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Electronics
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Resource Management in Cloud/Edge Computing
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Resource Management in Cloud/Edge Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 11973

Special Issue Editors

Prof. Dr. Jordi Garcia

E-Mail Website
Guest Editor
Advanced Network Architectures Lab (CRAAX), Department of Computer Architecture, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08800 Vilanova i la Geltrú, Spain
Interests: smart cities; smart scenarios; cloud computing; edge computing; resources management; data management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eva Marín-Tordera

E-Mail Website
Guest Editor
Advanced Network Architectures Lab (CRAAX), Department of Computer Architecture, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08800 Vilanova i la Geltrú, Spain
Interests: cloud and fog management; cybersecurity at the edge, and prediction maintenance strategies based on AI
Prof. Dr. Vitor Barbosa Souza

E-Mail Website
Guest Editor
Informatics Department, Federal University of Viçosa (UFV), 36570-900 Viçosa/MG, Brazil
Interests: fog computing; IoT; QoS; edge resources organization

Special Issue Information

Dear Colleagues,

Edge computing has emerged as a challenging technology to complement the well-known benefits of cloud computing, adding the capacity to exploit features derived from locality as its main additional advantage. For this reason, environments leveraging the capabilities of both technological paradigms, cloud and edge, are becoming prominent. While the cloud is built as a massive amount of homogeneous resources, the edge is conceived as a highly heterogeneous environment, posing additional challenges to their management.

This Special Issue is intended to collect the state of the art in resources management in the context of both cloud computing and edge computing as well as environments which combine cloud and edge technologies. We invite high-quality submissions from academia and industry that address topics related to any stage of the resources management process, including, but not limited to resources discovery, resources classification and organization, resources allocation, resources clustering, resources orchestration, static and dynamic scheduling, resources interoperability and portability, resources monitoring, green management, security and privacy at the edge, and fault tolerance in resources management.

Prof. Dr. Jordi Garcia
Prof. Dr. Eva Marín-Tordera
Prof. Dr. Vitor Barbosa Souza
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. Electronics 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 2400 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/edge platforms
  • Resources classification
  • Resources allocation
  • Resources clustering
  • Resources orchestration
  • Static scheduling
  • Dynamic scheduling
  • Resources monitoring
  • Security/privacy at the edge
  • Service to resource matching

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Published Papers (2 papers)

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Research

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29 pages, 2237 KiB  
Article
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing
by Vijay Prakash, Seema Bawa and Lalit Garg
Electronics 2021, 10(11), 1320; https://doi.org/10.3390/electronics10111320 - 31 May 2021
Cited by 18 | Viewed by 4182
Abstract
Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion [...] Read more.
Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources. Full article
(This article belongs to the Special Issue Resource Management in Cloud/Edge Computing)
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Review

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43 pages, 5618 KiB  
Review
Effective Cloud Resource Utilisation in Cloud ERP Decision-Making Process for Industry 4.0 in the United States
by Marlene Marinho, Vijay Prakash, Lalit Garg, Claudio Savaglio and Seema Bawa
Electronics 2021, 10(8), 959; https://doi.org/10.3390/electronics10080959 - 16 Apr 2021
Cited by 31 | Viewed by 6522
Abstract
Cloud enterprise resource planning (C-ERP) represents an evolution of traditional ERP, which also offers the advantages of cloud computing (CC) such as ease of use and resource elasticity. This article presents the opportunities and challenges of the C-ERP adoption for industry 4.0 in [...] Read more.
Cloud enterprise resource planning (C-ERP) represents an evolution of traditional ERP, which also offers the advantages of cloud computing (CC) such as ease of use and resource elasticity. This article presents the opportunities and challenges of the C-ERP adoption for industry 4.0 in the United States as well as the factors that boost or hinder such a decision. The quantitative research method is used to gather the predictor factors and correlation amongst them. An online survey questionnaire received 109 responses, mainly decision-makers and professionals from the US consumer goods industry. Statistical analysis has been carried out to rank the different levels of influence in the C-ERP adoption decision. The predictor’s complexity and regulatory compliance positively influence C-ERP private service deployment, whereas technology readiness is a good predictor of community service deployment. This paper also proposes a decision support system (DSS), tailored to industry 4.0, and aimed at assisting decision-makers in adopting C-ERP as an effective resource for decision-making. The DSS is built upon the predictors using the analytic hierarchy process (AHP) and it supports decision-makers in the selection of services and deployment models for C-ERP as a resource. Full article
(This article belongs to the Special Issue Resource Management in Cloud/Edge Computing)
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