Latency-aware Radio Resource Allocation over Cloud RAN for Industry 4.0

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

Demonstration: A cloud-native digital twin with adaptive cloud-based control and intrusion detection

Digital twins are taking a central role in the industry 4.0 narrative. How-ever, they are still illusive. Many aspects of the digital-twins have yet to materialize.For example, to what degree will they be integrated into cloud and industry 4.0 sys-tems as well as how and if they should augment their physical counterpart. Thosechoices are accompanied by challenging security aspects, many of which have to bestudied partially. In this paper, we present a novel digital-twin demonstrator that en-ables experimentation and advanced research on such systems. The demonstrator iscloud-native, has a distributed adaptive control system, incorporates edge and publicclouds, a PLC, intrusion detection, a wireless network emulator, and an attacker

Massive MIMO Pilot Scheduling over Cloud RAN

Cloud-RAN (C-RAN) is a promising paradigm for the next generation radio access network infrastructure, which offers centralized and coordinated base-band signal processing. On the other hand, this requires extremely low latency fronthaul links to achieve real-time centralized signal processing. In this paper, we investigate massive MIMO pilot scheduling in a C- RAN infrastructure. Three commonly used scheduling policies are investigated with simulations in order to provide insight on how the scheduling performance is affected by the latency incurred by the C-RAN infrastructure

Demonstration: A cloud-control system equipped with intrusion detection and mitigation

The cloud control systems (CCs) are inseparable parts of industry 4.0. The cloud, by providing storage and computing resources, allows the controllers to evaluate complex problems that are too computationally demanding to perform locally. However, connecting physical systems to the cloud through the network can provide an entry point for attackers to infiltrate the system and cause damage with potentially catastrophic consequences. Hence, in this paper, we present a demo of our proposed security framework for CCs and demonstrate how it can detect attacks on this system quickly and mitigate them

Demonstration: A cloud-control system equipped with intrusion detection and mitigation

The cloud control systems (CCs) are inseparable parts of industry 4.0. The cloud, by providing storage and computing resources, allows the controllers to evaluate complex problems that are too computationally demanding to perform locally. However, connecting physical systems to the cloud through the network can provide an entry point for attackers to infiltrate the system and cause damage with potentially catastrophic consequences. Hence, in this paper, we present a demo of our proposed security framework for CCs and demonstrate how it can detect attacks on this system quickly and mitigate them

Utilizing Massive MIMO for the Tactile Internet: Advantages and Trade-offs

Controlling robots in real-time over a wireless inter- face present fundamental challenges for forthcoming fifth gen- eration wireless networks. Mission critical real-time applications such as telesurgery over the tactile Internet require a commu- nication link that is both ultra-reliable and low-latency, and that simultaneously serving multiple devices and applications. Wireless performance requirements for these applications surpass the capabilities of current wireless cellular standards. The pre- vailing ambitions for the fifth generation wireless specifications go beyond higher throughput and embrace the wireless performance demands of mission critical real-time applications in robotics and the Internet of Things. To accommodate these demands, changes have to be made across all layers of the wireless infrastructure. The fifth generation wireless standards are far from finalized but massive Multiple-Input Multiple-Output has surfaced as a strong radio access technology candidate and has great potential to cope with all these stringent requirements. In this paper, we investigate how Ultra-Reliable and Low-Latency Communication with massive MIMO can be achieved for bilateral teleoperation, an integral part of the tactile Internet. We conclude through simulation what the performance bounds are for massive MIMO and thus how to configure such a system for near deterministic latency and what the inherit trade-offs are

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology

Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts

Towards Mission-Critical Control at the Edge and Over 5G

With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.Comment: June 18th: Upload the final version as submitted to IEEE Services [EDGE] 2018 on May 16th (updated abstract and some wording, results unchanged

Adaptive and Application-agnostic Caching in Service Meshes for Resilient Cloud Applications

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

Distributed Approach to the Holistic Resource Management of a Mobile Cloud Network

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 re- source 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. Sys- tematic 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 na ̈ıve and random methods