scholarly journals A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

2022 ◽  
Author(s):  
Anupama Mampage ◽  
Shanika Karunasekera ◽  
Rajkumar Buyya
Keyword(s):  
Resource Management ◽  
Fog Computing ◽  
Resource Provisioning ◽  
Future Research ◽  
Holistic View ◽  
Fine Grained ◽  
Computing Model ◽  
Service Ecosystem ◽  
Edge And Fog Computing ◽  
Auto Scaling

Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals FOG computing and its various uses in different applications

2018 ◽  
Vol 7 (2.7) ◽  
pp. 345
Author(s):  
Chandra Sekhar Maganty ◽  
Kothamasu Kiran Kumar
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Fog Computing ◽  
End User ◽  
New Era ◽  
Streaming Applications ◽  
Computing Model ◽  
Use Of Resources ◽  
To Come ◽  
Edge And Fog Computing

Cloud computing is the transformation, which involves storing large applications where data or information is exchanged among differ-ent platforms for giving good service to clients who belong to different organizations. It assures great use of resources by making data, software and infrastructure available with minimal cost along with security and reliability. Even though cloud computing gives many advantages, it has certain limitations like network congestion, fault tolerance, less bandwidth etc. To come out of this issue a new era computing model is introduced called Fog Computing. This new computing model can transfer fragile data without any delay to other devices in the network. The only difference between both is fog is located more close to the end user or the device and gives response to the client instantly. Moreover, it is beneficial to the real time streaming applications, internet of things which need reliable internet con-nectivity along with high speed. This paper is a review on Fog Computing, differences in edge and fog computing, use cases of fog and the architecture.

scholarly journals Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
László Toka
Keyword(s):  
High Reliability ◽  
Fog Computing ◽  
Resource Provisioning ◽  
Use Case ◽  
End User ◽  
Delay Constraints ◽  
Edge And Fog Computing ◽  
Novel Applications ◽  
Stream Analytics ◽  
Potential Use

AbstractNovel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether call for a powerful resource provisioning platform that offers the applications the best of the underlying infrastructure. We therefore propose Kubernetes-edge-scheduler that provides high reliability for applications in the edge, while provisioning less than 10% of resources for this purpose, and at the same time, it guarantees compliance with the latency requirements that end users expect. We present a novel topology clustering method that considers application latency requirements, and enables scheduling applications even on a worldwide scale of edge clusters. We demonstrate that in a potential use case, a distributed stream analytics application, our orchestration system can reduce the job completion time to 40% of the baseline provided by the default Kubernetes scheduler.

Edge and Fog Computing Enabled AI for IoT-An Overview

2019 ◽  
Author(s):  
Zhuo Zou ◽  
Yi Jin ◽  
Paavo Nevalainen ◽  
Yuxiang Huan ◽  
Jukka Heikkonen ◽  
...  
Keyword(s):  
Fog Computing ◽  
Edge And Fog Computing

scholarly journals Adopting the Situation in School Questionnaire to Examine Physical Education Teachers’ Motivating and Demotivating Styles Using a Circumplex Approach

2021 ◽  
Vol 18 (14) ◽  
pp. 7342
Author(s):  
Géraldine Escriva-Boulley ◽  
Emma Guillet-Descas ◽  
Nathalie Aelterman ◽  
Maarten Vansteenkiste ◽  
Nele Van Doren ◽  
...  
Keyword(s):  
Physical Education ◽  
Autonomy Support ◽  
Future Research ◽  
Physical Education Teachers ◽  
Structure Control ◽  
Fine Grained ◽  
Circular Structure ◽  
Need Support ◽  
Academic Courses

Grounded in SDT, several studies have highlighted the role of teachers’ motivating and demotivating styles for students’ motivation, learning, and physical activity in physical education (PE). However, most of these studies focused on a restricted number of motivating strategies (e.g., offering choice) or dimensions (e.g., autonomy support). Recently, researchers have developed the Situations-in-School (i.e., SIS-Education) questionnaire, which allows one to gain a more integrative and fine-grained insight into teachers’ engagement in autonomy-support, structure, control, and chaos through a circular structure (i.e., a circumplex). Although teaching in PE resembles teaching in academic courses in many ways, some of the items of the original situation-based questionnaire (e.g., regarding homework) are irrelevant to the PE context. In the present study, we therefore sought to develop a modified, PE-friendly version of this earlier validated SIS-questionnaire—the SIS-PE. Findings in a sample of Belgian (N = 136) and French (N = 259) PE teachers, examined together and as independent samples, showed that the variation in PE teachers’ motivating styles in this adapted version is also best captured by a circumplex structure, with four overarching styles and eight subareas differing in their level of need support and directiveness. The SIS-PE possesses excellent convergent and concurrent validity. With the adaptations being successful, great opportunities for future research on PE teachers (de-)motivating styles are created.

scholarly journals Affinity Selection in Germinal Centers: Cautionary Tales and New Opportunities

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1040
Author(s):  
Jose Faro ◽  
Mario Castro
Keyword(s):  
T Cell ◽  
B Cell ◽  
Cell Receptor ◽  
Future Research ◽  
Mathematical Framework ◽  
Immune Synapse ◽  
Cell Receptors ◽  
3 Dimensional ◽  
Fine Grained ◽  
Ligand Interactions

Our current quantitative knowledge of the kinetics of antibody-mediated immunity is partly based on idealized experiments throughout the last decades. However, new experimental techniques often render contradictory quantitative outcomes that shake previously uncontroversial assumptions. This has been the case in the field of T-cell receptors, where recent techniques for measuring the 2-dimensional rate constants of T-cell receptor–ligand interactions exposed results contradictory to those obtained with techniques measuring 3-dimensional interactions. Recently, we have developed a mathematical framework to rationalize those discrepancies, focusing on the proper fine-grained description of the underlying kinetic steps involved in the immune synapse. In this perspective article, we apply this approach to unveil potential blind spots in the case of B-cell receptors (BCR) and to rethink the interactions between B cells and follicular dendritic cells (FDC) during the germinal center (GC) reaction. Also, we elaborate on the concept of “catch bonds” and on the recent observations that B-cell synapses retract and pull antigen generating a “retracting force”, and propose some testable predictions that can lead to future research.

Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID Controllers

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Mikael Sabuhi ◽  
Nima Mahmoudi ◽  
Hamzeh Khazaei
Keyword(s):  
Adaptive Controller ◽  
Service Level ◽  
Resource Provisioning ◽  
Performance Model ◽  
Software Systems ◽  
Adaptive Controllers ◽  
Efficient Resource ◽  
Reliable Performance ◽  
Auto Scaling ◽  
Compare And Contrast

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller’s objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-level Agreements, while leading to efficient resource provisioning.

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