Despite extensive research on Quality-of-Services (QoS) analysis of 5G networks, practical applications remain limited. This gap may be attributed to the lack of available tools and the complexity of developing or configuring technical systems to implement theoretical approaches. To address this, we developed an edge control system within a private 5G base station, utilizing Precision Time Protocol (PTP) to enable accurate Age of Information (AoI) measurements. Through analysis using AoI and Age of Actuation (AoA) metrics, we demonstrate the accomplishment of comparable results in an emulated Local Area Network (LAN), which offers greater accessibility and reduced complexity. We also test the reliability of network emulation application on system performance analysis.

Conventional Quality of Service(QoS) measurements are incapable to meet the demand for analysing communication performance on timeliness of information in Industrial Control Networks with many connected subsystems. The Age of Information (AoI) and the Age of Actuation (AoA) give a promise to be a solution. Many recent research articles cover theoretical aspects and analyse performance characteristics of communication systems in terms of AoI, but practical realisation in industry has not been achieved yet. As a new companion metric to AoI, AoA is evaluated in a real test-bed for the first time in this paper. All analysis is based on measurement in an Edge-Control System implemented as our test-bed. To validate the usefulness of these metrics in the system performance tests on edge-cloud integration, the test-bed separated to field network and edge-to-field network scenarios, where the edge server is located in a 5G base-station. This article also describes how such measurements can be carried out. Practical issues such as time synchronizations between connected devices and how to calculate AoI and AoA based on measurements are handled.

Full-duplex (FD) communication improves spectral efficiency by allowing simultaneous transmission and reception on the same frequency band. This paper analyzes a two-tier network consisting of macrocells and picocells, where base stations (BSs) operate in FD mode while users operate in half-duplex (HD) mode. However, interference remains a major challenge in these systems, significantly impacting coverage probability. To enhance network performance, we incorporate user offloading and resource partitioning. Offloading redistributes users from macrocells to picocells to balance the network load. However, offloaded users often experience lower SINR compared to those connected to macrocells. Resource partitioning is applied to mitigate this SINR degradation and reduce interference. This method allocates a fraction of time or frequency resources where macrocells remain inactive, allowing picocells to serve users with less interference. We derive analytical expressions for the downlink coverage probability, considering the effects of offloading, resource partitioning, and key network parameters. Our results indicate that load balancing by itself is insufficient; however, when resource partitioning is combined with offloading, DL coverage probability is significantly improved.

In the rapidly advancing domain of autonomous vehicle technology, the management of intersection traffic emerges as a demanding challenge. This paper describes the development and implementation of a novel testbed designed for the investigation of an autonomous intersection management system (AIMS). The research primarily focuses on the complex coordination required among autonomous vehicles for efficient operation at urban intersections. At the heart of this system lies the Intersection Coordination Unit (ICU), which centralizes traffic management. Robotic vehicles, equipped with On-Board Units (OBU) and advanced sensors, communicate wirelessly with the ICU. This communication facilitates real-time updates on vehicle positions and movements, enhancing environmental perception and aiding in collision prevention. The paper includes a validation section, demonstrating the testbed’s capabilities and the quality of data it generates.

Mission-critical applications such as industrial control processes are evolving towards a new development paradigm by offloading their heavy computations to the edge of the emerging Fifth Generation Wireless Specifications (5G) network. In this manner, the applications can gain the economical and efficiency benefits of cloud computing, as well as reliable communication from the 5G network. However, the limited access to a configurable infrastructure of the 5G network and its edge computing infrastructure has restrained academic researchers from experimenting and validating their mission-critical application design under reasonable communication and computation scenarios. In this paper, we present a configurable mid-band 5G Stand-Alone (SA) deployment and demonstrate a control process that is running over the edge of the 5G network. We show in this paper a complete system setup for Control over the Edge (CoE) of the 5G network, and validate the feasibility of deploying similar mission-critical applications over the edge of 5G network.

With the help of modern technologies and advances in communication systems, the functionality of Industrial control systems (ICS) has been enhanced leading toward to have more efficient and smarter ICS. However, this makes these systems more and more connected and part of a networked system. This can provide an entry point for attackers to infiltrate the system and cause damage with potentially catastrophic consequences. Therefore, in this paper, we propose a digital twin-based security framework for ICS that consists of two parts: attack detection and attack mitigation. In this framework we deploy an intrusion detection system in digital domain that can detect attacks in a timely manner. Then, using our mitigation method, we keep the system stable with acceptable performance during the attack. Additionally, we implement our framework on a real testbed and evaluate its capability by subjecting it to a set of attacks.

The notion of Cloud RAN is taking a prominent role in narrative for the next generation wireless infrastructure. It is also seen as a mean to industrial communication systems. In order to provide reliable wireless connectivity for industrial deployments, by conventional means, the cloud infrastructure needs to be reliable and incur little latency, which however, is contradictory to the stochastic nature of cloud infrastructures. In this paper, we investigate the impact of stochastic delay on a radio resource allocation process deployed in Cloud RAN. We proceed to propose a strategy for realizing timely cloud responses and then adapt that strategy to a radio resource allocation problem. Further, we evaluate the strategies in an industrial IoT scenario using a simulated environment. Experimentation shows that, with our proposed strategy, a significant performance improvement on timely responses can be achieved even with noisy cloud environment. Improvements in resource utilization can be also attained for a resource allocation process deployed over Cloud RAN with this strategy.

