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

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.

Significant Trends of Smart Technologies

Although technology advances in a high speed and in different tracks and sectors, among the many major areas of trends of smart technologies are clouds and artificial intelligence. This chapter presents such significant trends in smart technologies with emphasis on clouds and their applications which make the implementation of smart cities efficient. It focuses on the general paradigm for smart technology platforms with five different levels, including edge and fog computing as well as the internet of things. In the chapter, other trends are covered such as data analytics for strategic decision making, artificial intelligence, machine learning, blockchain, open data, and cloud-based data. It also introduces the significance of using predictive analytics and using data for effective deep learning for smart applications.

An Overview of Fog Computing and Edge Computing Security and Privacy Issues

With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.

A Review of Blockchain-enabled Fog Computing in the Cloud Continuum Context

This article surveys the literature in search of systems and components that use Blockchain or Smart Contracts to manage computational resources, store data, and execute services using the Cloud paradigm. This paradigm has extended from warehouse-scale data centres to the edge of the network and in between, giving rise to the domains of Edge and Fog Computing. The Cloud Continuum encompasses the three fields and focuses on the management of applications composed of connected services that span from one end to the other of the computational spectrum. Several components that are commanded by Smart Contracts are identified and compared concerning their functionality. Two important research directions are the experimental evaluation of the identified platforms and the identification of standards that can accelerate the adoption of Blockchain-based Fog platforms.

A Comprehensive Survey on Security and Privacy for Electronic Health Data

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

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

Novel 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.