FMonE: A Flexible Monitoring Solution at the Edge
Monitoring has always been a key element on ensuring the performance of complex distributed systems, being a first step to control quality of service, detect anomalies, or make decisions about resource allocation and job scheduling, to name a few. Edge computing is a new type of distributed computin...
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Online Access: | http://dx.doi.org/10.1155/2018/2068278 |
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doaj-2e6f71f5c9fb445c8f2a4276bb52b9c42020-11-25T01:12:20ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/20682782068278FMonE: A Flexible Monitoring Solution at the EdgeÁlvaro Brandón0María S. Pérez1Jesus Montes2Alberto Sanchez3Universidad Politecnica de Madrid, Madrid, SpainUniversidad Politecnica de Madrid, Madrid, SpainUniversidad Politecnica de Madrid, Madrid, SpainUniversidad Rey Juan Carlos Madrid, SpainMonitoring has always been a key element on ensuring the performance of complex distributed systems, being a first step to control quality of service, detect anomalies, or make decisions about resource allocation and job scheduling, to name a few. Edge computing is a new type of distributed computing, where data processing is performed by a large number of heterogeneous devices close to the place where the data is generated. Some of the differences between this approach and more traditional architectures, like cloud or high performance computing, are that these devices have low computing power, have unstable connectivity, and are geo-distributed or even mobile. All of these aforementioned characteristics establish new requirements for monitoring tools, such as customized monitoring workflows or choosing different back-ends for the metrics, depending on the device hosting them. In this paper, we present a study of the requirements that an edge monitoring tool should meet, based on motivating scenarios drawn from literature. Additionally, we implement these requirements in a monitoring tool named FMonE. This framework allows deploying monitoring workflows that conform to the specific demands of edge computing systems. We evaluate FMonE by simulating a fog environment in the Grid’5000 testbed and we demonstrate that it fulfills the requirements we previously enumerated.http://dx.doi.org/10.1155/2018/2068278 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Álvaro Brandón María S. Pérez Jesus Montes Alberto Sanchez |
spellingShingle |
Álvaro Brandón María S. Pérez Jesus Montes Alberto Sanchez FMonE: A Flexible Monitoring Solution at the Edge Wireless Communications and Mobile Computing |
author_facet |
Álvaro Brandón María S. Pérez Jesus Montes Alberto Sanchez |
author_sort |
Álvaro Brandón |
title |
FMonE: A Flexible Monitoring Solution at the Edge |
title_short |
FMonE: A Flexible Monitoring Solution at the Edge |
title_full |
FMonE: A Flexible Monitoring Solution at the Edge |
title_fullStr |
FMonE: A Flexible Monitoring Solution at the Edge |
title_full_unstemmed |
FMonE: A Flexible Monitoring Solution at the Edge |
title_sort |
fmone: a flexible monitoring solution at the edge |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8669 1530-8677 |
publishDate |
2018-01-01 |
description |
Monitoring has always been a key element on ensuring the performance of complex distributed systems, being a first step to control quality of service, detect anomalies, or make decisions about resource allocation and job scheduling, to name a few. Edge computing is a new type of distributed computing, where data processing is performed by a large number of heterogeneous devices close to the place where the data is generated. Some of the differences between this approach and more traditional architectures, like cloud or high performance computing, are that these devices have low computing power, have unstable connectivity, and are geo-distributed or even mobile. All of these aforementioned characteristics establish new requirements for monitoring tools, such as customized monitoring workflows or choosing different back-ends for the metrics, depending on the device hosting them. In this paper, we present a study of the requirements that an edge monitoring tool should meet, based on motivating scenarios drawn from literature. Additionally, we implement these requirements in a monitoring tool named FMonE. This framework allows deploying monitoring workflows that conform to the specific demands of edge computing systems. We evaluate FMonE by simulating a fog environment in the Grid’5000 testbed and we demonstrate that it fulfills the requirements we previously enumerated. |
url |
http://dx.doi.org/10.1155/2018/2068278 |
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