Distributed Applications and Services for Future Internet

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 2742

Special Issue Editors

Department of Computer Science, Edge Hill University, Ormskirk, UK
Interests: distributed systems; Cloud/Fog/Edge computing; IoT and digital healthcare; ML
Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
Interests: distributed systems and applications; content delivery networks architecture; performance evaluation of networked systems
Department of Mathematics and Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4SB, UK
Interests: computer networks; wireless communications; parallel and distributed computing; ubiquitous computing; multimedia systems; modeling and performance engineering
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Special Issue Information

Dear Colleagues,

Distributed applications and services are predicted to enable future internet technologies and applications. Distributed systems have existed and evolved for many years; with the expansion of the Internet to edge devices, and IoT constantly generating a huge amount of data which require machine learning applications, the distribution of computation is expected to evolve and continue to expand. With Cloud computing entering its maturity stage, providing scalable service and resource provision, and with Fog and Edge computing still under development and able to support closer to edge computational power; distributed applications and services are going to drive the future internet vision of smart cities, digital healthcare and Industry 4.0, to name a few. Enormous efforts have been made to support the generation and analysis of big data for healthcare or industrial applications; machine learning- and AI-driven solutions are set to deliver real time intelligent applications. The use of deep neural networks in computer vision, language and speech processing has started challenging existing techniques, platforms and infrastructure when it comes to providing intelligent solutions. In this Special Issue, we focus on distributed applications and services that enable intelligent applications in the future Internet of Things and beyond. The aim of this Special Issue is to bring together research work focusing on advancing distributed applications and underpinning concepts, tools and frameworks in support of the realization of future intelligent internet applications.

Prof. Dr. Ella Pereira
Dr. Rubem Pereira
Prof. Dr. Geyong Min
Guest Editors

Manuscript Submission Information

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Keywords

  • Distributed application architectures and supporting middleware
  • Distributed intelligence
  • Cloud/fog/edge computing—architectures and services
  • AI as a service
  • Distributed resource provision and management
  • Distributed computation for ML applications
  • IoT data processing in cloud/fog/edge
  • Software tools, platforms and frameworks for distributed data analytics
  • IoT applications
  • QoS and performance evaluation
  • Networking and communication protocols for distributed applications and services
  • Application and services for smart cities, healthcare and industry 4.0

Published Papers (1 paper)

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Research

31 pages, 7425 KiB  
Article
Evaluating Task-Level CPU Efficiency for Distributed Stream Processing Systems
by Johannes Rank, Jonas Herget, Andreas Hein and Helmut Krcmar
Big Data Cogn. Comput. 2023, 7(1), 49; https://doi.org/10.3390/bdcc7010049 - 10 Mar 2023
Viewed by 2105
Abstract
Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increasingly difficult. A key factor to consider when [...] Read more.
Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increasingly difficult. A key factor to consider when evaluating the performance of a DSPS is CPU efficiency, which is the ratio of the workload processed by the system to the CPU resources invested. In this paper, we argue that developing new performance tools for creating DSPSs that can fulfill SLOs while using minimal resources is crucial. This is especially significant in edge computing situations where resources are limited and in large cloud deployments where conserving power and reducing computing expenses are essential. To address this challenge, we present a novel task-level approach for measuring CPU efficiency in DSPSs. Our approach supports various streaming frameworks, is adaptable, and comes with minimal overheads. This enables developers to understand the efficiency of different DSPSs at a granular level and provides insights that were not previously possible. Full article
(This article belongs to the Special Issue Distributed Applications and Services for Future Internet)
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