Cloud computing has become a prominent technology for the computing paradigm in various industrial sectors nowadays. For most industrial applications to perform in real-time, the support of periodic computing is required. However, it remains a challenge when the computing is executed in a cloud, since both the network connection and the cloud environment are uncertain. In this paper, we propose a new architecture to deploy real-time applications in the cloud. We call it punctual cloud. We detail the implementation and demonstrate how punctual cloud is deployed in a cloud-native manner on Kubernetes. We evaluate the system’s performance with a real-time resource allocation problem and show that, compared to a system without punctual cloud, which has maximum 40% punctual deliveries, our proposed architecture can attain over 90% responses to be delivered punctually for the application, while also being capable of remedying the performance degradation caused by long and uncertain response delays in the system.

Service meshes factor out code dealing with inter-micro-service communication. The overall resilience of a cloud application is improved if constituent micro-services return stale data, instead of no data at all. This paper proposes and implements application agnostic caching for micro services. While caching is widely employed for serving web service traffic, its usage in inter-micro-service communication is lacking. Micro-services responses are highly dynamic, which requires carefully choosing adaptive time-to-life caching algorithms. Our approach is application agnostic, is cloud native, and supports gRPC. We evaluate our approach and implementation using the micro-service benchmark by Google Cloud called Hipster Shop. Our approach results in caching of about 80% of requests. Results show the feasibility and efficiency of our approach, which encourages implementing caching in service meshes. Additionally, we make the code, experiments, and data publicly available.

Summary This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

Cities are deploying tens of thousands of sensors and actuators and developing a large array of smart services. The smart services use sophisticated models and decision-making policies supported by Cyber Physical Systems and Internet of Things technologies. The increasing number of sensors collects a large amount of city data across multiple domains. The collected data have great potential value, but has not yet been fully exploited. This survey focuses on the domains of transportation, environment, emergency and public safety, energy, and social sensing. This article carefully reviews both the data sets being collected across 14 smart cities and the state-of-the-art work in modeling and decision making methodologies. The article also points out the characteristics, challenges faced today, and those challenges that will be exacerbated in the future. Key data issues addressed include heterogeneity, interdisciplinary, integrity, completeness, real-timeliness, and interdependencies. Key decision making issues include safety and service conflicts, security, uncertainty, humans in the loop, and privacy.

The Mobile Cloud Network is an emerging cost and capacity heterogeneous distributed cloud topological paradigm that aims to remedy the application performance constraints imposed by centralised cloud infrastructures. A centralised cloud infrastructure and the adjoining Telecom network will struggle to accommodate the exploding amount of traffic generated by forthcoming highly interactive applications. Cost effectively managing a Mobile Cloud Network computing infrastructure while meeting individual application's performance goals is non-trivial and is at the core of our contribution. Due to the scale of a Mobile Cloud Network, a centralised approach is infeasible. Therefore, in this paper a distributed algorithm that addresses these challenges is presented. The presented approach works towards meeting individual application's performance objectives, constricting system-wide operational cost, and mitigating resource usage skewness. The presented distributed algorithm does so by iteratively and independently acting on the objectives of each component with a common heuristic objective function. Systematic evaluations reveal that the presented algorithm quickly converges and performs near optimal in terms of system-wide operational cost and application performance, and significantly outperforms similar naïve and random methods.

The spot pricing scheme has been considered to be resource-efficient for providers and cost-effective for consumers in the Cloud market. Nevertheless, unlike the static and straightforward strategies of trading on-demand and reserved Cloud services, the market-driven mechanism for trading spot service would be complicated for both implementation and understanding. The largely invisible market activities and their complex interactions could especially make Cloud consumers hesitate to enter the spot market. To reduce the complexity in understanding the Cloud spot market, we decided to reveal the backend information behind spot price variations. Inspired by the methodology of reverse engineering, we developed a Predator-Prey model that can simulate the interactions between demand and resource based on the visible spot price traces. The simulation results have shown some basic regular patterns of market activities with respect to Amazon's spot instance type m3.large. Although the findings of this study need further validation by using practical data, our work essentially suggests a promising approach (i.e.~using a Predator-Prey model) to investigate spot market activities.

The populations of large cities around the world are growing rapidly. Cities are beginning to address this problem by implementing significant sensing and actuation infrastructure and building services on this infrastructure. However, as the density of sensing and actuation increases and as the complexities of services grow there is an increasing potential for conflicts across Smart City services. These conflicts can cause unsafe situations and disrupt the benefits that the services were originally intended to provide. Although some of the conflicts can be detected and avoided during designing the services, many can still occur unpredictably during runtime. This paper carefully defines and enumerates the main issues regarding the detection and resolution of runtime conflicts in smart cities. In particular, it focuses on conflicts that arise across services. This issue is becoming more and more important as Smart City designs attempt to integrate services from different domains (transportation, energy, public safety, emergency, medical, and many others). Research challenges are identified and then addressed that deal with uncertainty, dynamism, real-time, mobility and spatio-temporal availability, duration and scale of effect, efficiency, and ownership. A watchdog architecture is also described that oversees the services operating in a Smart City. This watchdog solution detects and resolves conflicts, it learns and adapts, and it provides additional inputs to decision making aspects of services. Using data from a Smart City dataset, an emulated set of services and activities using those services are created to perform a conflict analysis. A second analysis hypothesizes 41 future services across 5 domains. Both of these evaluations demonstrate the high probability of conflicts in smart cities of the future.

In this paper, we propose a telco cloud meta-model that can be used to simulate different infrastructure configurations and explore their consequences for system performance and costs. To achieve this, we analyse current telecommunication and data centre infrastructure paradigms, describe the architecture of the telco cloud, and detail the benefits of merging both infrastructures in a unified system. Next, we detail the dynamics of the telco cloud and identify the components that are the most relevant from the perspective of modelling performance and cost. As a number of well established simulation technologies exist for most of the telco cloud components, we survey existing models in an attempt to construct a suitable composite meta-model. Finally, we present a showcase scenario to demonstrate the scope of our telco cloud simulator.