to centralized servers, thereby decreasing energy usage and latency [54]. ...
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... Edge computing involves deploying computational resources at geographically distributed edge locations, which allows data to be processed near the source rather than in a centralized data center (Ferreira et al., 2020;Klein et al., 2021). This proximity reduces the time it takes for data to travel across the network, thereby lowering latency (Shi et al., 2016). For instance, services such as AWS Lambda@Edge and Azure IoT Edge provide the capability to execute code and process data at edge locations, resulting in faster response times for end-users (Bertier et al., 2020). ...
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... This is similar to the Industrial Internet of Things [3], the Internet of Medical Things for monitoring vital signs [4], smart e-Health gateways [5], telemedicine platforms [6], the Internet of Agricultural Things for precision farming [7], food supply chain management [8], environmental monitoring [9], or the Internet of Military Things for battlefield awareness [10]. All of them are connected in some sense, from the very edge-where they collect the most accurate data directly at the source, the so-called "Edge" [11]-through the Internet fog [12], which is made up of millions of paths consisting of different communication protocols, referred to in [13], to a central system where computation is performed, mostly a cloud platform [14]. All these sectors together form a global system called the Internet of Everything [15]. ...
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... Traditional Cloud computing struggles to meet the qualityof-service (QoS) demands of IoT due to the scale, mobility, and geographical distribution of devices, as well as the need for low latency and localized processing [28]. The Cloud-Edge Continuum represents an evolution beyond traditional Cloud computing to address challenges arising from the rapid expansion of IoT devices and the vast amounts of data they generate. ...
Preprint
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... Autoencoders can effectively learn the normal patterns within data and identify deviations without requiring a significant number of labeled examples. Data preprocessing techniques also play a critical role in enhancing model performance (Shi et al. 2016). Approaches like data normalization ensure that different data scales do not skew model predictions, while feature engineering can extract meaningful characteristics from raw data, making it easier for models to identify anomalies. ...
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... These patterns lead to the generation of similar intermediate data during the inference process, providing a solid foundation for predictive optimization in subsequent tasks. By leveraging the predictable nature of data correlations, substantial benefits can be achieved, including streamlined computations and efficient pipeline execution [28]. To highlight the importance of data correlation, we focus on both the temporal and spatial localities of intermediate data using the ResNet101 model [29] on the widely-used UCF101 video dataset [30]. ...
Preprint
End-cloud collaboration offers a promising strategy to enhance the Quality of Service (QoS) in DNN inference by offloading portions of the inference workload from end devices to cloud servers. Despite the potential, the complex model architectures and dynamic network conditions will introduce numerous bubbles (\ie, idle waiting time) in pipeline execution, resulting in inefficient resource utilization and degraded QoS. To address these challenges, we introduce a novel framework named COACH, designed for near bubble-free pipeline collaborative inference, thereby achieving low inference latency and high system throughput. Initially, COACH employs an \textit{offline} component that utilizes an efficient recursive divide-and-conquer algorithm to optimize both model partitioning and transmission quantization, aiming to minimize the occurrence of pipeline bubbles. Subsequently, the \textit{online} component in COACH employs an adaptive quantization adjustment and a context-aware caching strategy to further stabilize pipeline execution. Specifically, COACH analyzes the correlation between intermediate data and label semantic centers in the cache, along with its influence on the quantization adjustment, thereby effectively accommodating network fluctuations. Our experiments demonstrate the efficacy of COACH in reducing inference latency and enhancing system throughput. Notably, while maintaining comparable accuracy, COACH achieves up to 1.7x faster inference and 2.1x higher system throughput than baselines.
... This layer would also involve an edge device used for local data processing and edge computing. It would perform basic predictions and filter data before sending it to the cloud, reducing latency and saving network capacity [25]. ...
