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.

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Resource Management in Fog/Edge Computing: A Survey on Architectures, Infrastructure, and Algorithms

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.

/pdf/resource-management-in-fog-edge-computing-a-survey-nbyahhyns9.pdf

Resource Management in Fog/Edge Computing: A Survey.

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

A comprehensive survey of load balancing techniques in software-defined network

Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon’s scheduling interface on the frame processing function of the bridge. To show qCon’s effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device—a Raspberry Pi 3 Model B board.

https://www.mdpi.com/1424-8220/18/10/3444/pdf

qCon: QoS-Aware Network Resource Management for Fog Computing.

Kubernetes is the most popular container orchestration platform that enables users to create and run multiple containers in cloud environments. Kubernetes offers resource management to isolate the resource usage of containers on a host server because performance isolation is an important factor in terms of service quality. This paper investigates whether the resource management of Kubernetes is sufficient to isolate the performance of containers. This is different from previous studies that mostly focuses on efficient resource management rather than the study on performance interference. We evaluate the performance interference 1) between CPU-intensive and networkintensive containers, and 2) between multiple network-intensive containers. Our evaluation results show that containers experience performance degradation by 50% due to the co-located containers even under the resource management of Kubernetes. This paper also points out that the root cause of the performance interference between multiple network-intensive containers is CPU contention, not network bandwidth. As a result, Kubernetes needs to consider the CPU usage of network-related workloads in resource management in order to mitigate the performance interference.

On the Resource Management of Kubernetes

Current network virtualization allows tenants to have their own virtual networks. However, new demands to "program" virtual networks at a finer granularity have arisen as tenants want the ability to provision and control switches and links in their virtual networks. This study proposes a new concept called the p-NIaaS model. The p-NIaaS model enables tenants to program their own packet processing logic and monitor network status from any virtual network infrastructure, which is not possible with current network virtualization. This article presents the Libera network hypervisor that implements the p-NIaaS model. Libera overcomes the shortcomings of existing network hypervisors such as scalability, VM migration support, and flexibility. The evaluation shows that the Libera hypervisor is highly scalable and effectively supports VM migration. We also present the overheads of Libera. Libera incurs up to 11 percent overhead in comparison with a non-virtualized network, which we believe is promising in the first prototype of the p-NIaaS model.

Libera for Programmable Network Virtualization

This paper proposes V-Sight, a network monitoring framework for software-defined networking (SDN)-based virtual networks. Network virtualization with SDN (SDN-NV) makes it possible to realize programmable virtual networks; so, the technology can be beneficial to cloud services for tenants. However, to the best of our knowledge, although network monitoring is a vital prerequisite for managing and optimizing virtual networks, it has not been investigated in the context of SDN-NV. Thus, virtual networks suffer from non-isolated statistics between virtual networks, high monitoring delays, and excessive control channel consumption for gathering statistics, which critically hinders the benefits of SDN-NV. To solve these problems, V-Sight presents three key mechanisms: 1) statistics virtualization for isolated statistics, 2) transmission disaggregation for reduced transmission delay, and 3) pCollector aggregation for efficient control channel consumption. V-Sight is implemented on top of OpenVirteX, and the evaluation results demonstrate that V-Sight successfully reduces monitoring delay and control channel consumption up to 454 times.

Network Monitoring for SDN Virtual Networks

TensorExpress provides in-network communication scheduling for distributed deep learning (DDL). In cloud-based DDL, parameter communication over a network is a key bottleneck. Previous studies proposed tensor packet reordering approaches to reduce network blocking time. However, network contention still exists in DDL. TensorExpress mitigates network contention and reduces overall training time. It schedules tensor packets in-network using P4, a switch programming language. TensorExpress improves latency and network blocking time up to 2.5 and 2.44 times, respectively.

Chuck Yoo

Papers

qCon: QoS-Aware Network Resource Management for Fog Computing.

On the Resource Management of Kubernetes

Libera for Programmable Network Virtualization

Network Monitoring for SDN Virtual Networks

TensorExpress: In-Network Communication Scheduling for Distributed Deep Learning