Orchestration Framework Research Articles

Cloud-Native Orchestration Framework for Network Slice Federation Across Administrative Domains in 5G/6G Mobile Networks

Cloud-Native Orchestration Framework for Network Slice Federation Across Administrative Domains in 5G/6G Mobile Networks

An ecosystem orchestration framework for the design of digital product passports in a circular economy

AbstractDigital product passports are considered key levers for a circular economy. By encouraging intraorganizational and interorganizational exchange of data and information, they enable informed life cycle decision‐making to circulate products and materials at their highest utility. This is contingent on the engagement of supply chain actors to contextualize the industrial implications in the span between regulatory compliance and additional value‐generation. Through the lens of ecosystem orchestration, this multiple case study draws upon industrial insights from three product manufacturers as well as a sample of their respective suppliers, service partners, customers, and third‐party recycling companies to identify 16 practices and five orchestration mechanisms. These unfold at different stages of ecosystem maturity. Combined, they construct a framework for the adaptation of digital product passports in industrial ecosystems. This provides guidance for practitioners throughout the contextualization and utilization of digital product passports and extends the scientific debate in the direction of operationalization. As digital product passports are still in their infancy, avenues for further research are identified, particularly concerning the operational foundations, including the adherent driving and inhibiting factors toward the implementation of digital product passports.

Orchestrating the ecosystem for data‐driven digital services and solutions: A multi‐level framework for the realization of sustainable industry

AbstractThe purpose of this study is to investigate how large manufacturing firms can orchestrate their ecosystem for the successful co‐creation of data‐driven digital services and solutions as a way to achieve sustainable industry benefits. An exploratory single‐case study approach has been adopted for this study, which included 23 in‐depth interviews conducted with informants from seven Swedish and international companies involved in a transportation ecosystem. Based on the analysis, this study has developed a multi‐level framework for ecosystem orchestration that contains three main operational levels: individual, organization, and ecosystem, with an integrated sustainable industry benefits section as the outcome of this process. By providing a detailed account of ecosystem orchestration, this study contributes to the successful co‐creation of digital services and solutions. In addition, insights from this research can be used as a continuous evaluation and improvement tool for managers to orchestrate their ecosystem for digital servitization.

Improving the interoperability of a Function‐as‐a‐Service platform using an orchestration framework with a cloud‐agnostic approach

AbstractFunction‐as‐a‐Service (FaaS) applications suffer from cold‐start latency owing to their high execution time and memory usage. We propose a FaaS orchestration framework based on a cloud‐agnostic approach for application and data interoperability. The framework's performance is evaluated on the web application of a student attendance system for online learning. The test system comprises 300 records from 50 students across five courses. Through optional file elimination, entry recognition, optional function generation, and function‐level rewriting, the proposed orchestration framework suitably manages the storage of the student attendance records. The framework notably contributes to the student attendance web application by ensuring scalable, reliable, and efficient operations. Various evaluation measures confirm the effectiveness of the orchestration framework. These measures include cold/warm‐start execution time, cold/warm‐start memory usage, and zip execution time. An empirical analysis reveals that our framework reduces the execution time by 2 s –15 s and memory usage by 1MB–3 MB for the evaluated application.

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$54\%$</tex-math></inline-formula> lower than a centralized framework based on deep-Q networks.

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.

Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing

The complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations.

Research on optimization strategy of container orchestration technology for cloud computing environment

Abstract Containers are becoming the new standard of technology in the cloud era, and a huge number of services are shifting from the traditional virtual machine-based monolithic architecture to the container-based cloud-native architecture. This paper explores the optimization of container orchestration technology in the cloud computing environment, mainly focusing on the optimization study of Kubernetes, a cloud resource orchestration tool for Docker containers. The problems of this container cloud platform were analyzed, and it was found that the orchestration strategy adopted by Kubernetes is a static orchestration strategy, which can easily cause redundancy of container resources. This paper suggests an optimization strategy that first analyzes the PSUB container resource allocation strategy and then presents a new container orchestration optimization strategy based on the PSUB container resource allocation strategy. This paper proposes a PBFDR strategy that utilizes CPU, memory, network bandwidth, and disk IO in a more reasonable and balanced manner by comparing and analyzing the optimization strategies of different container orchestration technologies. Introducing the PBFDR strategy into the Kubernetes container orchestration framework and comparing and analyzing it with the mainstream container orchestration frameworks Rancher and Docker Swarm, the balanced point of resource utilization of KLLPL is around 23%, and the load balancing degree in different deployment sizes is around 13.7%. The evidence indicates that container orchestration technology that is based on PBFDR policy can effectively enhance the utilization of cluster resources.

