Orchestration Solutions Research Articles

Optimal channel selection for tri-band Wi-Fi in a residential scenario

The growing use of Internet of Things devices and the increasing demand for high-speed, reliable, and secure wireless connectivity pose significant challenges for existing wireless networking solutions in modern smart homes. As such, there is an increasing urgency for the development of advanced and effective wireless technologies that can fulfil the new requirements for interconnected devices and services deployed in households. Tri-band Wi-Fi 6E equipment addresses these needs by reducing the network congestion and enhancing the performance across the 2.4 GHz, 5 GHz, and 6 GHz bands. However, improper band management can lead to frequent interference and network issues. As such, this work introduces a dynamic Wi-Fi link orchestration solution that follows a heuristic model. This approach aims to optimize the network layout and channel allocation based on device metrics, utilizing the EasyMesh specification for simplified network setup and management. The model was implemented and tested in a residential environment network, using EasyMesh and tri-band Wi-Fi 6E devices. Results show the effectiveness of the model in improving network capacity and adapting the links to the current traffic, outperforming the initial and base network configuration.

Collaborative Orchestration of Multi-Domain Edges From a Connected, Cooperative and Automated Mobility (CCAM) Perspective

The 5G ecosystem is comprised of the cellular 5G System, as well as a managed and orchestrated infrastructure providing virtualized network and service functions. The automotive industry with its stringent requirements for connected vehicles is a promising and yet challenging consumer of such 5G ecosystem. Deployment of service instances at distributed cloud resources of cellular network infrastructure edges enables localized low-latency access to these services from moving vehicles but comes along with challenges, such as the need for fast reconfiguration of the distributed deployment according to mobility pattern and associated service and resource demand. In this paper, we investigate a solution for the collaborative orchestration of services for Connected, Cooperative and Automated Mobility (CCAM) within such 5G ecosystem. A key objective is the service continuity for a highly dynamic automotive scenario, achieved by the associated management and orchestration of these services in distributed edge clouds. The proposed solution leverages a multi-tier orchestration system as well as localized management and protocol operations for collaborative edge resources. By means of both analytical and experimental evaluations, the paper draws conclusions on the gain in accelerating orchestration decisions and enforcements, while balancing associated protocol and computational load over the highly distributed and multi-layered orchestration system.

The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.

Online Microservice Orchestration for IoT via Multiobjective Deep Reinforcement Learning

By providing loosely coupled, lightweight, and independent services, the microservice architecture is promising for large-scale and complex service provision requirements in the Internet of Things (IoT). However, it requires more fine-grained resource management and orchestration for service provision. Most of the existing microservice orchestration solutions are based on those designed for the traditional cloud. They can only provide coarse-grained resource allocation using possibly conflicting weighted objectives. In this article, we present a fine-grained microservice orchestration approach to provide services online for dynamic requests of IoT applications. By using a fine-grained resource model of energy cost and service end-to-end response time of orchestrated microservices, we formulate the microservice orchestration problem as a multiobjective Markov decision process. We then propose a multiobjective optimization solution based on deep reinforcement learning (DRL) to simultaneously reduce energy consumption and response time. Through extensive experiments, our proposed algorithm presents significant performance results than the state of the art. To the best of our knowledge, this is the first work that addresses microservice orchestration using DRL for multiple conflicting objectives.

An edge abstraction layer enabling federated and hierarchical orchestration of CCAM services in 5G and beyond networks

This paper shows a flexible orchestration solution for deploying Cooperative, Connected, and Automated Mobility (CCAM) services in 5G and beyond networks. This solution is based on the concepts of federation and hierarchy of orchestration functions. The federated approach is leveraged to cope with the differentiated complexity operation when multiple network operators are considered, whereas the hierarchical approach addresses the issue of jointly orchestrating multiple edge platforms in the network of a single operator. In this complex orchestration architecture, the main contribution of this paper consists of the design and implementation of an Abstraction and Adaptation Layer (AAL) for edge clouds, a new component enabling a truly cooperative and coordinated orchestration between different edge systems, characterized by appreciable experimental performance in terms of latency.

