PiCasso: Enabling information-centric multi-tenancy at the edge of community mesh networks

Selimi, Mennan; Lertsinsrubtavee, Adisorn; Sathiaseelan, Arjuna; Cerdà-Alabern, Llorenç; Navarro, Leandro

doi:10.1016/j.comnet.2019.106897

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…However, the code uses only a single processor. Raspberry Pi is a singleboard computer widely used in EC projects, as can be seen in [6], [17]- [19]. The PowerAPI was configured to capture the mean power of the intervals, using the procfs module [49] with a sampling period of 1 ms.…”

Section: Evaluation Setupmentioning

confidence: 99%

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Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Lopes

Santana

Costa

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Edge computing (EC) is a promising technology capable of bridging the gap between Cloud computing services and the demands of emerging technologies such as the Internet of Things (IoT). Most EC-based solutions, from wearable devices to smart cities architectures, benefit from Machine Learning (ML) methods to perform various tasks, such as classification. In these cases, ML solutions need to deal efficiently with a huge amount of data, while balancing predictive performance, memory and time costs, and energy consumption. The fact that these data usually come in the form of a continuous and evolving data stream makes the scenario even more challenging. Many algorithms have been proposed to cope with data stream classification, e.g., Very Fast Decision Tree (VFDT) and Strict VFDT (SVFDT). Recently, Online Local Boosting (OLBoost) has also been introduced to improve predictive performance without modifying the underlying structure of the decision tree produced by these algorithms. In this work, we compared the four-way relationship among time efficiency, energy consumption, predictive performance, and memory costs, tuning the hyperparameters of VFDT and the two versions of SVFDT with and without OLBoost. Experiments over 6 benchmark datasets using an EC device revealed that VFDT and SVFDT-I were the most energy-friendly algorithms, with SVFDT-I also significantly reducing memory consumption. OLBoost, as expected, improved the predictive performance, but caused a deterioration in memory and energy consumption.

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Section: Evaluation Setupmentioning

confidence: 99%

“…OLBoost provides a way to increase predictive performance without seriously compromising energy, memory, and time costs, as ensemble solutions would. The tests were performed over six binary benchmark datasets in a Raspberry Pi, which has been frequently pointed as a platform for EC devices [6], [17]- [19].…”

mentioning

confidence: 99%

Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Lopes

Santana

Costa

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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“…However, the evaluation of communication protocol for delivering the service has not been discussed yet. In contrast, this paper presents a lightweight architecture of PiGeon [6] and evaluates the performance of service delivery with BASP service placement heuristic and NDN solution.…”

Section: V R E L At E D W O R Kmentioning

confidence: 99%

“…Each tier has its own Docker image. The web server runs the Elgg6 , a social networking engine and it connects to the memcached server and the database server. The clients (implemented using the Faban workload generator) send requests to login into the social network and perform different operations (e.g., login to Facebook, writing a post, updating a post, sending a message, etc).…”

mentioning

confidence: 99%

Towards Information-Centric Edge Platform for Mesh Networks: The Case of CityLab Testbed

Selimi

Navarro

Braem

et al. 2020

2020 IEEE International Conference on Fog Computing (ICFC)

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“…If available, GPUs instead of CPUs are used for better performance. On low-capacity devices such as singleboard-computers (SBCs), training takes a much longer time than on higher end devices [4] [5]. In addition, often all cores of the CPU are consumed during the training process.…”

Section: Introductionmentioning

confidence: 99%

BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices

Ibraimi

Selimi

Freitag

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Inference with trained machine learning models is now possible with small computing devices while only a few years ago it was run mostly in the cloud only. The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines. But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device. Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption. In this paper, we present an approach which uses a dynamic epoch parameter in the model training. We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning. We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy. Thus, BePOCH makes machine learning model training on lowcapacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important aspect towards greener machine learning techniques.

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PiCasso: Enabling information-centric multi-tenancy at the edge of community mesh networks

Cited by 9 publications

References 36 publications

Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Towards Information-Centric Edge Platform for Mesh Networks: The Case of CityLab Testbed

BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices

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