Orchestration Mechanisms Research Articles

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Orchestration mechanisms in sustainability-oriented innovation: a meta-organization perspective

Purpose This paper aims to investigate how orchestration mechanisms characterizing meta-organizations can support sustainability-oriented innovation (SOI) leveraged by digital transition. Design/methodology/approach This study adopted a qualitative approach. The authors collected qualitative data from 41 in-depth interviews with key informants, participatory longitudinal observations and secondary data analysis related to a digital innovation hub (DIH) and its network. Findings The findings reveal how meta-organizations, such as DIHs, advance SOI – considering the potentialities of digital technologies – through four orchestration mechanisms: collaboration platform setup, SOI resource arrangement, SOI process enablement and SOI scale-up. Each mechanism is founded on recurrent SOI practices identified as aligning, assembling and progressing, and each is characterized by specific activities that depend on meta-organizations’ evolutionary purposes related to the stages of the creation process. The activation of mechanisms concerns a process outlining three sequential evolutionary trajectories: for assessing and regulating interaction, for promoting and coordinating collective action and for creating a collective identity. Originality/value This study contributes to meta-organization and orchestration research oriented toward SOI and leveraged by the digital transition, and considers the distinctive orchestration mechanisms and practices required to support SOI leveraged by the digital transition. Differently from previous research on orchestration and meta-organizations, this study considers the time perspective, and thus, the sequentiality of mechanisms (evolutionary trajectories) to achieve the purposes characterizing the stages of meta-organizations’ creation process.

Optimizing Key Value Indicators in Intent-Based Networks through Digital Twins aided service orchestration mechanisms

For many years, the orchestration of network resources and services has been addressed by considering homogeneous communication infrastructures and simple Service Level Agreements (SLAs), generally defined through a list of traditional Key Performance Indicators (KPIs). Unfortunately, state-of-the-art solutions risk being quite ineffective for future telecommunication systems. Beyond 5G networks, for instance, are emerging as complex and heterogeneous ecosystems where resources belonging to diverse network domains with evolving capabilities can be dynamically exposed to support much more complex and cross-domain services and applications. At the same time, SLAs will be defined by also considering novel performance demands, including security, economic, and environmental needs. Based on these premises, this work proposes a novel orchestration strategy designed to fulfill service requirements expressed through Key Value Indicators (KVIs), while combining the potentials of both Network Digital Twins and Intent-Based Networking. Leveraging insights from Network Digital Twins, multiple service orchestration options are explored to optimize resource utilization. Simultaneously, Intent-Based Networking is adopted to streamline network management via intents, specifying Beyond 5G requirements through KPIs and KVIs. An optimal orchestration scheme has been conceived through a multi-criteria decision-making algorithm and a many-to-many matching game between domains and service requests mapped into intents, aiming to minimize SLA violations over time. The performance of the conceived solution has been investigated through computer simulations in realistic scenarios. The obtained results clearly highlight its effectiveness and demonstrate that it is able to reduce SLA violations (related to latency, throughput, costs, and cyber risk requirements) by up to 22.44% compared to other baseline techniques.

Understanding Supply Chain Orchestration Mechanisms to Achieve Sustainability-Oriented Innovation

Understanding Supply Chain Orchestration Mechanisms to Achieve Sustainability-Oriented Innovation

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.

Data pipeline approaches in serverless computing: a taxonomy, review, and research trends

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments.

ORCHESTRATION IN INNOVATION ECOSYSTEMS PRE-INITIATION STAGE

The orchestrator plays a vital role during the design, mapping, and analysis of the ecosystems’ pre-initiation stages, which will be important in shaping the ecosystem guidelines later. However, there is a lack of knowledge on how these firms plan entry strategies before innovation ecosystem (IE) building. So, this study aims to investigate which orchestration practices support orchestrators in the design of ecosystems. This work transposes innovation network practices to a more complex ecosystem context. We summarised these practices into four orchestration mechanisms. We draw on orchestrating innovation networks’ theoretical perspective and depict the orchestration mechanism into practices associated with understanding how orchestrators design IE in its very early stages of formation. This study contributes to the literature on uncertainty management at the IE level by showing practices for uncertainty management as one important driver for unlocking ecosystem emergence. Second, this study advances the configurational view of IE by depicting an underlying set of practices performed by IE orchestrators and members to foresee and design the IE before its emergence, addressing the dynamics of nascent ecosystem operations. Third, this study contributes to the shaping literature by showing ways to imagine future possibilities and acting proactively to realise those possibilities inside this actionable new structure of economic relationships that requires specific management to increase the probability of success for value proposition’s (VP’s) creation and delivery to the market.

