Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.

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Network Function Virtualization: State-of-the-Art and Research Challenges

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

https://www.cs.wm.edu/~liqun/paper/mobidata15-fog.pdf

A Survey of Fog Computing: Concepts, Applications and Issues

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.

Network function virtualization: Challenges and opportunities for innovations

The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as "the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially breakthrough technology amalgamation.

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Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing

As an important component of the virtual reality (VR) technology, 360-degree videos provide users with panoramic view and allow them to freely control their viewing direction during video playback. Usually, a player displays only the visible portion of a 360 video. Thus, fetching the entire raw video frame wastes bandwidth. In this paper, we consider the problem of optimizing 360 video delivery over cellular networks. We first conduct a measurement study on commercial 360 video platforms. We then propose a cellular-friendly streaming scheme that delivers only 360 videos' visible portion based on head movement prediction. Using viewing data collected from real users, we demonstrate the feasibility of our approach, which can reduce bandwidth consumption by up to 80% based on a trace-driven simulation.

Optimizing 360 video delivery over cellular networks

IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1-2%) with orders of magnitude speedup relative to solving even the LP relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates. Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by LRU or LFU cache replacement policies. We also investigate the trade-off between disk space and network bandwidth.

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Optimal content placement for a large-scale VoD system

Flare is a practical system for streaming 360-degree videos on commodity mobile devices. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. We conduct an IRB-approved user study where we collect head movement traces from 130 diverse users to gain insights on how to design the viewport prediction mechanism for Flare. We then develop novel online algorithms that determine which spatial portions to fetch and their corresponding qualities. We also innovate other components in the streaming pipeline such as decoding and server-side transmission. Through extensive evaluations (~400 hours' playback on WiFi and ~100 hours over LTE), we show that Flare significantly improves the QoE in real-world settings. Compared to non-viewport-adaptive approaches, Flare yields up to 18x quality level improvement on WiFi, and achieves high bandwidth reduction (up to 35%) and video quality enhancement (up to 4.9x) on LTE.

https://dl.acm.org/doi/pdf/10.1145/3241539.3241565

Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices

Peer-to-peer systems can be used to form a low-latency decentralized data delivery system. Structured peer-to-peer systems provide both low latency and excellent load balance with uniform query and data distributions. Under the more common skewed access distributions, however, individual nodes are easily overloaded, resulting in poor global performance and lost messages. This paper describes a lightweight, adaptive, and system-neutral replication protocol, called LAR, that maintains low access latencies and good load balance even under highly skewed demand. We apply LAR to Chord and show that it has lower overhead and better performance than existing replication strategies.

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Vijay Gopalakrishnan

Papers

Network function virtualization: Challenges and opportunities for innovations

Optimizing 360 video delivery over cellular networks

Optimal content placement for a large-scale VoD system

Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices

Adaptive replication in peer-to-peer systems