United States

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.

IEEE NCA 2022

Bi-objective optimization of availability and cost for cloud services

bi-objective optimization

observability

availability modeling

self-adaptive systems

cloud computing

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)


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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.

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.

Reinforcement Learning-based Virtual Sensor Provision in Internet of Vehicles (IoV)

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks. 

Data driven method for In band Network Telemetry Monitoring of Aggregated Traffic

Industrial Internet-of-Things (IoT) massively deploys intelligent computing in industrial production and manufacturing environments seeking automation, reliability, and control. Machine Learning models provide intelligent decisions to drive manufacturing systems to the next level of productivity, efficiency, and safety. One of the critical challenges that must be faced is the deployment of Machine Learning models at the network edge to detect data anomalies caused by Industrial IoT hardware failures, since industrial IoT devices are prone to errors and failures. These anomalies can harm the industrial IoT system by producing false alarms, consuming network resources, and affecting productivity. Because of that, it is critical to rely on low latency and high precision detection systems to verify the data received from industrial IoT devices. In light of this, we assessed key performance indicators of five machine learning models running at edge computing, to provide in-depth discussions. The performance results were obtained from an oil refinery scenario using a real industrial IoT dataset. The performance was measured in terms of (a) Accuracy, (b) Precision, (c) Recall, (d) F1 score, (e) Training time, and (f) Response time.

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Bi-objective optimization of availability and cost for cloud services

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

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