Light-HIDRA: Scalable and decentralized resource orchestration in Fog-IoT environments

With the proliferation of Internet of Things (IoT) ecosystems, traditional resource orchestration mechanisms, executed on fog devices, encounter significant scalability, reliability and security challenges. To tackle these challenges, recent decentralized algorithms in Fog-IoT use Distributed Ledger Technologies to orchestrate resources and payments between peers. However, while distributed ledgers provide many desirable properties, their consensus mechanism introduces a performance bottleneck. This paper introduces Light-HIDRA, a consensus-less and decentralized resource orchestration system for Fog-IoT environments. At its core, Light-HIDRA uses Byzantine Reliable Broadcast (BRB) to coordinate actions without centralized control, therefore drastically reducing communication overhead and latency compared to consensus-based solutions. Light-HIDRA coordinates the scheduling and execution of workloads, and securely manages the payments that peers receive for dedicating resources to workloads. Light-HIDRA further increases performance and reduces overhead by grouping peers into distinct domains. We conduct an in-depth analysis of the protocol’s security properties, investigating its efficiency and robustness in diverse situations. We evaluate the performance of Light-HIDRA, highlighting its performance over HIDRA, a state-of-the-art baseline that uses smart contracts. Our experiments demonstrate that Light-HIDRA reduces the bandwidth usage by up to 57x, the latency of workload offloading by up to 142x, and shows superior throughput compared to HIDRA.

Scalability and Performance Optimization Strategies in Large-Scale System Architectures

This paper aims to break away from traditional thinking paradigms by introducing theories and methods from complexity science to provide innovative and effective solutions for scalability and performance optimization of large-scale system architectures. The paper first constructs a complex network model for large-scale system architecture, which includes nodes (services), edges (interactions), and global attributes, along with dynamic models reflecting key metrics such as system performance and scalability, providing a theoretical foundation for subsequent research. Subsequently, from a complexity perspective, it innovatively proposes scalability strategies, including adaptive scalability strategies for complex network optimization, data-driven complex system regulation, and elastic computing and service orchestration mechanisms. In terms of performance optimization, it applies tools such as complex systems observation theory and network flow theory to explore insights and breakthroughs in complexity related to full-link performance monitoring and diagnostics, asynchronous communication, task scheduling, and software-hardware collaborative optimization. Finally, the paper summarizes the research findings, distilling the core concepts and methodological innovations of scalability and performance optimization strategies from a complexity perspective. It also looks ahead to the prospects of further integrating complexity science and information technology in the future, along with potential research topics in new scenarios and challenges.

5G and edge: A reinforcement learning approach for Virtual Network Embedding with cost optimization and improved acceptance rate

5G technologies are fueling a revolution across numerous industries, including manufacturing, healthcare, and entertainment, by enabling the development and deployment of novel applications at the network’s edge. To meet the demanding service level agreements of these industries, a dynamic and adaptable infrastructure strategy that combines cloud and edge computing models is needed. This hybrid approach offers the benefits of both centralized cloud processing and decentralized edge computing, optimized for responsiveness and efficiency. A key element for success is an orchestration mechanism that dynamically allocates resources to ensure the infrastructure can adapt to fluctuating demands in real time, optimizing resource utilization and meeting SLA requirements. Among these mechanisms, virtual network embedding (VNE) and dynamic resource management (DRM) have emerged as tools for defining where and how edge technology should be used. However, current VNE approaches struggle to adapt to real-time fluctuations in demand across geographically distributed edge resources. This work introduces a novel resource allocation algorithm, the VNE-CRS, which uses an Artificial Intelligence technique called Reinforcement Learning to orchestrate resources across multiple domains. This approach benefits from the strength of Reinforcement Learning: its ability to consider the entire problem from beginning to end while incorporating various aspects of 5G Quality of Service Indicators for optimal decision-making. Experiments were conducted to evaluate the performance of VNE-CRS against state-of-the-art algorithms for multi-domain edge environments. Results have shown that employing Reinforcement Learning techniques for VNE resource allocation yields performance gains of 12.32 percentage points in comparison with the GRC algorithm and 28.80 percentage points in comparison with the base edge environment, presenting an acceptability rate closer to the Public Cloud environment with all benefits of edge environment. In conclusion, VNE-CRS offers an efficient solution for resource allocation in 5G environments, achieving superior performance and transforming the VNE architecture into a comprehensive orchestration system that optimizes infrastructure utilization for strategic long-term benefits.