Resource orchestration in 5G and beyond: Challenges and opportunities

5G networks have strict constraints regarding the services in terms of latency, reliability, and availability, which pose additional challenges to the traditional orchestration solutions, and 6G networks will increment the number of slices and services deployed over different technological domains, adding more difficulties for the orchestrator. Distributed and automated solutions will be essential for this context. This article identifies the main challenges in 5G/6G orchestration and then describes the utility of Artificial Intelligence-driven solutions, outlining an orchestrator architecture for 5G networks. The architecture is then explored as an orchestration solution for two ongoing research projects focused on the deployment of critical services over 5G networks.

Towards a Low-Cost Teacher Orchestration Using Ubiquitous Computing Devices for Detecting Student’s Engagement

The ubiquitous devices and technologies to support teachers and students in a learning environment include the Internet of things (IoT), learning analytics (LA), augmented or virtual reality (AR/VR), ubiquitous learning environment (ULE), and wearables. However, most of these solutions are obtrusive, with substantial infrastructure costs and pseudo-real-time results. Real-time detection of students’ activeness, participation, and activity monitoring is important, especially during a pandemic. This research study provides a low-cost teacher orchestration solution with real-time results using off-the-shelf devices. The proposed solution determines a teacher’s activeness using multimodal data (MMD) from both teacher and student’s devices. The MMD extracts different features from data, decodes them, and displays them to the instructor in real time. It allows the instructor to update their teaching methodology in real time to get more students on board and provide a more engaging learning experience. Our experimental results show that real-time feedback about the classroom’s current status helped improve learning outcomes by about 45%. Also, we investigated a 50% increase in classroom engaging experience.

An experimental study on latency-aware and self-adaptive service chaining orchestration in distributed NFV and SDN infrastructures

Network Function Virtualization (NFV) and Software Defined Networking (SDN) changed radically the way 5G networks will be deployed and services will be delivered to vertical applications (i.e., through dynamic chaining of virtualized functions deployed in distributed clouds to best address latency requirements). In this work, we present a service chaining orchestration system, namely LASH-5G, running on top of an experimental set-up that reproduces a typical 5G network deployment with virtualized functions in geographically distributed edge clouds. LASH-5G is built upon a joint integration effort among different orchestration solutions and cloud deployments and aims at providing latency-aware, adaptive and reliable service chaining orchestration across clouds and network resource domains interconnected through SDN. In this paper, we provide details on how this orchestration system has been deployed and it is operated on top of the experimentation infrastructure provided within the Fed4FIRE+ facility and we present performance results assessing the effectiveness of the proposed orchestration approach.

Slicing-Based Reliable Resource Orchestration for Secure Software-Defined Edge-Cloud Computing Systems

The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G and beyond. However, edge-cloud computing systems are composed of various hardware facilities, leading to difficulties in hardware control and management. With network slicing, underlying resource sharing among multiple slice users is allowed, leading to potential attacks to the slice formulation processes and malicious usage of network slices that may result in inefficient resource utilization of the system. To address the aforementioned network slice security issue, we first propose a new systematic framework, named software-defined edge-cloud computing (SD-ECC), which applies standard software to control the hardware infrastructure regardless of vendor variations. With SD-ECC, resource slices are formulated by including storage and computational resources provided by edge and cloud servers. Then, we study an optimal slicing-based resource orchestration problem by considering slice-initiated attacks as possible adversaries, which includes both interslice and intraslice resource orchestrations. A secure slicing-based resource orchestration (SS-RO) algorithm is designed by minimizing the delay and resource utilization simultaneously to mitigate the impacts of the slice-initiated attacks, where the Benders decomposition is employed to obtain the interslice orchestration outcome, and a quadratic transformation method is applied to derive the intraslice orchestration solution. The experimental results demonstrate that the proposed SS-RO algorithm outperforms baseline schemes in terms of the ratio of accepted attacking tasks, energy consumption, and system throughput.

