Dynamic Service Research Articles

A Novel Dynamic Bandwidth Allocation Scheme for XGPON based Mobile Fronthaul for Small Cell CRAN

Time division multiplexing passive optical networks (TDM-PON) have emerged as a promising technology for mobile fronthaul for small cell CRAN. The performance of such TDM-based PON mobile fronthaul for small cell CRAN greatly depends on efficient dynamic bandwidth allocation (DBA) mechanisms. In this paper, a novel DBA mechanism based on the concept of dynamic service interval for ITU-based TDM-PON (XG-PON) for mobile fronthaul has been proposed. The proposed scheme is implemented using network simulator −3 (NS-3) simulation tool for evaluation of network performance parameters. The performance evaluation of the proposed DBA against the existing schemes such as optimized round robin (ORR) and Group-GIANT DBA shows a significant improvement in upstream mean delay, packet loss ratio and grant-request ratio. In addition to this, the proposed scheme shows that larger number of ONU(RRH) can be supported by the network for front haul uplink traffic transmitted via XG-PON in small cell CRAN.

EXPLORATORY RESEARCH ON THE RELATIONSHIP BETWEEN DIGITAL SERVICE SUPPLY CHAIN CAPABILITY AND SUPPLY CHAIN PERFORMANCE

Cross-border e-commerce (CBEC) has developed rapidly in the recent years, with continuous formation of new patterns in international and domestic double cycle development in the post-epidemic era. CBEC will be playing a more important role and undertake the task of foreign trade transformation and driving domestic economic growth. In this case, the corresponding service supplies chain capability（SSCC）becomes a key part of whether the CBEC platform could complete the task. Based on the grounded theory, this research has collected data through interviews with the relevant people working in the supply chain department of the CBEC platform to systematically explore the key factors behind dynamic service capability of the digital supply chain within the CBEC platform service ability. Then, the author constructs the theoretical model of the Digital Service Supply Chain Capability. The results show that the CBEC digital service supply chain capability consists of the logistics, capital flows and information flows. Data analysis includes internal data and external data, while internal data consists of the three main categories — customer demand forecast, product quality prediction and internal data collection & analysis. Finally, external data consists of the three main categories: market intelligence collection, assessing supplier risks and big data.

In Situ Investigation on Life‐Time Dynamic Structure–Performance Correlation Toward Electrocatalyst Service Behavior in Water Splitting

AbstractElectrocatalytic water splitting is recognized as quite a promising protocol for the renewable hydrogen energy production, and significant effort has been devoted to the design of high‐performance electrocatalysts that can efficiently alleviate the critical issue of fossil fuel scarcity. However, the vast majority of traditional design strategies restrictedly focus on the pristine electrocatalyst structure and ultimate performance results, which may lead to the incorrect or even opposite understanding of catalytic structure–performance correlations. With the burgeoning development of in situ techniques, the dynamic service behaviors of electrocatalysts during water splitting have been growingly investigated from enriched perspectives, only by which can the life‐time dynamic structure–performance correlations be established. Herein, to shed new light on the next‐stage development of in situ investigations for water splitting electrocatalysts, a series of dynamic service behavior that existed in water splitting process and highlight their key role for understanding the life‐time dynamic structure–performance correlations is comprehensively summarized. Besides, a wide variety of in situ techniques are systematically dissected in terms of their functional features, advantages, and limitations. Critical challenges and prospects are also discussed for establishing the life‐time dynamic structure–performance correlations of water splitting electrocatalysts.

Teknologi Baru Pada Pendidikan Tinggi Menuju Revolusi Industri 4.0: Studi Kasus Indonesia dan Malaysia

IoT with E-learning is intended to support data collection from devices and share to other devices in use for effective E-learning applications from Smart Campus. This study aims to conduct studies related to online learning models by utilizing Internet of Everything (IoE) technology to support Revolution 4.0. This study aims to support the latest communication paradigm in which the objects of everyday life will be equipped with a series of appropriate protocols and enable them to communicate well with each other as part of the internet. IoE will help improve learning by leveraging the large subject data generated by these objects to provide dynamic services to educators, learners, and even content developers. Using qualitative research methods This research uses a questionnaire to find out the views and assessments of the community in this case online learners regarding online learning as one of the impacts of the Covid-19 pandemic and produces an online learning model that is supported by an integrated system between learning media such as LMS and devices. others use IoE. The results of this study support the implementation of Smart campuses that allow the use of IoE methodologies to make them always ready in certain network areas.

Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.

Investigation of Dynamic Responses and Vibration Serviceability of Temporary Grandstands by a 3 DOF Interaction Model due to Swaying Motion

Excessive vibration of temporary grandstand by the crowd has lateral rhythmic motions, which attracted increasing attention in the recent years. This paper focuses on experiments where a temporary grandstand occupied by 20 participants is oscillated by a shaking table with a series of random waves and the crowd-induced rhythmic swaying motions at lateral direction, respectively. The dynamic forces that were induced by participants who have swayed at 0.5–1.8 Hz are recorded by a tri-axial human biomechanics force plate. A new relationship between the annoyance rate and structural acceleration at logarithmic coordinate is investigated and proposed, and the swaying load model is given. Based on these experimental results, a simplified three-degree-of-freedom lumped dynamic model of the joint human–structure system is reinterpreted. Afterwards, combined with a feasible range of crowd/structural dynamic parameters, a series of interaction models are analyzed, the vibration dose value (VDV) of the structure is obtained and discussed, and the notable parameters for interaction model are predicted. The experimental results show that the lateral serviceability limit is 1.29 m/s1.75 and the upper boundary is 2.32 m/s1.75. The dynamic response of model indicated that the VDV of structure will be decreased with increasing the mass of static crowd and damping ratio of the dynamic crowd. The max response of the model is α ≤ 0.6, f2 = 1.8 Hz or α > 0.6, f2 = 1.5 Hz or f1 = 2.5–3.5 Hz. It may be used as a reference value in vibration safety and serviceability assessment of TDGs, to estimate realistically the vibration response on the occasions when the crowds are swaying.

Brown-Field Migration Aware Routing and Spectrum Assignment in Backbone Optical Networks

With the development of optical networks technology, broad attention has been paid to flexible grid technology in optical networks due to its ability to carry large-capacity information as well as provide flexible and fine-grained services through on-demand spectrum resource allocation. However, a one-time green-field deployment of a flexible grid network may not be practical. The transition technology called the fixed/flex-grid optical networks is more applicable and highly pragmatic. In such network, many nodes would likely be upgraded from a fixed-grid to flex-grid. In fact, dynamic service provisioning during the process of a node upgrade in fixed/flex-grid optical networks have become a challenge because the service connection can be easily interrupted, which leads to considerable data loss because of node upgrade. To overcome this challenge, we propose a brown-field migration aware routing and spectrum assignment (BMA-RSA) algorithm in fixed/flex-grid optical networks. The aim is to construct a probabilistic migration label (PML) model. The well-designed label setting of PML can balance the relationship between distance and node-upgrade probability. Dynamic service provisioning operations are undertaken based on the PML model to achieve a migration-aware dynamic connection before network migration occurs. We also evaluate the performance of different service provisioning strategies under different traffic models. The simulation results show that the BMA-RSA algorithm can achieve: (1) the tradeoff between distance and node upgrade probability during the process of service provisioning; (2) lower service interruption compared with the traditional non-migration aware K-shortest-path routing and spectrum assignment algorithm.

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

Dynamic Service Restoration for Integrated Energy Systems Under Seismic Stress

Seismic events and following aftershocks can cause devastating impacts on overground and underground energy system infrastructures. This paper designs a novel approach to restore the system functionality in response to seismic stress by using coordinated dynamic system reconfiguration and operation. First, seismic impact on system infrastructures is evluated, considering substations, pipes and distribution lines. Load curtailment is assumed to be caused by the overloading of system branches, buckling towers, reversed gas flows, and isolated sub-circuits. Smart system operation and reconfiguration are coordinatedly applied by using combined soft open points (SOP) and energy storage units, aiming to restore system service by minimizing load curtailment and system recovering time. Service index is quantified considering both load curtailment and recovering time. A representative integrated electricity and gas system is employed to demonstrate the effectiveness of the proposed method. Results illustrate that the adverse impact of seismic on systems can be effectively reduced by the proposed dynamic service restoration method. The model provides system operators with a powerful tool to restore the functionality of distribution systems under seismic events.

