2021 IEEE International Conference on Edge Computing (EDGE)最新文献

Data Sharing-Aware Task Allocation in Edge Computing Systems 边缘计算系统中数据共享感知的任务分配

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00018

Sanaz Rabinia, Haydar Mehryar, Marco Brocanelli, Daniel Grosu

{"title":"Data Sharing-Aware Task Allocation in Edge Computing Systems","authors":"Sanaz Rabinia, Haydar Mehryar, Marco Brocanelli, Daniel Grosu","doi":"10.1109/EDGE53862.2021.00018","DOIUrl":"https://doi.org/10.1109/EDGE53862.2021.00018","url":null,"abstract":"Edge computing allows end-user devices to offload heavy computation to nearby edge servers for reduced latency, maximized profit, and/or minimized energy consumption. Data-dependent tasks that analyze locally-acquired sensing data are one of the most common candidates for task offloading in edge computing. As a result, the total latency and network load are affected by the total amount of data transferred from end-user devices to the selected edge servers. Most existing solutions for task allocation in edge computing do not take into consideration that some user tasks may actually operate on the same data items. Making the task allocation algorithm aware of the existing data sharing characteristics of tasks can help reduce network load at a negligible profit loss by allocating more tasks sharing data on the same server. In this paper, we formulate the data sharing-aware task allocation problem that make decisions on task allocation for maximized profit and minimized network load by taking into account the data-sharing characteristics of tasks. In addition, because the problem is NP-hard, we design the DSTA algorithm, which finds a solution to the problem in polynomial time. We analyze the performance of the proposed algorithm against a state-of-the-art baseline that only maximizes profit. Our extensive analysis shows that DSTA leads to about 8 times lower data load on the network while being within 1.03 times of the total profit on average compared to the state-of-the-art.","PeriodicalId":115969,"journal":{"name":"2021 IEEE International Conference on Edge Computing (EDGE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122190964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A roadmap on learning and reasoning for distributed computing continuum ecosystems 分布式计算连续体生态系统的学习和推理路线图

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00021

Andrea Morichetta

{"title":"A roadmap on learning and reasoning for distributed computing continuum ecosystems","authors":"Andrea Morichetta","doi":"10.1109/EDGE53862.2021.00021","DOIUrl":"https://doi.org/10.1109/EDGE53862.2021.00021","url":null,"abstract":"A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination. Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of Edge- Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. In this work, we focus on the approaches that center on high-level, general strategies, like the Free Energy Principle and Global Neuronal Workspace theories. The goal of exploring these techniques is to introduce principles that can potentially help us build distributed systems able to jointly work on the whole computing continuum, from the Edge to the Cloud, with self-adapting capabilities, i.e., dealing with uncertainty and the need for generalization, which is currently an open issue.","PeriodicalId":115969,"journal":{"name":"2021 IEEE International Conference on Edge Computing (EDGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

EDGE 2021 Program Committee EDGE 2021项目委员会

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/edge53862.2021.00009

引用次数: 0

EDGE 2021 Organizing Committee EDGE 2021组委会

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/edge53862.2021.00008

引用次数: 0

A Framework for Analyzing Resource Allocation Policies for Multi-Access Edge Computing 一种多访问边缘计算资源分配策略分析框架

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00023

Kaustabha Ray, A. Banerjee

引用次数: 4

A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua 一种基于随机贪婪的人工智能应用设计时间工具，用于计算连续体中的组件放置和资源选择

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00014

Hamta Sedghani, Federica Filippini, D. Ardagna

{"title":"A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua","authors":"Hamta Sedghani, Federica Filippini, D. Ardagna","doi":"10.1109/EDGE53862.2021.00014","DOIUrl":"https://doi.org/10.1109/EDGE53862.2021.00014","url":null,"abstract":"Artificial Intelligence (AI) and Deep Learning (DL) are pervasive today, with applications spanning from personal assistants to healthcare. Nowadays, the accelerated migration towards mobile computing and Internet of Things, where a huge amount of data is generated by widespread end devices, is determining the rise of the edge computing paradigm, where computing resources are distributed among devices with highly heterogeneous capacities. In this fragmented scenario, efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources. In this paper, we propose a tool to effectively address the component placement problem for AI applications at design time. Through a randomized greedy algorithm, our approach identifies the placement of minimum cost providing performance guar-antees across heterogeneous resources including edge devices, cloud GPU-based Virtual Machines and Function as a Service solutions. Finally, we compare the random greedy method with the HyperOpt framework and demonstrate that our proposed approach converges to a near-optimal solution much faster, especially in large scale systems.","PeriodicalId":115969,"journal":{"name":"2021 IEEE International Conference on Edge Computing (EDGE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132238510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Distributed Online Resource Scheduling for Mobile Edge Servers 面向移动边缘服务器的分布式在线资源调度

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00013

Ziwen Zhou, Tianming Zhao, Wei Li, Albert Y. Zomaya

{"title":"Distributed Online Resource Scheduling for Mobile Edge Servers","authors":"Ziwen Zhou, Tianming Zhao, Wei Li, Albert Y. Zomaya","doi":"10.1109/EDGE53862.2021.00013","DOIUrl":"https://doi.org/10.1109/EDGE53862.2021.00013","url":null,"abstract":"Due to the flexibility and scalability, the increasing number of mobile edge computing applications involves Mobile Edge Servers (MES). MES introduces the new challenge of online resource scheduling to serve different requests under limited energy to offer similar functionalities of the immobile edge servers. The previous studies consider the case of the identical server, which has poor scalability and is hard to apply to real-world applications. This work proposes a novel model named the distributed k-server problem that formulates the MES resource scheduling to address the heterogeneity issues in both servers and requests. We design an algorithm named DWFA based on the efficient network flow-based Work Function Algorithm (WFA) to the classic k-server problem as an immediate solution to the proposed problem. DWFA inher-its the competitiveness of WFA but has high computational complexity. To further increase scalability via the computing power of MES, we parallelise DWFA to design a distributed algorithm named FD-WFA as a distributed execution of DWFA, which significantly reduces the computational complexity and increases the practicality. Extensive simulations have been con-ducted to verify the theoretical results and show the advantages of FD-WFA over the benchmarks.","PeriodicalId":115969,"journal":{"name":"2021 IEEE International Conference on Edge Computing (EDGE)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards an Assurance Framework for Edge and IoT Systems 面向边缘和物联网系统的保证框架

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00015

M. Anisetti, C. Ardagna, Nicola Bena, Ruslan Bondaruc

引用次数: 3

Message from Congress General Chairs of IEEE SERVICES 2021 IEEE服务2021大会主席致辞

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/edge53862.2021.00006

引用次数: 0

Towards Sustainable Satellite Edge Computing 走向可持续的卫星边缘计算

2021 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2021-09-01 DOI: 10.1109/EDGE53862.2021.00010

Qing Li, Shangguang Wang, Xiao Ma, Ao Zhou, Fangchun Yang

引用次数: 4