Intelligent Orchestration Research Articles

System Center Virtual Machine Manager 2022: Enhanced Cloud Integration and Management Capabilities

System Center Virtual Machine Manager (SCVMM) 2022 represents a transformative advancement in private cloud management, offering enhanced capabilities across virtualization, deployment, and cloud integration domains. This comprehensive platform introduces sophisticated hardware-level security protocols, dynamic resource allocation mechanisms, and intelligent orchestration capabilities that significantly improve operational efficiency. The integration with Azure Arc and support for Azure Kubernetes Service demonstrates SCVMM's evolution in hybrid cloud management, while enhanced data protection solutions ensure robust security and recovery capabilities. The platform's implementation of energy-aware management protocols and low-carbon operational methodologies showcases Microsoft's commitment to environmental sustainability without compromising performance. Through advanced predictive analytics, automated workflow management, and streamlined deployment architectures, SCVMM 2022 addresses the growing demands of modern enterprise environments. This article examines the platform's core enhancements, deployment methodologies, cloud integration capabilities, and impact on enterprise operations while providing implementation best practices derived from extensive research and real-world deployments.

(Invited) Sparking Autonomous Full Cycle Battery Research

Materials optimization typically focuses on fine-tuning selected properties of a material for targeted applications. However, optimizing a single material property or component does not necessarily lead to optimized system performance. This talk demonstrates how to design, run, and debug a Materials Acceleration Platform (MAP) that integrates computational and experimental capabilities from dozenz of institutions across Europe to autonomously optimize both material-level properties, like ionic conductivity of electrolytes, and system-level performance, like the end-of-life (EOL) of lithium-ion coin cells, by varying electrolyte formulations.The MAP leverages automation, robotics, and machine learning to enable high-throughput experimentation, inverse design, and intelligent orchestration of research across the entire battery value chain – from lab benches to manufacturing and back. This holistic, intention-agnostic approach aims to elucidate how materials influence interfaces, components, and full battery packs, and vice versa. By integrating robotic experimentation, intelligent manufacturing, and machine learning models like the Attention-based Recurrent Algorithm for Neural Analysis (ARCANA) of battery lifetime, the MAP seeks to achieve 10-100x acceleration in battery research and development, reducing the current 20-30 year proliferation time to just a few months.The results demonstrate the MAP's capability to rediscover known trends, like ionic conductivity variation with salt concentration, while also enabling complex multi-scale optimizations spanning materials, components, and full systems. This paradigm shift in research orchestration highlights the power of autonomously linking models, theory, and experiments across domains to spark revolutionary advances in battery technology development.The talk will conclude with a demo of the Kilofactory, the worlds first fully automated pouch cell factory that starts from active materials precursors and is capable of providing a fully parameterized cell every 15 minutes.

P3LS: Partial Least Squares under privacy preservation

Modern manufacturing value chains require intelligent orchestration of processes across company borders in order to maximize profits while fostering social and environmental sustainability. However, the implementation of integrated, systems-level approaches for data-informed decision-making along value chains is currently hampered by privacy concerns associated with cross-organizational data exchange and integration. We here propose Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated learning technique that enables cross-organizational data integration and process modeling with privacy guarantees. P3LS involves a singular value decomposition (SVD) based PLS algorithm and employs removable, random masks generated by a trusted authority in order to protect the privacy of the data contributed by each data holder. We demonstrate the capability of P3LS to vertically integrate process data along a hypothetical value chain consisting of three parties and to improve the prediction performance on several process-related key performance indicators. Furthermore, we show the numerical equivalence of P3LS and PLS model components on both a synthetic and a real-world dataset and provide a thorough privacy analysis of the former. Moreover, we propose privacy-preserving explained X- and Y-block variance computations for determining the contribution of each data holder to the federated process model as a basis to incentivize data federation and fair profit-sharing.

Intelligent Resource Orchestration for 5G Edge Infrastructures

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding

Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. In this respect, there is an increasing need for intelligent orchestration mechanisms, such that NFV can exploit its potential and live up to its promise. Genetic algorithms have emerged as a promising alternative to the proliferation of heuristic and exact methods for the Service Function Chain (SFC) embedding problem. To this end, we design and evaluate a genetic algorithm (GA), which computes efficient embeddings with runtimes on par with approximate methods. We present a GA model as state-space search in order to clarify the design choices of a GA. Our proposed GA utilizes a heuristic for the generation of the initial population, with the aim of directing the search towards the solution. Given the sensitivity of GAs on their various parameters, we introduce a parameter adjustment framework for GA fine-tuning. A comparative evaluation among a range of GA variants with diverse features sheds light on the impact of these features on SFC embedding efficiency. The GA variant that stands out is further benchmarked against a baseline greedy algorithm and a state-of-the-art heuristic. Our evaluation results indicate that the GA yields notable gains in terms of request acceptance and resource efficiency.