Cost and response time optimization of edge architectures

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives.

Digital technologies and corporate green innovation: opening the “black box” of resource orchestration mechanisms

PurposeDriven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.Design/methodology/approachDrawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.FindingsThe findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.Practical implicationsThe findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.Social implicationsThe research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.Originality/valueCompared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

Memory orchestration mechanisms in serverless computing: a taxonomy, review and future directions

Memory orchestration mechanisms in serverless computing: a taxonomy, review and future directions

Intent-driven orchestration of serverless applications in the computing continuum

Following the emergence of Internet of Things (IoT) and edge computing technologies and the development of distributed applications with strict Quality of Service (QoS) requirements, a transition is underway from centralized computing models to distributed computing continuum systems. The latter provide abstractions of the available resources, while they lead to the development of orchestration mechanisms with increased distributed intelligence and autonomy characteristics. These characteristics make the computing continuum suitable for managing serverless computing applications, considering their loose coupling with the resources and the need for reactiveness and efficiency to serve dynamic workloads in the edge and cloud part of the infrastructure. This paper presents an intent-based approach for the orchestration of a function chain over an edge and cloud infrastructure. We propose a Reinforcement Learning (RL) based autoscaling method that incorporates a reward function that aims to provide optimal utilization of resources and timely autoscaling of the functions, taking advantage of methods for workload prediction. Based on a high-level intent, the proposed optimization problem dictates the scheduling of the functions, minimizing the communication overhead and transformation cost while also considering the energy efficiency of the infrastructure. The proposed solution is evaluated with other state-of-the-art techniques for autoscaling and scheduling of serverless functions in a small edge infrastructure. Our solution provides 1.52% QoS violations with a slight increase in the deployed resources, while also significantly reducing the total cost and power consumption for the deployment of the function chain.

AI-Assisted Synergetic Orchestration Mechanisms for Autoscaling in Computing Continuum Systems

AI-Assisted Synergetic Orchestration Mechanisms for Autoscaling in Computing Continuum Systems

Optimizing the configuration of personalized service supply chain under resource orchestration mechanism

Optimizing the configuration of personalized service supply chain under resource orchestration mechanism

The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges

The emergence of the sixth generation of cellular systems (6G) signals a transformative era and ecosystem for mobile communications, driven by demands from technologies like the internet of everything (IoE), V2X communications, and factory automation. To support this connectivity, mission-critical applications are emerging with challenging network requirements. The primary goals of 6G include providing sophisticated and high-quality services, extremely reliable and further-enhanced mobile broadband (feMBB), low-latency communication (ERLLC), long-distance and high-mobility communications (LDHMC), ultra-massive machine-type communications (umMTC), extremely low-power communications (ELPC), holographic communications, and quality of experience (QoE), grounded in incorporating massive broad-bandwidth machine-type (mBBMT), mobile broad-bandwidth and low-latency (MBBLL), and massive low-latency machine-type (mLLMT) communications. In attaining its objectives, 6G faces challenges that demand inventive solutions, incorporating AI, softwarization, cloudification, virtualization, and slicing features. Technologies like network function virtualization (NFV), network slicing, and software-defined networking (SDN) play pivotal roles in this integration, which facilitates efficient resource utilization, responsive service provisioning, expanded coverage, enhanced network reliability, increased capacity, densification, heightened availability, safety, security, and reduced energy consumption. It presents innovative network infrastructure concepts, such as resource-as-a-service (RaaS) and infrastructure-as-a-service (IaaS), featuring management and service orchestration mechanisms. This includes nomadic networks, AI-aware networking strategies, and dynamic management of diverse network resources. This paper provides an in-depth survey of the wireless evolution leading to 6G networks, addressing future issues and challenges associated with 6G technology to support V2X environments considering presenting +challenges in architecture, spectrum, air interface, reliability, availability, density, flexibility, mobility, and security.

