Managing the Cloud Continuum: Lessons Learnt from a Real Fog-to-Cloud Deployment (original) (raw)
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IEEE Wireless Communications, 2016
The recent advances in the cloud services technology are fueling a plethora of information technology innovation, including networking, storage and computing. Today, various flavors have evolved of Internet of Things (IoT), cloud computing and the so-called fog computing,a concept referred to capabilities of edge-devices and user's clients to compute, store and exchange data among each other and with the cloud. Though the evolution was not easily foreseeable to happen at such a rapid pace, each piece of it today facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation and smart homes. As most of the cloud, fog and network services today run simultaneously in each scenario, we observe that we are at the dawn of what maybe the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter,embedding capacities such as storage or processing, as well as embedding new functionalities, such as decision making, data collection and forwarding, sharing, etc, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This paper introduces a layered fog-to-cloud (F2C) architecture, its benefits and strengths as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented and a comparative performance analysis, albeit conceptual, all clearly show the way forward towards a new IoT scenario with a set of existing and unforeseen services provided on a highly distributed and dynamic compute, storage and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs as well as conventional clouds.
Evaluating the benefits of combined and continuous Fog-to-Cloud architectures
Computer Communications, 2017
The need to extend the features of Cloud computing to the edge of the network has fueled the development of new computing architectures, such as Fog computing. When put together, the combined and continuous use of fog and cloud computing, lays the foundation for a new and highly heterogeneous computing ecosystem, making the most out of both, cloud and fog. Incipient research efforts are devoted to propose a management architecture to properly manage such combination of resources, such as the reference architecture proposed by the OpenFog Consortium or the recent Fog-to-Cloud (F2C). In this paper, we pay attention to such a combined ecosystem and particularly evaluate the potential benefits of F2C in dynamic scenarios, considering computing resources mobility and different traffic patterns. By means of extensive simulations we specifically study the aspects of service response time, network bandwidth occupancy, power consumption and service disruption probability. The results indicate that a combined fog-to-cloud architecture brings significant performance benefits in comparison with the traditional standalone Cloud, e.g., over 50% reduction in terms of power consumption.
mF2C: towards a coordinated management of the IoT-fog-cloud continuum
2018
Fog computing enables location dependent resource allocation and low latency services, while fostering novel market and business opportunities in the cloud sector. Aligned to this trend, we refer to Fog-to-cloud (F2C) computing system as a new pool of resources, set into a layered and hierarchical model, intended to ease the entire fog and cloud resources management and coordination. The H2020 project mF2C aims at designing, developing and testing a first attempt for a real F2C architecture. This document outlines the architecture and main functionalities of the management framework designed in the mF2C project to coordinate the execution of services in the envisioned set of heterogeneous and distributed resources.
Resource Management in Fog/Edge Computing
ACM Computing Surveys, 2020
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.
Resource Management in Fog/Edge Computing: A Survey
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013–2018, to identify and classify the architectures, infrastructure , and underlying algorithms for managing resources in fog/edge computing.
Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications
Sensors
The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It fo...
Sensors
Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management...
A Paradigm Shift from Cloud to Fog Computing
The computing scenario is shifting from cloud computing towards edge networking which is known as Fog computing. It is now becoming an important facilitator in upcoming internet of things applications. With the rapid increase in the number of internet connected devices, the increased demand of real-time, low-latency services is proving to be a challenge for the traditional cloud computing framework. Fog computing is observed to be an aerial compared to the traditional cloud. It is hierarchical distributed architecture. This computing ensures the low and predictable latency in the latency-sensitive of Internet of Things (IoT) applications such as the healthcare services which is primary objective of this computing. This paper focuses on architecture, policy manager involved in this novel computing technology and with its usage and applicabilty.
Budget and Performance-efficient ApplicationDeployment along Edge-Fog-Cloud Ecosystem
2019
Applications that make use of the Internet of Things (IoT) capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted towards the cloud data centres for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, sending the data towards a cloud data centre can lead to a performance bottleneck. With the emergence of Fog and Edge computing hosted microservices, data processing could be moved towards the network edge. We propose a novel Pareto-based approach that makes use of a multi-criteria bin packing optimisation for the efficient and optimal distributed deployment of microservices -- along the edge, fog/cloudlet and cloud tiers. This optimisation takes account of non-functional requirements, such as operational cost, compute resource utilisation, service availability, response time, latency and similar. The results show that the present approach provides an optimal and sustainable consumptio...
A Study of Moving from Cloud Computing to Fog Computing
Qubahan Academic Journal, 2021
The exponential growth of the Internet of Things (IoT) technology poses various challenges to the classic centralized cloud computing paradigm, including high latency, limited capacity, and network failure. Cloud computing and Fog computing carry the cloud closer to IoT computers in order to overcome these problems. Cloud and Fog provide IoT processing and storage of IoT items locally instead of sending them to the cloud. Cloud and Fog provide quicker reactions and better efficiency in conjunction with the cloud. Cloud and fog computing should also be viewed as the safest approach to ensure that IoT delivers reliable and stable resources to multiple IoT customers. This article discusses the latest in cloud and Fog computing and their convergence with IoT by stressing deployment's advantages and complexities. It also concentrates on cloud and Fog design and new IoT technologies, enhanced by utilizing the cloud and Fog model. Finally, transparent topics are addressed, along with potential testing recommendations for cloud storage and Fog computing, and IoT.