United States

&quot;Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all
players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge Computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for
the mean GFT is 0 ms in the best case and 9.26 ms in the worst case, providing a uniform user experience, even when mobility is as high as 50% (it means 50% of the players are moving). When there is mobility, the GFT increases by 28.29% in the best
case and 37.18% in the worst case, compared to a no-mobility scenario. We also demonstrate that, given computing power, there is a tradeoff between responsiveness and GFT.&quot;

IEEE NCA 2022

Mobility-aware Multi-Access Edge Computing for Multiplayer Augmented and Virtual Reality Gaming

game frame time

virtual and augmented reality games

service placement

offloading decision

multi-access edge computing

mobility

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

"Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all
players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge Computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for
the mean GFT is 0 ms in the best case and 9.26 ms in the worst case, providing a uniform user experience, even when mobility is as high as 50% (it means 50% of the players are moving). When there is mobility, the GFT increases by 28.29% in the best
case and 37.18% in the worst case, compared to a no-mobility scenario. We also demonstrate that, given computing power, there is a tradeoff between responsiveness and GFT."

technical paper

### Welcome to IEEE NCA 2022!

NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing. NCA, which is sponsored by the IEEE Computer Society, reaches out to both researchers and practitioners, and to both academia and industry. The conference features keynotes, technical presentations, and workshops.

All papers presented at the main conference and the workshops will be submitted to IEEE Xplore for publication and indexing.

[More about NCA](https://www.nca-ieee.org/2022/index.html)

**Organization**

* Steering Committee Chair: Dimiter R. Avresky, *IRIANC, Boston/Munich, USA/Germany*
* General Chair: Michele Colajanni, *University of Bologna, Italy*
* Program Chair: Miguel Pardal, *INESC-ID, Lisboa, Portugal*
* Program Chair: Luca Ferretti, *University of Modena and Reggio Emilia, Italy*
* Financial Chair: Mauro Andreolini, *University of Modena and Reggio Emilia, Italy*

**The topics of interest include, but are not limited to:**

* Theory of Network Computing
* Autonomic Network Computing
* Content Delivery Network (CDN)
* High Speed Networks/Protocols and Middleware
* Routing Mechanisms
* Overlay Networks/Peer-to-Peer Systems
* System Area Networks (SAN)/Clusters
* Performance Modeling/Quality of Services (QoS) Issues
* Web Caching and Switching
* Dependable Wide, Local, and System Area Networks
* Network Security
* Intrusion-Tolerant Networked Systems
* Scalable and Dependable Servers and Data Centers
* Middleware for Dependable Network Computing
* Self* (Configuring, Healing, Optimizing, Protecting, Organizing, Aware)
* Platforms for Network Computing
* Network Protocols: Verification and Validation
* Autonomic Network Computing for Achieving Self* Properties and Applications Availability
* Seamless and Virtual Capabilities for Implementing Self* Properties
* Software Defined Networking (SDN): Principles and Implementation
* Designing Highly-Available and Secure SDN
* Network Functions Virtualization (NFV): Design and Optimization
* Network Cloud Computing
* Machine Learning Techniques for Cloud Resources Management
* Big Data in Clouds for IoT
* Future Internet and Communicating Objects
* Mobile Ad-Hoc Networks (MANET)
* Sensor Networks
* Fog/Edge Computing
* Energy-Efficient Computation and Communication
* Applications to Real and/or Complex Problems, Practical Experiences and Prototypes
* Programming Environments for Distributed Systems
* Blockchain
* Byzantine Fault Tolerance
* Web and Mobile Security
* Security of Networked Applications (including Automotive, Industrial and Cyberphysical Systems)


To access the IEEE NCA 2022 content, [please register here ](https://www.nca-ieee.org/2022/registration.html)

NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing.

Energy efficiency is considered a significant concern in the deployment and operation of data center networks. In this work, to address the energy consumption problem in data center networks, we propose an energy-efficient routing approach that considers that the network devices work based on traffic demand. Our approach monitors the traffic demand to improve the energy efficiency in SD-DCN networks. The simulation results reflect the enhancement of the proposed approach to optimize the end-to-end delay, throughput, and energy savings.

Energy-Aware Routing in SDN Enabled Data Center Network

Cloud-based services are a current approach for developing large-scale applications with advantages such as flexibility, access to on-demand resources, and business agility. The overall application functionality results from complex interactions of many decoupled services, each having its operational specificity. Due to this complexity, the manual configuration of these systems is very arduous, error-prone and likely to impair the quality of service, leading to malfunctioning services, lowering availability and accruing costs. Identifying the optimal solution to simultaneously optimize availability and costs, whilst meeting service-level objectives remains a challenge for professionals developing solutions using cloud services. This paper proposes a mathematical formulation of a bi-objective problem to identify the optimal set of solutions for the system configuration. Empirical evaluation of the proposed approach in a case study of a real industrial scenario results in an R-Squared of 0.85, an MSE of 0.021 and an optimization accuracy of 0.928. These methods can help practitioners to keep services at an optimum configuration enabling autonomic service operation, whilst improving availability and cost.