Symbiotic association of resources and market-facing capabilities in supply chains as determinants of performance: a resource orchestration perspective

PurposeThe purpose of this study is to advance the understanding of resource orchestration in inter-firm partnerships that appropriately configure and align strategic cross-firm supply chain resources and capabilities generating synergies to deliver superior performance.Design/methodology/approachApplying the resource orchestration logic, supported by the relational view of competitive advantage, the study draws from an empirical analysis of survey data from 152 top-level executives of US manufacturing firms to investigate the effect of leveraging and coherently combining cross-firm supply chain resources with capabilities on operational performance.FindingsThe study underscores the view that appropriately orchestrated combinations of key partnership resources and capabilities as mechanisms for marketing strategy implementation, enhance performance. Specifically, research results suggest that complementary inter-firm resources and lean align, and similarly idiosyncratic resources and agility align synergistically to deliver superior operational performance outcomes. The results also accent partnership responses to intense competition, enabling enhanced operational performance. The findings thus enrich the understanding of the resource orchestration logic and strategy, making important theoretical contributions.Research limitations/implicationsAs is typical in marketing and strategy research, the study research design has a cross-sectional framework, thus limiting insights on the resource orchestration dynamics that can otherwise be generated using a longitudinal design. Also, the resource orchestration stream is still nascent. Further research is needed to delineate the orchestration mechanisms that deliver on performance outcomes, especially in supply chains.Practical implicationsA key insight for supply chain and marketing managers is that close-knit inter-firm partnerships are critical for accessing idiosyncratic and complementary resources that can be configured and symbiotically aligned with market-facing agility and lean capabilities, respectively, to deliver market value. Proactive partnerships, especially in highly competitive and disruptive environments, enable mobilizing cross-firm resources and building appropriate matching combinations with capabilities to deliver on operational performance.Originality/valueThe study, guided by theory, advances the understanding of how key cross-firm resources and capabilities deliver performance gains. The key to competitive advantage and enhanced performance outcomes may lie in acquiring, leveraging and deploying appropriately matched resource-capability combinations. The present study investigates this proposition within the context of supply chain partnerships, focusing on cross-firm resources and capabilities.

Container-based Virtualization for Real-time Industrial Systems—A Systematic Review

Industrial Automation and Control systems have matured into a stable infrastructure model that has been kept fundamentally unchanged, using discrete embedded systems (such as Programmable Logic Controllers) to implement the first line of sensorization, actuation, and process control and stations and servers providing monitoring, supervision, logging/database and data-sharing capabilities, among others. More recently, with the emergence of the Industry 4.0 paradigm and the need for more flexibility, there has been a steady trend towards virtualizing some of the automation station/server components, first by using virtual machines and, more recently, by using container technology. This trend is pushing for better support for real-time requirements on enabling virtualization technologies such as virtual machines and containers. This article provides a systematic review on the use of container virtualization in real-time environments such as cyber-physical systems, assessing how existing and emerging technologies can fulfill the associated requirements. Starting by reviewing fundamental concepts related to container technology and real-time requirements, it goes on to present the methodology and results of a systematic study of 37 selected papers covering aspects related to the enforcement of real-time constrains within container hosts and the expected task latency on such environments, as well as an overview of container platforms and orchestration mechanisms for RT systems.

An Adaptive Multihop Branch Ensemble-Based Graph Adaptation Framework With Edge-Cloud Orchestration for Condition Monitoring

Condition monitoring plays a crucial role in securing smooth production, which has been facilitated into a cyber-physical system (CPS) integration paradigm with the new information and communication technologies and data-driven intelligence. However, traditional methods limit its successful deployment from the different distribution of training data and testing data, the missing relationships between the input signals, and the insufficient data size. To overcome these limitations, a novel graph-based adaptation framework with edge-cloud orchestration is proposed. A three-stage edge-cloud orchestration mechanism is encapsulated with CPS architecture. The proposed graph-based approach mainly consists of an adaptive multi-hop branch ensemble module to intelligently aggregate the node information, a distance metric learner to autonomously align the data distribution, and a classifier module to automatically generate the pseudo-labeled data to guide the edge-cloud orchestration and output results. Finally, a real-life case study and extensive experiments are conducted to prove the effectiveness of the proposed approach.