Flexible Skill-based Production Systems through novel OPC UA Design Approaches

In order to align future forms of production with flexibility, interoperability, and adaptability, the essential steps toward horizontal and vertical integration are indispensable. This paper discusses a conventional and conceptual approach to OPC UA communication and intelligent orchestration of skill-based control logic. The conceptual approach synthesizes this idea with agent structures to provide the possibility to access and combine production capabilities in a reconfigurable manner whilst ensuring the stringent technical requirements on lower automation layers realized by Programmable Logic Controllers (PLCs).

Intelligent Orchestration Algorithm for Power Backbone Optical Network with Service QoS Awareness

The power backbone optical network has the shortcomings of low spectrum utilization, and high service blocking rate, which cannot satisfy the differentiated quality of service (QoS) demands of the power services. This paper proposed a power backbone optical network architecture that uses elastic optical networks (EONs) to improve frequency utilization, and proposes an upper confidence bound (UCB)-based intelligent orchestration algorithm for power backbone optical network. Simulation verified that the proposed intelligent orchestration algorithm can improve the performance of the intelligent bandwidth allocation and meet the requirements of differentiated QoS demands.

Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation.

Advances in 5G and the Internet of Things (IoT) have to cater to the diverse and varying needs of different stakeholders, devices, sensors, applications, networks, and access technologies that come together for a dedicated IoT network for a synergistic purpose. Therefore, there is a need for a solution that can assimilate the various requirements and policies to dynamically and intelligently orchestrate them in the dedicated IoT network. Thus we identify and describe a representative industry-relevant use case for such a smart and adaptive environment through interviews with experts from a leading telecommunication vendor. We further propose and evaluate candidate architectures to achieve dynamic and intelligent orchestration in such a smart environment using a systematic approach for architecture design and by engaging six senior domain and IoT experts. The candidate architecture with an adaptive and intelligent element (“Smart AAA agent”) was found superior for modifiability, scalability, and performance in the assessments. This architecture also explores the enhanced role of authentication, authorization, and accounting (AAA) and makes the base for complete orchestration. The results indicate that the proposed architecture can meet the requirements for a dedicated IoT network, which may be used in further research or as a reference for industry solutions.

Mobile Games for a house museum.

The paper discusses a formal didactic activity in a higher education context, which brought to the design, development, and testing of thirteen Location-Based Mobile Games (LBMGs) for the Bagatti Valsecchi House Museum. The activity involved BSc Design students in developing and testing interactive solutions aimed at reaching out to the "under 35" community of the museum with engaging and entertaining experiences. For this purpose, the stakeholder group of the museum experts was also involved in co-designing the solutions. On the one hand, this study focuses on the beneficial approach of involving Design students in the multiple roles of designer, player/visitor, and target audience. On the other hand, it looks at those aspects that may turn LBMGs into a means for engaging and entertaining museum visitors' experiences. We focus on four LBMGs (out of thirteen) that the museum selected to be tested with their younger community, highlighting those elements that emerged as particularly relevant for enhancing visitors' engagement and motivations. In this regard, three aspects stand out as the most impacting: (i) the benefits of a design approach based on the early involvement of both experts (the museum) and the target audience (students themselves); (ii) an intelligent orchestration of narratives and game mechanics, specifically designed to leverage the fascinating museum space, and (iii) the ability of such games to stimulate social engagement.

Learning-based Orchestrator for Intelligent Software-defined Networking Controllers

This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we present this using Machine Learning in Software-Defined Networking technology. The first task consists of extracting relevant information from the Data flow generated from a SDN and using them to learn, to predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional Neural Network model to separate the classes that we’re working on. The result was very promising the model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to researchers looking for the trade-offs between SDN performance and IA customization.