Bi-objective optimization of availability and cost for cloud services

Firewalls for IoT devices can be enforced at the gateway router or at the cloud. Utilizing a fog based architecture, firewalls can also be enforced at the fog layer. Depending on where the firewall is running, varying delays and overhead will be experienced by IoT devices. Some IoT devices are sensitive to the incurred delays. So choosing the most suitable platform to enforce a firewall for each individual device considering the delay sensitivity of the device is important. This presentation  gives an overview of a delay-aware IoT firewall enforcing mechanism using fog-cloud architecture. The presentation includes the system architecture, methodology, workflow of dynamic assignment of different devices to different platforms, the idea of delay monitoring and load-balancing, and future work.

Enforcing delay-aware fog-cloud based IoT firewall (Work In Progress)

"This work present a software-defined, dynamic and autonomous network slice management middleware for 5G-based industrial internet of things use
cases, such as adaptive robotic repair. "

Software-defined Dynamic 5G Network Slice Management for Industrial Internet of Things

"During this presentation, we consider communication market scenarios where operators lease resources from the InP.
We propose a resolution approach based on an admission control program and a greedy heuristic algorithm that performs the resource allocation.
We show that our algorithm provides good results for multiple scenarios generated in a real environment.
"

Inter-slice B5G Bandwidth Resource Allocation

In beyond 5G (B5G) network, unmanned aerial vehicle (UAV) is considered as a promising solution to improve network coverage and satisfy the wide variety of service requirements. In B5G, mobile terminals (MTs) will be able to connect to
UAVs flying in different altitudes and serving different classes of applications such as enhanced mobile broadband (eMBB), ultra reliable low latency communication (uRLLC) and security intensive applications such as security as a service (SaaS). It
may be noted that UAVs in high altitude incur high power consumption, but reduced secrecy throughput and packet loss. In contrast, lower altitude UAVs incur high packet loss and secrecy throughput with reduced power consumption. Due to
such trade-off and the fact that the traditional user association problem is NP hard, selecting an appropriate UAV to satisfy the user requirement is a computationally challenging task. In this work, an UAV selection mechanism has been proposed for UAV enabled B5G network based on analytic hierarchy process (AHP) which essentially captures the aforementioned trade-off while associating the MTs with the UAVs. The proposed AHP selects the best UAV based on the application class. Simulation results show that the proposed UAV selection mechanism outperforms the existing best downlink (BD) based approach in terms of user throughput, secrecy throughput and packet loss rate

An analytic hierarchy process (AHP) based UAV selection mechanism for beyond 5G networks

In this work, a network selection mechanism for reconfigurable intelligent surface (RIS) assisted network has been proposed. The goal of the proposed mechanism is to select appropriate base station (BS) or BS-RIS pair to maximize user throughput while satisfying the delay and reliability constraints of uRLLC users. First, the network selection mechanism for RIS assisted network has been formulated as multi arm bandit (MAB) problem; Then, based on the formulation, a network selection strategy has been proposed. Finally, through extensive system level simulations, it has been shown that the proposed scheme outperforms the existing A3 handover scheme in terms of reliability and user throughput.

MAB based Network Selection mechanism for URLLC users in RIS assisted network

Video streaming is the dominating traffic in today's data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed on seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40-50% lower completion time and 5-60% reduced total cost.

MPEC2: Multilayer and Pipeline Video Encoding on the Computing Continuum

It becomes very common to use cell phones in public transportation and the cars. Vehicular networking has a major problem which is the degradation of signal quality due to interference and the large number of mobile devices. Artificial intelligence (AI) is a promising technique for next-generation wireless networks. Deep learning is a type of AI derived from machine learning; here the machine can learn by itself, unlike programming where it is content to execute rules to the letter predetermined. In addition, AI can be explored in order to solve various problems. In this paper, we tackle the problem of resource allocation in a vehicular small cell network (VSCN). Indeed, we propose a new mechanism based on deep reinforcement learning denoted Resource Allocation based Deep Reinforcement Learning (RA-DRL). The main goal of our proposed method is to maximize the total system sum rate (throughput) while guaranteeing minimum interferences, Quality of Service (QoS) and the demand for all users. Simulation results demonstrate that our proposed RA-DRL algorithm exhibits better performance comparing to the other methods, by maximizing the total system sum rate while maintaining inter-VSCs interferences and a minimum latency

Deep Reinforcement Learning for Downlink Resource Allocation in Vehicular Small Cell Networks

The Internet of Vehicles (IoV) has been recognized as a powerful application of the Internet of Things (IoT) in the Intelligent Transportation System (ITS), providing intelligence for interconnection between devices, interaction with the environment, and thus, greater efficiency in sensor data exploitation. Therefore, leveraging the huge capability of sensors embedded in vehicles to offer a Sensing As A Service (Se-aaS) represents a great solution to exploit under-used sensor resources and continue providing sensors despite their positions and mobility patterns.
Nevertheless, the high network mobility and the fast topology changes in IoV impact the vehicle availability and complicate the service provision. To this aim, we propose a vehicle sensor virtualization in a Cloud IoV architecture that encompasses functional blocks of mobile sensor suppliers, Sensor Cloud Service Provider (SCSP), and service consumers. Moreover, we propose a reinforcement learning-based solution for vehicle sensor selection to predict and dynamically select the physical sensors composing the vehicle virtual sensor. The conducted simulations show the effectiveness of the proposed solution.

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Energy-Aware Routing in SDN Enabled Data Center Network

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