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Serverless Continuum: Serverless Computing for the Edge-Cloud-IoT Continuum
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Serverless Continuum: Serverless Computing for the Edge-Cloud-IoT Continuum

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1409

Special Issue Editors

Dr. Stefan Nastic

E-Mail Website
Guest Editor
Distributed Systems Group, Tu Wien, 1040 Vienna, Austria
Interests: serverless computing; edge-cloud continuum; reliability engineering; AI/ML
Special Issues, Collections and Topics in MDPI journals
Dr. Patrizio Dazzi

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Interests: distributed systems; high-performance computing; edge computing; decentralised computing
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Tserpes

E-Mail Website
Guest Editor
Informatics and Telematics, Harokopio University of Athens, 176 76 Athens, Greece
Interests: edge-cloud continuum; performance modeling; machine learning; data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Sashko Ristov

E-Mail Website
Guest Editor
Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria
Interests: serverless computing; cloud engineering and federation; performance modeling and optimization; BIM; digital twins

Special Issue Information

Dear Colleagues,

The amalgamation of multiple edge and cloud clusters into the so-called Edge-Cloud-IoT continuum is completely transforming how we perceive, consume, and manage compute infrastructures and other continuum resources, such as storage and network. Additionally, novel resource types, such as edge-based sensors, and specialized devices, such as AI inference accelerators, are increasingly becoming an integral part of this novel computing continuum. This calls for novel and fundamentally different approaches to application execution models as well as infrastructure management and operation in the Edge-Cloud continuum.

Serverless computing has emerged as a suitable paradigm for the emerging Edge-Cloud-IoT continuum systems and applications. By abstracting away the underlying infrastructure operation and management, it promises to improve developers' experiences and also optimize overall resource utilization. However, a number of challenges related to programming support, reliability engineering, and performance engineering still remain.

We are inviting novel research papers and contributions on the following non-exhaustive list of topics:

  • FaaS Platforms for Edge-Cloud-IoT Continuum.
  • Serverless Compute Fabric for Edge and Cloud.
  • Serverless Backend Services for Edge-Cloud FaaS Paradigm.
  • Serverless management of Reliable Edge-Cloud Infrastructures.
  • Self -Provisioning Serverless Infrastructure for Edge-Cloud-IoT Continuum.
  • Resource and Performance Isolation for FaaS in Edge-Cloud-IoT Continuum.
  • AI and Machine Learning for Serverless Computing in Edge-Cloud-IoT Continuum.
  • In-network FaaS Computing for the Edge-Cloud-IoT Continuum.
  • Fault tolerance and resilience for Serverless computing.
  • Serverless and FaaS approaches for Edge and Cloud.
  • Programming Models for Serverless Computing.

Dr. Stefan Nastic
Dr. Patrizio Dazzi
Dr. Konstantinos Tserpes
Dr. Sashko Ristov
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Published Papers (1 paper)

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Research

26 pages, 4909 KiB  
Article
FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms
by Ming Li, Jianshan Zhang, Jingfeng Lin, Zheyi Chen and Xianghan Zheng
Sensors 2023, 23(18), 7829; https://doi.org/10.3390/s23187829 - 12 Sep 2023
Viewed by 1042
Abstract
The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into [...] Read more.
The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments. Full article
(This article belongs to the Special Issue Serverless Continuum: Serverless Computing for the Edge-Cloud-IoT Continuum)
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