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Performance Evaluation in the Era of Cloud and Edge Computing

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 12067

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Special Issue Editors

Prof. Dr. Maria Carla Calzarossa

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Guest Editor

Director of the Performance Evaluation lab at Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
Interests: performance evaluation; workload characterization; cloud computing; social networks
Special Issues, Collections and Topics in MDPI journals

Dr. Marco L. Della Vedova

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Guest Editor

Dipartimento di Matematica e Fisica, Università Cattolica del Sacro Cuore – Brescia, Brescia, Italy
Interests: scheduling; machine learning; stochastic modeling; workload forecasting; cloud computing

Prof. Dr. Luisa Massari

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Guest Editor

Dipartimento di Ingegneria Industriale & Informazione, Università di Pavia, Pavia, Italy
Interests: performance evaluation; workload characterization; benchmarking; social networks; computer networks

Prof. Dr. Daniele Tessera

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Guest Editor

Dipartimento di Matematica e Fisica, Università Cattolica del Sacro Cuore – Brescia, Brescia, Italy
Interests: performance evaluation; workload characterization; benchmarking; cloud computing

Special Issue Information

Dear Colleagues,

Cloud and edge computing technologies are being used at present to deploy a large variety of sophisticated services and applications characterized by different service level requirements. To satisfy these requirements as well as user expectations, it is necessary to measure, assess, and predict the performance of these complex technological infrastructures and their workloads. The peculiarities of these technologies and variability of the workload intensity and characteristics open new performance challenges, for example, dealing with dynamic resource provisioning under uncertainty, workload forecasting, load distribution, and data-flow scheduling. Methodologies, techniques, and tools for performance evaluation have to cope with these challenges.

This Special Issue is soliciting contributions presenting state-of-the art, original solutions or case studies in the field of performance evaluation of cloud and edge computing infrastructures and services. Topics of interest include but are not limited to:

Monitoring;
Benchmarking;
Workload characterization;
Modeling;
Simulation.

Prof. Dr. Maria Carla Calzarossa
Dr. Marco L. Della Vedova
Prof. Dr. Luisa Massari
Prof. Dr. Daniele Tessera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Cloud computing
Edge computing
Application and service QoS
Performance modeling
Performance prediction
Workload forecasting
Analytic models
Simulation tools
High Performance Computing

Published Papers (3 papers)

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Research

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26 pages, 1830 KiB

Open AccessArticle

Multi-formalism Models for Performance Engineering

by Enrico Barbierato, Marco Gribaudo and Giuseppe Serazzi

Future Internet 2020, 12(3), 50; https://doi.org/10.3390/fi12030050 - 13 Mar 2020

Viewed by 2909

Abstract

Nowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given system and studying the performance indices values generated by the model’s simulation. This process requires considering a set of paradigms, carefully balancing the benefits and the disadvantages of each one. While queuing networks are particularly suited to modeling cloud and edge computing architectures, particular occurrences—such as autoscaling—require different techniques to be analyzed. This work presents a review of paradigms designed to model specific events in different scenarios, such as timeout with quorum-based join, approximate computing with finite capacity region, MapReduce with class switch, dynamic provisioning in hybrid clouds, and batching of requests in e-Health applications. The case studies are investigated by implementing models based on the above-mentioned paradigms and analyzed with discrete event simulation techniques. Full article

(This article belongs to the Special Issue Performance Evaluation in the Era of Cloud and Edge Computing)

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12 pages, 575 KiB

Open AccessArticle

A Methodology Based on Computational Patterns for Offloading of Big Data Applications on Cloud-Edge Platforms

by Beniamino Di Martino, Salvatore Venticinque, Antonio Esposito and Salvatore D’Angelo

Future Internet 2020, 12(2), 28; https://doi.org/10.3390/fi12020028 - 7 Feb 2020

Cited by 9 | Viewed by 3338

Abstract

Internet of Things (IoT) is becoming a widespread reality, as interconnected smart devices and sensors have overtaken the IT market and invaded every aspect of the human life. This kind of development, while already foreseen by IT experts, implies additional stress to already congested networks, and may require further investments in computational power when considering centralized and Cloud based solutions. That is why a common trend is to rely on local resources, provided by smart devices themselves or by aggregators, to deal with part of the required computations: this is the base concept behind Fog Computing, which is becoming increasingly adopted as a distributed calculation solution. In this paper a methodology, initially developed within the TOREADOR European project for the distribution of Big Data computations over Cloud platforms, will be described and applied to an algorithm for the prediction of energy consumption on the basis of data coming from home sensors, already employed within the CoSSMic European Project. The objective is to demonstrate that, by applying such a methodology, it is possible to improve the calculation performances and reduce communication with centralized resources. Full article

(This article belongs to the Special Issue Performance Evaluation in the Era of Cloud and Edge Computing)

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Review

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12 pages, 1186 KiB

Open AccessReview

Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics

by Majid Ashouri, Fabian Lorig, Paul Davidsson and Romina Spalazzese

Future Internet 2019, 11(11), 235; https://doi.org/10.3390/fi11110235 - 8 Nov 2019

Cited by 27 | Viewed by 5290

Abstract

The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects. Full article

(This article belongs to the Special Issue Performance Evaluation in the Era of Cloud and Edge Computing)

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