Teaching young adult learners is an intelligent orchestration of science and arts

Purpose This paper aims to propose a classroom teaching orchestration technique, analogically drawing a reference from the movie, Prestige (2006). The generation of post-millennials has shorter attention span and motivation and prefers a learner-centric teaching style. This paper attempts to mitigate such challenges by bringing an analogy with a magic trick along with proposing a “divergent-convergent diamond structure” to anchor and synthesise teaching deliverables for the learners. Design/methodology/approach This is a practitioner’s study where practical experiences of the authors have led to the conceptualisation of the central theme discussed. Findings From a practitioner’s opinion and interpretation, the proposed “divergent-convergent diamond structure” has the potential to make postgraduate classes more engaging. The proposed structure may suggestively promote self-efficacy, trigger curious minds, bring relevance, indulge participatory learning and consolidate the concepts.

Cloud-Edge Orchestration for the Internet of Things: Architecture and AI-Powered Data Processing

The Internet of Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing, and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralized and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This article first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.

Intelligent orchestration of database resources in power system

With the continuous development of cloud computing technology, the size of database clusters is getting larger and larger. The database resources in existing power systems need to be manually deployed, which puts great pressure on operation and maintenance personnel. Therefore, resource orchestration and automatic deployment are proposed and realized. This method implements a visual orchestration interface through the GoJS framework, establishes a deployment template, uses Spring template to encapsulate task execution scripts, orchestrates database instances, high-availability standby machines, and logical replication at one time, simplifying the deployment process. Practice shows that this method can be effective Reduce deployment time and improve work efficiency.

Can an orchestration system increase collaborative, productive struggle in teaching-by-eliciting classrooms?

ABSTRACT Mathematics is often taught by explaining an idea, then giving students practice in applying it. Tutoring systems can increase the effectiveness of this method by monitoring the students’ practice and giving feedback. However, math can also be taught by having students work collaboratively on problems that lead them to discover the idea. Here, teachers spend the bulk of their time orchestrating collaborations and supporting students in building productively on each other’s contributions. Our research question is: Can tutoring technology somehow make teaching-by-eliciting more effective? Using tutoring technology, we developed an intelligent orchestration system named FACT. While students solve problems in small groups, it makes recommendations to the teacher about which groups to visit and what to say. Data from over 50 iterative development trials (study 1) suggest that FACT increased neither the collaboration nor productivity of the students’ struggle compared to paper-based classes. However, the data also suggest that when there is just one teacher in the classroom, then only a few of the groups that need a visit can get one. We modified FACT to directly send students the provocative questions that it formerly sent only to teachers. A pilot test (study 2) suggests that this version may increase productive struggle, but increasing collaboration remains an unsolved problem.

Middleware, framework and novel computing models for grid and cloud service orchestration

Highly scalable, resilient commodity hardware along with the availability of huge amounts of data allow consumers and companies to access to a wide range of high-value services through a pay-per-use, pay-as-you-go model. The increasing need for intelligent services at large scale is bringing providers to optimise the use of resources in order to meet the increasing web-scale demand. Programming models are evolving to address the high complexity of service composition, which need resiliency and efficiency. Algorithms and technologies for intelligent orchestration allow multiple owners of cloud and grid infrastructures to satisfy customer requirements and business constraints. We assist in the development of new architectures, frameworks, middlewares and computing models, as well as innovative applications, aimed at supporting scientists and business analysts in the development of unified computing frameworks and data analysis at large scale. This paper summarises a number of contributions given in the area of distributed cloud and grid computing, with particular emphasis on service orchestrations and emergent applications for high-value services.

Orchestrating OGC web services to produce thematic maps in a spatial information infrastructure

An intelligent geoportal orchestrates (automatically coordinate) web services to prepare, discover and present information to the user. Implementations of web service standards by the Open Geospatial Consortium (OGC) and ISO/TC 211, Geographic information/Geomatics, such as, the Web Map Service (WMS), Web Feature Service (WFS) and Styled Layer Descriptor (SLD) enable the display of spatial data in a geoportal. Ultimately, our goal is to intelligently orchestrate web services to produce thematic maps. As a first step towards our ultimate goal, in this article, we present the results of experiments with the orchestration of OGC web services to produce thematic maps. Our goal here was to evaluate whether orchestrating OGC web services can produce thematic maps. The results prove that this is possible, but show that there is customized functionality that has to be wrapped into WPSs. This poses a challenge to on the fly intelligent orchestration, which is required in an intelligent geoportal.