All one needs to know about fog computing and related edge computing paradigms: A complete survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete 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 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.

In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users’ willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks.

Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications

Device to Device communication enables the deployment of mobile cloudlets in LTE-advanced networks. The distributed nature of mobile users and dynamic task arrivals makes it challenging to schedule tasks fairly among multiple devices. Leveraging the idea of software defined networking, we propose a software defined cooperative offloading model (SDCOM), where the SDCOM controller is deployed at the PDN gateway and schedules tasks in a centralized manner to save the energy of mobile devices and reduce the traffic on access links. We formulate the minimum-energy task scheduling problem as a 0-1 knapsack problem and prove its NP-hardness. To compute the optimal solution as a benchmark, we design the conditioned optimal algorithm based on the aggregated analysis of energy consumption. The greedy algorithm with a polynominal-time complexity is proposed to solve large-scale problems efficiently. To address the problem without predicting future information on task arrivals, we further design an online task scheduling algorithm (OTS). It can minimize the energy consumption arbitrarily close to the optimal solution by appropriately setting the tradeoff coefficient. Moreover, we extend OTS to design a proportional fair online task scheduling algorithm to achieve the fair energy consumption among mobile devices. Extensive trace-based simulations demonstrate the effectiveness of SDCOM for a variety of typical mobile devices and applications.

Software Defined Cooperative Offloading for Mobile Cloudlets

Cooperative cache has become a promising technique to optimize the traffic by caching big data in networks. However, controlling distributed cache nodes to update cached contents synergistically is still challenging in designing cooperative cache systems. This paper proposes an SDN-based Cooperative Cache Network (SCCN) for ISP networks, aiming to minimize the content transmission latency while reducing the inter-ISP traffic. Based on the proposed increment recording mechanism, the SCCN Controller can timely capture the change of content popularity, and place the most popular contents on the appropriate SCCN Switches. We formulate the optimal content placement as a specific multi-commodity facility location problem and prove its NP-hardness. We propose a Relaxation Algorithm (RA) based on relaxation-rounding technique to solve the problem, which can achieve an approximation ratio of $1/2$ in the worst case. To solve large scale problems for big data efficiently, we further design a Heuristic Algorithm (HA), which can find a near-optimal solution with three orders of magnitude speedup compared to RA. Specifically, HA can achieve a desirable tradeoff between the transmission delay and the Internet traffic. We implement a prototype based on Open vSwitch to demonstrate the feasibility of SCCN. Extensive trace-based simulation results show the effectiveness of SCCN under various network conditions.

SDN-Based Big Data Caching in ISP Networks

The invention discloses an energy consumption optimized collaborative task migration method in a mobile cloud computing environment. In the mobile cloudlet environment in an enterprise network or a campus network, the migration method aims at enabling a mobile device to migrate tasks to other device or cloud in the network under conditions of limited electric quantity and limited computing power, reducing redundant computing tasks in the network, promoting the energy consumption efficiency of the redundant computing tasks in the network and enabling the mobile device to obtain higher computing power through collaborating with other devices to execute the local computing tasks. The migration method belongs to concentrated scheduling; task execution scheduling is decided in a controller; and the influences of the device residual electric quantity, the task computing energy consumption, the task transmission energy consumption and the location selection in executing the tasks on the shared execution results are weighed.

Energy consumption optimized collaborative task migration method in mobile cloud computing environment

Dynamic adaptive streaming over HTTP (DASH) has emerged as a popular Internet video service, constituting a growing fraction of LTE network traffic today. We identify the root causes of DASH performance problems in bit-ate stability, energy consumption of User Equipment (UE) and efficiency of bandwidth utilization, from both the users' and network operators' perspectives. Unlike the existing researches that separately studied two important performance metrics in DASH, i.e., Quality of Experience (QoE) and UEs' energy consumption. We propose an energy-aware DASH delivery framework over LTE networks (EDASH), jointly optimizing the network throughput, users' QoE and UEs' energy efficiency. We formulate the bandwidth allocation problem as a nonlinear integer program, and design the EDASH Online Allocation algorithm (EOA). EOA assigns bandwidth based on channel conditions and buffer occupancy of UEs to achieve efficient video delivery among multiple users. Furthermore, we present the detailed design and implementation of EDASH using Apache HTTP server and Android smartphones. Both simulation and experiment results reveal that our scheme can improve the network throughput while striking a better balance between users' QoE and UEs' energy consumption.

Yangjun Zhang

Papers

Software Defined Cooperative Offloading for Mobile Cloudlets

SDN-Based Big Data Caching in ISP Networks

Energy consumption optimized collaborative task migration method in mobile cloud computing environment

EDASH: Energy-Aware QoE Optimization for Adaptive Video Delivery over LTE Networks