Integrating Smart Computility for Subflow Orchestration in Remote Virtual Services.

The burgeoning domain of the metaverse has sparked significant interest from a diverse array of industries, including healthcare services. However, the metaverse and its associated applications present various challenges. This could strain the comprehensive capacity of existing networks. In this paper, we have investigated vital network demands of healthcare services within the metaverse. First, to meet the increasing demands of the metaverse, there is a need for enhanced bandwidth, reduced latency, and improved packet loss control. Furthermore, the transmission mechanism should exhibit flexibility to automatically adapt to the diverse hybrid needs of different healthcare services. Considering the aforementioned challenges, a transmission paradigm tailored for the metaverse-based healthcare services is developed. Multipath transmission has the potential to effectively enhance network performance in multiple aspects. Significantly, we devise an orchestration framework to reconcile edge-side subflow management with diverse healthcare applications. Using machine learning techniques, the framework can produce near-optimal subflow adjustment strategies for client nodes and miscellaneous services. Comprehensive experiments are performed on applications with diverse requirements to validate the adaptability of the framework to the application needs. The experimental results demonstrate that the proposed method enables the network to autonomously adapt to changing network conditions and service requirements. This includes applications' preferences for high throughput, low delay, and high stability. Moreover, the test results show that the proposed approach can notably decrease the occurrences of network quality falling below the minimum requirement. Given its adaptability and impact on network quality, this work paves the way for future metaverse-based healthcare services.

DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice‐based system

SummaryRecently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud‐native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube‐scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube‐scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube‐scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average.

Cloud native Lightweight Slice Orchestration (CLiSO) framework

Cloud-native network functions are becoming promising for 5G and beyond networks. They provide the much needed agility and flexibility that was missing from virtual machines. Though, this paradigm shift of using cloud-native application design principles, containerization, microservices, high resilience, and on-demand scaling has created challenges for legacy orchestration systems. They were designed for handling virtual machine-based network functions. Indeed, network slice orchestration requires interaction with multiple technological domain orchestrators, access, transport, core network, and edge computing. The specifications and existing orchestrators are made on top of the legacy virtual machine based network function orchestration. Hence, this limitation constrains their approach to managing a cloud-native network function. To overcome their challenges, we propose a novel Cloud-native Lightweight Slice Orchestration (CLiSO) framework extending our previously proposed Lightweight edge Slice Orchestration (LeSO) framework. In addition, we present a technology-agnostic and deployment-oriented network slice template. To allow zero-touch management of network slices, our framework provides a concept of Domain Specific Handlers. The framework has been thoroughly evaluated via orchestrating OpenAirInterface container network functions on public and private cloud platforms.

Orchestrating resources with suppliers for product innovation

AbstractHow orchestrating external, supplier‐provided resources affects product innovation is an important question. While product innovation is essential to achieve a competitive advantage, it is costly as it requires significant investments. It thus puts a severe strain on firm resources, which is particularly critical for resource‐scarce small–medium enterprises (SMEs). Therefore, these firms must combine their own resources with those of external parties, particularly suppliers, to enhance their product innovation. However, knowledge about how orchestrating these resources affects product innovation is limited, because empirical studies on resource orchestration have largely focused on firm‐internal resources; furthermore, there is ambiguity regarding the extent to which drawing on external resources is beneficial. In addition, the conditions that affect the resource orchestration–product innovation relationship remain unexplored. Therefore, we focus on supplier logistics integration (SLI), an important resource orchestration action referring to the orchestration of external, supplier‐provided resources; we draw on the resource orchestration framework and the related work on organizational rigidity to theorize that there is an inverted U‐shaped relationship between SLI and product innovation. Additionally, we suggest that learning orientation and environmental dynamism mitigate rigidities associated with high levels of SLI and thus facilitate the translation of SLI into product innovation. Testing our hypotheses with data from Swedish SMEs supports our theorizing and provides important contributions. Most importantly, we extend the resource orchestration framework to include the focus on external, supplier‐provided resources, thereby advancing our knowledge and understanding of resource orchestration for product innovation in SMEs.

VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management

Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.

A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks.

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications.

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control

An Orchestration Framework for Digital Innovation: Lessons From the Healthcare Industry

An Orchestration Framework for Digital Innovation: Lessons From the Healthcare Industry

Microservices Container Security Orchestration Framework within Kubernetes and Docker for Business-Critical Applications within Digital Transformation

Container virtualization technology facilitates the creation of microservices-based systems through continuous integration. Container-based apps can be deployed more easily when they use orchestration systems like Kubernetes, which has become the de facto standard. It can be difficult to create effective and precise orchestration systems, nevertheless. The scheduler, a crucial orchestrator task that allocates physical resources to containers, is the subject of this article. Scheduling strategies are developed using several Quality-of-Service metrics. The CI in CI/CD stands for continuous integration. Continuous integration drives the automation in the development and delivery of the code and developers frequently apply code changes. It’s an automated process that allows multiple developers to contribute software components to the same project without integration conflicts. CI also triggers the process of testing the applications automatically upon code commit into the repository. Container virtualization technology facilitates the creation of microservices-based systems through continuous integration. Container-based apps can be deployed more easily when they use orchestration systems like Kubernetes, which has become the de facto standard. It can be difficult to create effective and precise orchestration systems, nevertheless. The scheduler, a crucial orchestrator task that allocates physical resources to containers, is the subject of this article. Scheduling strategies are developed using several Quality of Service metrics.

SAP: Subchain-Aware NFV Service Placement in Mobile Edge Cloud

Existing Network Function Virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks; APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning

Network slicing can provide logically isolated networks on the shared network infrastructure by invoking multiple technologies and administrative domains to fulfill end-to-end (E2E) service level agreements (SLAs). To guarantee the E2E service communication quality in the sliced network, an SLA-based cross-domain orchestration framework is proposed in this paper. The framework includes an E2E cross-domain coordination orchestrator at the upper layer and multiple subordinate domain controllers. Furthermore, we design two latency equalization policies applied to the upper layer orchestrator to divide the latency budget for each lower layer domain. Based on the reinforcement learning approach, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network SFC Mapping (PN-SFC), intra-domain resource allocation/mapping algorithms are designed independently for the lower radio access network (RAN) and core network (CN) domain controllers, respectively. The above algorithms are used to jointly optimize the enhanced mobile broadband (eMBB) users service satisfaction level and maximize the number of E2E accessed users. Simulation results show that our proposed algorithm can effectively guarantee the eMBB users QoS and improve the network capacity.

컨테이너 기반 마이크로서비스의 이력 데이터 수집 기법

As the provision of cloud-based services becomes smoother, cloud-native development methodology are on the rise. The microservice architecture can be used the cloud-native methodology. Microservice architecture is a scaled-down size of a single function or small service unit and a function execution flow is formed through loosely coupling between microservices. When adopting a microservice architecture, it is necessary to trace the execution flow and collect historical data in order to configure a smooth function flow between microservices. In the current microservice architecture, however, there is a lack of research on historical data elements or execution flow tracking for microservice execution. Therefore, a historical data collection method based on the execution architecture and flow of microservice based on container are proposed in this paper. For the construction of the method, the architecture and technique for collecting historical data were presented in units of microservice, container, and container orchestration framework. In addition, in order to show that historical data collection is possible based on the proposed method, a case study was conducted and simulated based on the actual environment. The poposed method is expected to be used as a basis for research on service management and monitoring based on microservice architecture.