The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.

The exponential rise of intelligent Internet of Things (IoT) devices and the development of Cyber-Physical Systems (CPS) pose new challenges and requirements for modern applications. These include the need for seamless interconnectivity and interoperable interaction between various physical and virtual elements. The enrichment and transformation of IoT technologies to support such interactions is undergoing, considering the need for convergence with edge and cloud computing technologies and the management of IoT applications across resources in the computing continuum. This broader sense of connectivity is tightly connected with the development of Digital Twins (DT), which take advantage of the development of virtual counterparts of IoT devices and CPS. Novel architectural approaches are required to manage complex DTs’ topologies, collectively forming a Digital Twin Network (DTN) that acts as a middleware to provide advanced communication, efficient orchestration, and autonomous decision-making capabilities. This manuscript presents an architectural approach and a relevant open-source software stack implementation -called VOStack- for developing DTs. VOStack is open and modular by design, while it tackles IoT interoperability and convergence challenges with edge and cloud computing technologies. VOStack is thoroughly evaluated under various deployment schemas, virtualization techniques, and based on the provision of an IoT application in the context of a smart city scenario, demonstrating efficient utilization of resources and high efficiency of Machine Learning (ML)-driven orchestration mechanisms.

The evolution toward sixth-generation (6G) networks necessitates integrated resource management solutions to address the interdependencies between network segments, such as Radio Access Network (RAN) and Edge Cloud (EC) infrastructures. Unified management of network and compute fabrics is crucial for achieving seamless service delivery, end-to-end power efficiency, and delay guarantees, while resiliency becomes a key enabler for adapting to various application demands and diverse network segment conditions. In this context, this paper proposes a unified framework for dependable wireless EC networks that jointly addresses the problems of RAN selection and Service Function Chain (SFC) embedding to minimize the total power consumption across network segments under end-to-end delay SFC deployment constraints. The framework iteratively solves these problems, considering the interdependencies between RAN ingress points and the EC network resource constraints. To deal with the high dimensionality of the considered parameters and achieve timely and scalable decision-making, a coalition formation game optimizes RAN selection, while a delay-aware heuristic approach undertakes the power-efficient embedding of multiple SFCs within the EC network. Simulation results demonstrate the framework’s efficiency in reducing power consumption compared to segment-specific approaches, highlighting the importance of cross-segment dependencies. Also, the adaptability of the proposed unified modeling and the framework’s scalability are demonstrated, ensuring resilient performance under varying network parameter settings.

The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.

Maximizing the performance of modern applications requires timely resource management of the virtualized resources. However, proactively deploying resources for meeting specific application requirements subject to a dynamic workload profile of incoming requests is extremely challenging. To this end, the fundamental problems of task scheduling and resource autoscaling must be jointly addressed. This paper presents a scalable architecture compatible with the decentralized nature of Kubernetes [1], to solve both. Exploiting the stability guarantees of a novel AIMD-like task scheduling solution, we dynamically redirect the incoming requests towards the containerized application. To cope with dynamic workloads, a prediction mechanism allows us to estimate the number of incoming requests. Additionally, a Machine Learning-based (ML) Application Profiling Modeling is introduced to address the scaling, by co-designing the theoretically-computed service rates obtained from the AIMD algorithm with the current performance metrics. The proposed solution is compared with the state-of-the-art autoscaling techniques under a realistic dataset in a small edge infrastructure and the trade-off between resource utilization and QoS violations are analyzed. Our solution provides better resource utilization by reducing CPU cores by 8% with only an acceptable increase in QoS violations.

Autoscaling resources in a power-efficient way is essential to enable Green Computing resource management solutions. The development of dynamic resource provisioning techniques could lead to the minimization of power consumption and simultaneously guarantee high quality of service (QoS) inline with the workload demand. In this work, we introduce AHP4HPA, an autoscaling framework for Kubernetes Clusters, which is aligned with the Kubernetes architecture and state-of-the-art practices. We define resource profiles, namely a mapping between the QoS and the computing resources, to maximize the performance. Furthermore, Analytic Hierarchy Process (AHP) is exploited to dictate the scaling decision of the resources under various Key Performance Indicators (KPIs) toward power optimization of the allocated resources. To guarantee maximum performance of the deployed image classification application, an ARIMA model is dedicated to providing predictions regarding the incoming workload traffic. The framework is evaluated against a realistic dataset in a small-scale testbed. Numerical results indicate at least a 9% reduction of the average energy consumption when compared to other state of the art techniques.

Network slicing has been at the forefront of 5G network research, with various slicing orchestration architectures seeking to reap the benefits of slicing for the enhanced performance and reliability of 5G (and beyond) network services. In this context, cross-slice communication (CSC) has drawn significant attention, since CSC can foster interactions among services deployed in co-located slices, lowering the barrier for the consumption of services.To capitalize the benefits of CSC (e.g., reduced latency and cost), CSC should be established with the highest degree of co-location and also in a secure and policy-compliant manner. To this end, we present an orchestration framework that fulfills all main technical requirements for CSC instantiation. In this respect, we elaborate on the CSC instantiation workflows and shed light into the cross-layer interactions that span our proposed CSC orchestrator, the Network Function Virtualization Orchestrator (NFVO) and the Virtualized Infrastructure Manager (VIM). Our experimental results indicate that our proposed CSC orchestration framework introduces a negligible performance overhead and also incurs a minimal latency inflation compared to a direct form of inter-slice communication without any provision for security and resource isolation.

In the era of 5G networks, the demand for high quality service provisioning is growing extremely fast. The enabling of Network Function Virtualization and Network Slicing in the scope of 5G network aims to meet the strict requirements of various business cases. Alongside, the complexity of deployment such services becomes also higher, regarding the differences between infrastructure capabilities and the plethora of various individual requirements. This fact makes the selection of the appropriate infrastructure for slice deployment a complex, but also, a major process, as the optimization of the selection leads to the satisfaction of the user and the better resource allocation from the provider's perspective. In this work, an Edge PoP Selection framework for network slice deployment is proposed. This framework takes into account the user's hard and soft requirements and performs a two-stage selection. The selection of the appropriate infrastructure is based on a multi-criteria decision making method. The proposed framework is evaluated and compared with simple filtering and single-objective selection approaches. The promising results show the importance of the two stage framework in order to simultaneously meet the user's requirements and the optimal utilization of the resources.

The evolution of Internet of Things (IoT) has sparked significant research interest in edge computing. Within this scope and given the ever-increasing number of IoT and mobile devices, computation offloading is emerging as a cutting-edge and significant research area with enormous potential and practical applications.In this respect, we present the architecture design and experimental evaluation of an orchestration framework for smart computation offloading from IoT or mobile devices to edge cloud servers. The proposed orchestration platform, namely COSMOS, includes control-plane components for workload prediction, load balancing, and admission control. COSMOS is particularly tailored to the needs of an object identification service that receives images from a multitude of Points of Interest (PoIs), performs object identification using a trained model (based on Tensorflow), calculates the prediction accuracy, and finally returns to the end-users the identification outcome and accuracy along with useful information about the identified object. COSMOS has been deployed and evaluated in a large-scale experimental facility that employs OpenStack and OpenSourceMANO (OSM) for Network Function Virtualization (NFV) orchestration. Our experimental results indicate the feasibility of computation offloading for this object identification service and further uncover useful insights in terms of performance and scalability.