Proceedings Article10.1145/3437378.3444367
Serverless Edge Computing: Vision and Challenges
Mohammad Sadegh Aslanpour,Adel Nadjaran Toosi,Claudio Cicconetti,Bahman Javadi,Peter Sbarski,Davide Taibi,Marcos Dias De Assuncao,Sukhpal Singh Gill,Raj Gaire,Schahram Dustdar +9 more
- 01 Feb 2021
- pp 3444367
208
TL;DR: In this paper, an in-depth analysis promotes a broad vision for bringing Serverless to the Edge Computing and issues major challenges for serverless to be met before entering Edge computing.
read more
Abstract: Born from a need for a pure “pay-per-use” model and highly scalable platform, the “Serverless” paradigm emerged and has the potential to become a dominant way of building cloud applications Although it was originally designed for cloud environments, Serverless is finding its position in the Edge Computing landscape, aiming to bring computational resources closer to the data source That is, Serverless is crossing cloud borders to assess its merits in Edge computing, whose principal partner will be the Internet of Things (IoT) applications This move sounds promising as Serverless brings particular benefits such as eliminating always-on services causing high electricity usage, for instance However, the community is still hesitant to uptake Serverless Edge Computing because of the cloud-driven design of current Serverless platforms, and distinctive characteristics of edge landscape and IoT applications In this paper, we evaluate both sides to shed light on the Serverless new territory Our in-depth analysis promotes a broad vision for bringing Serverless to the Edge Computing It also issues major challenges for Serverless to be met before entering Edge computing
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Resource Scheduling in Edge Computing: A Survey
TL;DR: In this article, the authors present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed, and introduce a unified model before summarizing the current works on resource scheduling from three research issues.
294
DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing
TL;DR: A Deep Meta Reinforcement Learning-based offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions, is proposed, which achieves obvious improvement over the Deep Q-Learning algorithm and has strong portability in making real-time offload decisions even in time-varying IoT environments.
Edge AI: A survey
TL;DR: In this paper , a detailed survey of edge computing and its paradigms including transition to edge AI is presented to explore the background of each variant proposed for implementing edge computing, and the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network.
177
The internet of things security: A survey encompassing unexplored areas and new insights
Abiodun Esther Omolara,Abiodun Esther Omolara,Abdullah Alabdulatif,Oludare Isaac Abiodun,Oludare Isaac Abiodun,Moatsum Alawida,Moatsum Alawida,Abdulatif Alabdulatif,Wafa' Hamdan Alshoura,Humaira Arshad,Humaira Arshad +10 more
TL;DR: In this paper, a systematic literature review of over 200 articles is presented to provide new insights into the security of IoTs, taking cognizant of its social, economic, technical and legal implications, which will be beneficial to researchers, manufacturers, individuals, organizations and governments.
157
Fog computing: A taxonomy, systematic review, current trends and research challenges
TL;DR: This review article aims to classify recently published studies and investigate the current status in the area of fog computing, and proposed taxonomy for fog computing frameworks based on the existing literature and compared the different research work based on taxonomy.
126
References
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
Ashkan Yousefpour,Caleb Fung,Tam T. Nguyen,Krishna P. Kadiyala,Fatemeh Jalali,Amirreza Niakanlahiji,Jian Kong,Jason P. Jue +7 more
TL;DR: In this paper, the authors provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences, and provide a taxonomy of research topics in fog computing.
863
Serverless Computing: Current Trends and Open Problems
Ioana Baldini,Paul Castro,Kerry Shih-Ping Chang,Perry Cheng,Stephen J. Fink,Vatche Ishakian,Nick Mitchell,Vinod Muthusamy,Rodric Rabbah,Aleksander Slominski,Philippe Suter +10 more
- 10 Jun 2017
TL;DR: This chapter surveys existing serverless platforms from industry, academia, and open-source projects, identifies key characteristics and use cases, and describes technical challenges and open problems.
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
TL;DR: In this article, the authors divide Edge Intelligence into two categories: Intelligence-enabled Edge Computing (IEC) and Artificial Intelligence on Edge (AI on Edge) to provide more optimal solutions to key problems in edge computing with the help of popular and effective AI technologies.
644
•Posted Content
Cloud Programming Simplified: A Berkeley View on Serverless Computing
Eric Jonas,Johann Schleier-Smith,Vikram Sreekanti,Chia-Che Tsai,Anurag Khandelwal,Qifan Pu,Vaishaal Shankar,Joao Carreira,Karl Krauth,Neeraja J. Yadwadkar,Joseph E. Gonzalez,Raluca Ada Popa,Ion Stoica,David A. Patterson +13 more
TL;DR: Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, it is predicted these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.
612
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
TL;DR: In this paper, the authors divide edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge), and provide insights into this new interdisciplinary field from a broader perspective.