Cost and Latency Optimized Edge Computing Platform

Latency-critical applications, e.g., automated and assisted driving services, can now be deployed in fog or edge computing environments, offloading energy-consuming tasks from end devices. Besides the proximity, though, the edge computing platform must provide the necessary operation techniques in order to avoid added delays by all means. In this paper, we propose an integrated edge platform that comprises orchestration methods with such objectives, in terms of handling the deployment of both functions and data. We show how the integration of the function orchestration solution with the adaptive data placement of a distributed key–value store can lead to decreased end-to-end latency even when the mobility of end devices creates a dynamic set of requirements. Along with the necessary monitoring features, the proposed edge platform is capable of serving the nomad users of novel applications with low latency requirements. We showcase this capability in several scenarios, in which we articulate the end-to-end latency performance of our platform by comparing delay measurements with the benchmark of a Redis-based setup lacking the adaptive nature of data orchestration. Our results prove that the stringent delay requisites necessitate the close integration that we present in this paper: functions and data must be orchestrated in sync in order to fully exploit the potential that the proximity of edge resources enables.

Orchestrating innovation networks: Alignment and orchestration profile approach

This conceptual paper organizes and advances existing knowledge on the management practices of inter-organizational innovation networks by developing an orchestration profile approach. We utilize the principle of alignment to connect orchestration practices with the management requirements of different types of innovation networks. We explain that while goals and underlying value-creation logics differ in different types of innovation networks and, consequently, practices vary from network to network, generally applicable dimensions of orchestration can be identified that provide a useful analytical tool. By addressing the network types and orchestration dimensions simultaneously, we propose three innovation-network orchestration profiles, which we label translative, transformative, and transcending. These profiles are suggested to be generic and, as such, provide managerial heuristics for creating effective orchestration solutions for various innovation networks.

Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.

On QoE-Oriented Cloud Service Orchestration for Application Providers

New virtualization technologies allow Infrastructure Providers (InPs) to lease their resources to Application Service Providers (ASPs) for highly scalable delivery of cloud services to end-users. However, existing literature lacks knowledge on Quality of Experience (QoE)-oriented cloud service orchestration algorithms that can guide ASPs on how to plan their budget to enhance satisfactory QoE delivery to end-users. In contrast to the InP's cloud service orchestration, the ASP's orchestration should not rely on expensive infrastructure control mechanisms such as Software-Defined Networking (SDN), or require aprioriknowledge on the number of services to be instantiated and their anticipated placement location within InP's infrastructure. In this paper, we address this issue of delivering satisfactory user QoE by synergistically optimizing both ASP's management and data planes. The optimization within the ASP management planefirst maximizes Service Level Objective (SLO) coverage of users when application services are being deployed, and are not yet operational. The optimization of the ASP data plane then enhances satisfactory user QoE delivery when applications services are operational with real user access. Our evaluation of QoE-oriented algorithms using realistic numerical simulations, real-world cloud testbed experiments with actual users and ASP case studies show notably improved performance over existing cloud service orchestration solutions.

ITRIX - an AI Enabled Solution for Orchestration of Recovery Instructions

This paper presents ITRIX (IT Recovery Instructions eXtraction)- a solution that automates the complex manual process of creating executable recovery plans from textual documents describing IT environment disaster recovery procedure. The solution applies natural language processing and deep learning to identify textual content that contain recovery instructions and information relevant to disaster recovery. This enables rapid creation and review of recovery plans resulting in 80% reduction in time taken to generate them.

Toward a Real Deployment of Network Services Orchestration and Configuration Convergence Framework for 5G Network Slices

A seamless interworking between network function virtualization (NFV) and software defined networking (SDN) to orchestrate network services for the 5G systems is very fundamental for network slice creation. The orchestration of large scale network slices across multiple administrative as well as technological domains with heterogeneous resources and a distributed form of slice management can benefit from harnessing existing NFV orchestration (NFVO) solutions. In this regard, this article presents a network service orchestration and configuration convergence framework that is capable of providing a large scale network slicing solution for 5G network operators. Using this framework, 5G network operators can orchestrate and configure network slices directly from their infrastructure and that of credible registered slice providers who have resources for the orchestration of only a subset of the overall network slice. The framework is equipped with mechanisms that allow a distributed form of slice configuration and management.

