Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokemon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have bought us various degrees of digital transformation. The term `metaverse' has been coined to further facilitate the digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalysed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital `big bang' of our cyberspace is not far away. This survey paper presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems, and illustrates the possibility of the digital `big bang'. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously - Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors -- Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for the development of the metaverse.

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Android Open Source Projectを対象としたパッチレビュー活動の調査

A comprehensive survey of service function chain provisioning approaches in SDN and NFV architecture

The Mobile Edge-Cloud (MEC) network has emerged as a promising networking paradigm to address the conflict between increasing computing-intensive applications and resource-constrained mobile Internet-of-Thing (IoT) devices with portable size and storage. In MEC environments, Virtualized Network Functions (VNFs) are deployed for provisioning network services to users to reduce the service cost on top of dedicated hardware infrastructures. However, VNFs may suffer from failures and malfunctions while network service providers have to guarantee continuously reliable services to their consumers to meet the ever-growing service demands of users, thereby securing their revenues for the service. In this article, we focus on reliable VNF service provisioning in MECs, by placing primary and backup VNF instances to cloudlets in an MEC network to meet the service reliability requirements of users. We first formulate a novel VNF service reliability problem with the aim to maximize the revenue collected by admitting as many as user requests while meeting their different reliability requirements, assuming that requests arrive into the system one by one without the knowledge of future arrivals, and the admission or rejection decision must be made immediately. We then develop two efficient online algorithms for the problem under two different backup schemes: the on-site (local) and off-site (remote) schemes, by adopting the primal-dual updating technique. Both algorithms achieve provable competitive ratios with bounded moderate resource capacity violations. We finally evaluate the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, compared with existing baseline algorithms.

Reliability-Aware Network Service Provisioning in Mobile Edge-Cloud Networks

It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates 100%: (i) authorized ID verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB, and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM.

Network Function Virtualization (NFV) turns a sequence of network functions on hardwares into a service chain of virtual network functions (VNFs) provisioned on virtual machines or containers. However, the chain of VNFs may suffer from interruption as long as one VNF fails due to software faults or hardware malfunctions. A common approach to ensuring high availability is to provide backup nodes for primary VNFs. However, existing work on allocating backup nodes have not considered the heterogeneous resource demands of different VNFs. In this paper, we formalize the resource-aware backup allocation problem, which aims to minimize the backup resource consumption while meeting the overall availability demand. To this end, we prove the NP-hardness of this problem and propose the RABA-CDDE algorithm based on differential evolution to solve it. Besides, to reduce the computation overhead of RABA-CDDE, a greedy algorithm is proposed. Our extensive evaluation shows that the proposed algorithms can reduce the resource consumption by about 15% and 35% respectively compared to the state-of-art solutions in dedicated and shared protection scenarios.

RABA: Resource-Aware Backup Allocation For A Chain of Virtual Network Functions

Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-communities (e.g., cities and metropolises), urban structures (e.g., smart homes and cars), and living structures (e.g., human bodies, synthetic genomes). The question that we address in this article pertains to designing these CPS systems to be resilient-from-the-ground-up, and through progressive learning, resilient-by-reaction. An optimally designed system is resilient to both unique attacks and recurrent attacks, the latter with a lower overhead. Overall, the notion of resilience can be thought of in the light of three main sources of lack of resilience, as follows: exogenous factors, such as natural variations and attack scenarios; mismatch between engineered designs and exogenous factors ranging from DDoS (distributed denial-of-service) attacks or other cybersecurity nightmares, so called "black swan" events, disabling critical services of the municipal electrical grids and other connected infrastructures, data breaches, and network failures; and the fragility of engineered designs themselves encompassing bugs, human-computer interactions (HCI), and the overall complexity of real-world systems. In the paper, our focus is on design and deployment innovations that are broadly applicable across a range of CPS application areas.

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap.

The increasing cost of cloud services and the need for decentralization of servers has led to a rise of interest in Edge computing. Edge computing brings the servers closer to the end users which helps in reducing network latency. However, the presence of multiple edge servers adversely affects the reliability due to difficulty in maintenance of heterogeneous servers. This paper aims at evaluating the performance of various server configuration models in edge computing using EdgeCloudSim, a popular simulator for edge computing. The performance is evaluated in terms of service time and percentage of failed tasks for an Augmented Reality application.We evaluated the performance of the following edge computing models, Exclusive: Mobile only, Edge only, Cloud only; and Hybrid: Edge & Cloud hybrid with load-balancing on the Edge, and Mobile & Edge hybrid. We analyzed the impact of variation of different parameters such as WAN bandwidth, cost of cloud resources, heterogeneity of edge servers, etc., on the performance of the edge computing models. We show that due to variation in the above parameters, the exclusive models are not sufficient for computational requirements and there is a need for hybrid edge computing models.

Tango of edge and cloud execution for reliability

Video streaming, one of the most popular technologies for online video playback, has already been applied to 4G LTE networks. Previous work has been devoted to understanding the power consumption in general 4G LTE networks, while it is still unclear how the online video streaming makes impact on the power performance of mobile devices. Inspired by this, this paper investigates the relationship between the mobile device's power performance characteristics and the behaviours of video streaming in 4G LTE networks. There are many natural issues/questions that are clearly interesting and important, while it is non-trivial to answer these issues/questions exactly (e.g., where is the energy saving room? how much is it?). To address a series of issues like the above, we formulate our energy models together with an algorithm that can assist our analysis. Particularly, we design a systematic platform, and conduct a comprehensive and also deep analysis on the power consumption of video streaming in 4G LTE networks. Our experiments reveal us a series of valuable findings--the saving room in the network part is large (from 41.86 to 69.62%), the number of RRC tails and the transmission pattern could be promising for optimizing the power consumption, for example.

Power consumption analysis of video streaming in 4G LTE networks

In this paper, we propose iCellSpeed, an on-device solution to increase data access speed by substantiating unrealized performance potentials. We find that performance potentials are missed in today's mobile networks, as the data speed a user device gets is much lower than what the device could get. The issue is rooted in the current cell selection practice, which misses good candidate cells that offer faster access speed, thus under-utilizing the available capabilities in mobile networks. We design iCellSpeed to facilitate network-controlled cell selection with proactive device-side assistance towards more desirable cells. Our evaluation over AT&T and Verizon confirms its effectiveness. iCellSpeed increases data access speed by more than 10 Mbps at 79% of test locations (> 25Mbps at 29% of locations, up to 80.6 Mbps). It doubles access speed at 62.5% of locations with the gain up to 28.4x. Datasets are available at [9].

Chunyi Peng

Papers

RABA: Resource-Aware Backup Allocation For A Chain of Virtual Network Functions

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap.

Tango of edge and cloud execution for reliability

Power consumption analysis of video streaming in 4G LTE networks

iCellSpeed: increasing cellular data speed with device-assisted cell selection