2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)最新文献_第2页

A Multi-Weight Strategy for Container Consolidation 集装箱拼箱的多重量策略

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC) Pub Date : 2020-05-01 DOI: 10.1109/ICFEC50348.2020.00009

Najet Hamdi, Walid Chainbi

{"title":"A Multi-Weight Strategy for Container Consolidation","authors":"Najet Hamdi, Walid Chainbi","doi":"10.1109/ICFEC50348.2020.00009","DOIUrl":"https://doi.org/10.1109/ICFEC50348.2020.00009","url":null,"abstract":"Finding energy efficient management strategies while satisfying the Quality of Service (QoS) requirements is one of the critical challenges in Clouds. Virtual Machine consolidation has proved its efficiency in dealing with the energy issue in Cloud environments. Recently, containers are increasingly gaining popularity with regards to virtual machines (VMs) and going to be major deployment model especially at the edge of the network. Hence, it becomes critical to address energy efficiency from container’s point of view. Over the past few years, there have been several attempts for container consolidation heuristics. Unfortunately, most of the proposed strategies took container consolidation from VM’s point of view. In fact, they considered only one single deployment model in which containers are deployed on virtual machines. On the basis of such assumption, container consolidation is achieved through VMs consolidation. The fact stating that containers can be deployed as well in bare metal requires to take container consolidation from a different point of view. This paper focuses on container consolidation issue with regards to the bare metal deployment model and suggests a new consolidation strategy. The performance of our strategy is evaluated using the Docker Platform and is compared against two other consolidation strategies.","PeriodicalId":277214,"journal":{"name":"2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125045452","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

ICFEC 2020 TOC

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC) Pub Date : 2020-05-01 DOI: 10.1109/icfec50348.2020.00004

引用次数: 0

Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge Deep-Edge:异构边缘上深度学习模型更新的有效框架

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC) Pub Date : 2020-04-13 DOI: 10.1109/ICFEC50348.2020.00016

Anirban Bhattacharjee, A. Chhokra, Hongyang Sun, Shashank Shekhar, A. Gokhale, G. Karsai, A. Dubey

{"title":"Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge","authors":"Anirban Bhattacharjee, A. Chhokra, Hongyang Sun, Shashank Shekhar, A. Gokhale, G. Karsai, A. Dubey","doi":"10.1109/ICFEC50348.2020.00016","DOIUrl":"https://doi.org/10.1109/ICFEC50348.2020.00016","url":null,"abstract":"Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although cloud data-centers can meet the computational requirements of the resource-intensive and time-consuming model update task, transferring data from the edge devices to the cloud incurs a significant cost in terms of network bandwidth and are prone to data privacy issues. With the advent of GPU-enabled edge devices, the DL model update can be performed at the edge in a distributed manner using multiple connected edge devices. However, efficiently utilizing the edge resources for the model update is a hard problem due to the heterogeneity among the edge devices and the resource interference caused by the colocation of the DL model update task with latency-critical tasks running in the background. To overcome these challenges, we present Deep-Edge, a load- and interference-aware, fault-tolerant resource management framework for performing model update at the edge that uses distributed training. This paper makes the following contributions. First, it provides a unified framework for monitoring, profiling, and deploying the DL model update tasks on heterogeneous edge devices. Second, it presents a scheduler that reduces the total re-training time by appropriately selecting the edge devices and distributing data among them such that no latency-critical applications experience deadline violations. Finally, we present empirical results to validate the efficacy of the framework using a real-world DL model update case-study based on the Caltech dataset and an edge AI cluster testbed.","PeriodicalId":277214,"journal":{"name":"2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116316754","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: 3

Modelling Fog Offloading Performance 建模雾卸载性能

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC) Pub Date : 2020-02-12 DOI: 10.1109/ICFEC50348.2020.00011

A. Majeed, P. Kilpatrick, I. Spence, B. Varghese

{"title":"Modelling Fog Offloading Performance","authors":"A. Majeed, P. Kilpatrick, I. Spence, B. Varghese","doi":"10.1109/ICFEC50348.2020.00011","DOIUrl":"https://doi.org/10.1109/ICFEC50348.2020.00011","url":null,"abstract":"Fog computing has emerged as a computing paradigm aimed at addressing the issues of latency, bandwidth and privacy when mobile devices are communicating with remote cloud services. The concept is to offload compute services closer to the data. However many challenges exist in the realisation of this approach. During offloading, (part of) the application underpinned by the services may be unavailable, which the user will experience as down time. This paper describes work aimed at building models to allow prediction of such down time based on metrics (operational data) of the underlying and surrounding infrastructure. Such prediction would be invaluable in the context of automated Fog offloading and adaptive decision making in Fog orchestration. Models that cater for four container-based stateless and stateful offload techniques, namely Save and Load, Export and Import, Push and Pull and Live Migration, are built using four (linear and non-linear) regression techniques. Experimental results comprising over 42 million data points from multiple lab-based Fog infrastructure are presented. The results highlight that reasonably accurate predictions (measured by the coefficient of determination for regression models, mean absolute percentage error, and mean absolute error) may be obtained when considering 25 metrics relevant to the infrastructure.","PeriodicalId":277214,"journal":{"name":"2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132380812","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

Priority-based Fair Scheduling in Edge Computing 边缘计算中基于优先级的公平调度

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC) Pub Date : 2020-01-24 DOI: 10.1109/ICFEC50348.2020.00012

A. Madej, Nan Wang, N. Athanasopoulos, R. Ranjan, B. Varghese

{"title":"Priority-based Fair Scheduling in Edge Computing","authors":"A. Madej, Nan Wang, N. Athanasopoulos, R. Ranjan, B. Varghese","doi":"10.1109/ICFEC50348.2020.00012","DOIUrl":"https://doi.org/10.1109/ICFEC50348.2020.00012","url":null,"abstract":"Scheduling is important in Edge computing. In contrast to the Cloud, Edge resources are hardware limited and cannot support workload-driven infrastructure scaling. Hence, resource allocation and scheduling for the Edge requires a fresh perspective. Existing Edge scheduling research assumes availability of all needed resources whenever a job request is made. This paper challenges that assumption, since not all job requests from a Cloud server can be scheduled on an Edge node. Thus, guaranteeing fairness among the clients (Cloud servers offloading jobs) while accounting for priorities of the jobs becomes a critical task. This paper presents four scheduling techniques, the first is a naive first come first serve strategy and further proposes three strategies, namely a client fair, priority fair, and hybrid that accounts for the fairness of both clients and job priorities. An evaluation on a target platform under three different scenarios, namely equal, random, and Gaussian job distributions is presented. The experimental studies highlight the low overheads and the distribution of scheduled jobs on the Edge node when compared to the naive strategy. The results confirm the superior performance of the hybrid strategy and showcase the feasibility of fair schedulers for Edge computing.","PeriodicalId":277214,"journal":{"name":"2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120939012","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: 11