Service Placement for Latency Reduction in the Fog Using Application Profiles

The Cloud-Fog-Internet of Things continuum combines different paradigms to provide connectivity and ubiquity for end-users, while also granting low latency and low jitter to cope with different challenges, including the requirements of latency-sensitive applications, such as virtual/augmented reality and online gaming. This constitutes a complex and dynamic environment with heterogeneous resources that need to be managed or orchestrated, in order to accomplish application requirements for low latency. Common orchestration solutions make placement decisions based only on the resources of the underlying network and the application resource requests; however, using the profiles of applications to make placement decisions has the potential to enhance the final performance perceived by the end-users. This paper proposes the use of application profiles according to their popularity to guide their placement. To corroborate the effectiveness of the use of the profiles, two placement mechanisms are presented, one based on Genetic Algorithm and the other inspired on graph partitions. Simulation results show that it is possible to reduce the latency and jitter of applications via a service placement guided by the profiles. The mechanism based on graph partitions showed better results for all scenarios, followed closely by the Genetic Algorithm in the scenarios with lower load.

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.

Breaking Down Network Slicing: Hierarchical Orchestration of End-to-End Networks

Network slicing is one of the key enabling techniques for 5G, allowing network providers to support services with diverging requirements on top of their physical infrastructure. In this article, we address the limited support and oversimplified resource allocation on different network segments of existing end-to-end (E2E) orchestration solutions. We propose a hierarchical orchestration scheme for E2E networks, breaking down the E2E resource management and network slicing problems per network segment. We introduce a higher-level orchestrator, the hyperstrator, to coordinate the distributed orchestrators and deploy network slices (NSs) across multiple network segments. We developed a prototype implementation of the hyperstrator and validated our hierarchical orchestration concept with two proofof- concept experiments, showing the NS deployment and the impact of the resource allocation per network segment on the performance of NSs. The results show that the distributed nature of our orchestration architecture introduces negligible overhead for provisioning NSs in our particular setting, and confirm the need of a hyperstrator for coordinating network segments and ensuring consistent QoS for NSs.

Benchmarking open source NFV MANO systems: OSM and ONAP

With the increasing trend in softwarization and virtualization of network functions and systems, NFV management and orchestration (MANO) solutions are being developed to meet the agile and flexible management requirements of virtualized network services in the 5G era and beyond. In this regard, ETSI ISG NFV has specified a standard MANO system that is used as a reference by vendors as well as open-source MANO projects. These MANO systems are inherently very complex and have a direct impact on the overall performance of NFV systems. However, unlike traditional networking functions and systems, there are no well-defined test methods and KPIs based on which the performance of the NFV-MANO system can be tested, validated and benchmarked. Given the absence of formal MANO specific evaluation techniques based on which the performance and features of a MANO system can be quantified, and compared against, we introduce in this paper a formal benchmarking methodology and KPIs for MANO systems. For illustration purposes, we analyze and compare the performance of the two most popular open-source NFV MANO projects, namely ONAP and OSM, using a complex open-source virtual customer premises equipment (vCPE) VNF. Our results show the current features support, performance to be expected and gaps to be covered in future releases.

PSMART: A lightweight, privacy-aware service function chain orchestration in multi-domain NFV/SDN

Service Function Chaining (SFC) has become an integral part of every emerging application to satisfy its specific demand. Due to the massive increase in such diverse applications, there is a need for an efficient solution for flexible SFC orchestration. In this context, deploying SFC across domains can be a promising way to provide the required network services through cost-effective resource utilization. However, the operation of Multi-Domain SFC (MD-SFC) mainly has a trade-off between privacy and performance. While sharing the detailed network information, such as topology and resource availability, is important for the efficient MD-SFC orchestration, the network operators are not willing to disclose such private and detailed network information about the domain network. As a result, there is always a higher demand for privacy preservation while employing efficient MD-SFC orchestration. In this paper, we present pSMART, a lightweight, privacy-aware service function chain orchestration in a multi-domain NFV/SDN scenario. The main purpose of pSMART is to utilize less sensitive information, to reduce privacy and security risks, and to employ learning based decision making for efficient MD-SFC mapping. We quantify and analyse the gain of the proposed pSMART approach in terms of privacy-preservation and response time. The simulation results show that the proposed pSMART approach achieves a higher level of privacy and optimizes the response time of MD-SFC orchestration.