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Foreword by Peter Friess & Gérald Santuci: It goes without saying that we are very content to publish this Clusterbook and to leave it today to your hands. The Cluster of European Research projects on the Internet of Things – CERP-IoT – comprises around 30 major research initiatives, platforms and networks work-ing in the field of identification technologies such as Radio Frequency Identification and in what could become tomorrow an Internet-connected and inter-connected world of objects. The book in front of you reports to you about the research and innovation issues at stake and demonstrates approaches and examples of possible solutions. If you take a closer look you will realise that the Cluster reflects exactly the ongoing developments towards a future Internet of Things – growing use of Identification technologies, massive deployment of simple and smart devices, increasing connection between objects and systems. Of course, many developments are less directly derived from the core research area but contribute significantly in creating the “big picture” and the paradigm change. We are also conscious to maintain Europe’s strong position in these fields and the result being achieved, but at the same time to understand the challenges ahead as a global endeavour with our international partners. As it regards international co-operation, the cluster is committed to increasing the number of common activities with the existing international partners and to looking for various stakeholders in other countries. However, we are just at the beginning and, following the prognostics which predict 50 to 100 billion devices to be connected by 2020, the true research work starts now. The European Commission is decided to implement its Internet of Things policy for supporting an economic revival and providing better life to its citizens, and it has just selected from the last call for proposals several new Internet of Things research projects as part of the 7th Framework Programme on European Research. We wish you now a pleasant and enjoyable reading and would ask you to stay connected with us for the future. Special thanks are expressed to Harald Sundmaeker and his team who did a remarkable effort in assembling this Clusterbook.
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MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
Conference Paper
While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a systematic comparator of the performance and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance of these measurements while limiting measurement cost. Applying CloudCmp to four cloud providers that together account for most of the cloud customers today, we find that their offered services vary widely in performance and costs, underscoring the need for thoughtful provider selection. From case studies on three representative cloud applications, we show that CloudCmp can guide customers in selecting the best-performing provider for their applications.
Conference Paper
PROFINET is the industrial Ethernet standard devised by PROFIBUS International (PI) for either modular machine and plant engineering or distributed IO. Using a plant-wide multi-vendor engineering for modular machines, commissioning time as well as costs are reduced. With distributed IO IO-controllers (e.g., PLCs) with their associated IO-devices may also be integrated into PROFINET solutions. Communication is a major part of PROFINET. Real-time communication for standard factory automation applications as well as extensions which enables motion control applications is covered in a common real-time protocol. The advantages of modular and multi-vendor engineering and distributed IO can be used even in applications with time-critical data transfer requirements.
OpenFog Architecture Overview. OpenFog Consortium Architecture Working Group
  • Openfog Architecture
  • Overview
Fog computing and its role in the internet of things
  • F Bonomi
  • R Milito
  • J Zhu
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F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things," in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 2012, pp. 13-16.
The hadoop distributed file system
  • K Shvachko
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K. Shvachko, H. Kuang, S. Radia, and R. Chansler, "The hadoop distributed file system," in Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium on. IEEE, 2010, pp. 1-10.
Spark: cluster computing with working sets
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  • I Stoica
M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, "Spark: cluster computing with working sets," in Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, vol. 10, 2010, p. 10.
Towards wearable cognitive assistance
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K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, "Towards wearable cognitive assistance," in Proceedings of the 12th annual international conference on Mobile systems, applications, and services. ACM, 2014, pp. 68-81.
PROFINET-scalable factory communication for all applications," in Factory Communication Systems
  • J Feld
J. Feld, "PROFINET-scalable factory communication for all applications," in Factory Communication Systems, 2004. Proceedings. 2004 IEEE International Workshop on. IEEE, 2004, pp. 33-38.
Characterizing and modeling the impact of wireless signal strength on smartphone battery drain
  • N Ding
  • D Wagner
  • X Chen
  • A Pathak
  • Y C Hu
  • A Rice
N. Ding, D. Wagner, X. Chen, A. Pathak, Y. C. Hu, and A. Rice, "Characterizing and modeling the impact of wireless signal strength on smartphone battery drain," SIGMETRICS Perform. Eval. Rev., vol. 41, no. 1, pp. 29-40, Jun. 2013. [Online]. Available: http://doi.acm.org/10.1145/2494232.2466586