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices’ status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices’ features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

Evolutionary Algorithm Based Adaptive Load Balancing (EA-ALB) in Cloud Computing Framework

In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work. Initially, process of K-means clustering is used for computing optimal min-max rates and then, local search capability for solving the load balancing problems in cloud model is determined with the incorporation of Evolutionary Algorithm. The results show that the proposed model achieves better results in terms of load balancing factors, Virtual Machine (VM) migration, energy consumption and so on, when compared to the existing model.

MAACO: A Dynamic Service Placement Model for Smart Cities

Smart cities generate huge volumes of data to be processed by applications with different criticality and requirements. For example, a healthcare application needs lower latency when requested from an ambulance travelling to a hospital during an emergency compared to applications in less-critical domains. Cities can use Multi-access Edge Computing to reduce latency by placing applications’ services closer to users. A service placement process selects the set of servers to run the services for deployment. Smart cities challenge this selection as a large number of servers and services generate a large number of potential solutions with different QoS properties. Additionally, placement approaches must consider applications’ criticality and users’ mobility to offer an appropriate overall latency. Current approaches have considered servers’ utilisation and users’ location to place services. However, they do not consider applications’ criticality and mobile users’ paths. This paper presents MAACO, a Mobility-Aware, priority-driven, ACO-based service placement model that prioritises applications according to their criticality and minimises critical applications’ latency, while considering predicted paths for mobile users. Evaluation results show that MAACO achieves lower latency and waiting time compared against baselines at the cost of reduced load balance between the network servers.

Optimizing and Visualizing Dynamic Service Performance for Airspace Users to Enable Safe and Efficient Urban Air Mobility in Dubai

Optimizing and Visualizing Dynamic Service Performance for Airspace Users to Enable Safe and Efficient Urban Air Mobility in Dubai

Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile Computation Offloading in Industrial Networks

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

ShareOn: Shared Resource Dynamic Container Migration Framework for Real-Time Support in Mobile Edge Clouds

Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn’s performance in comparison to alternative migration techniques, achieving a gain of 15–22% in system response time for highly loaded edge cloud nodes.

Systematic Scalability Modeling of QoS-aware Dynamic Service Composition

In Dynamic Service Composition (DSC), an application can be dynamically composed using web services to achieve its functional and Quality of Services (QoS) goals. DSC is a relatively mature area of research that crosscuts autonomous and services computing. Complex autonomous and self-adaptive computing paradigms (e.g., multi-tenant cloud services, mobile/smart services, services discovery and composition in intelligent environments such as smart cities) have been leveraging DSC to dynamically and adaptively maintain the desired QoS, cost and to stabilize long-lived software systems. While DSC is fundamentally known to be an NP-hard problem, systematic attempts to analyze its scalability have been limited, if not absent, though such analysis is of a paramount importance for their effective, efficient, and stable operations. This article reports on a new application of goal-modeling, providing a systematic technique that can support DSC designers and architects in identifying DSC-relevant characteristics and metrics that can potentially affect the scalability goals of a system. The article then applies the technique to two different approaches for QoS-aware dynamic services composition, where the article describes two detailed exemplars that exemplify its application. The exemplars hope to provide researchers and practitioners with guidance and transferable knowledge in situations where the scalability analysis may not be straightforward. The contributions provide architects and designers for QoS-aware dynamic service composition with the fundamentals for assessing the scalability of their own solutions, along with goal models and a list of application domain characteristics and metrics that might be relevant to other solutions. Our experience has shown that the technique was able to identify in both exemplars application domain characteristics and metrics that had been overlooked in previous scalability analyses of these DSC, some of which indeed limited their scalability. It has also shown that the experiences and knowledge can be transferable: The first exemplar was used as an example to inform and ease the work of applying the technique in the second one, reducing the time to create the model, even for a non-expert.

Stationary system-length distribution of Markovian bulk service queue with modified bulk service rule and dynamic service rates

In this paper, we consider a single server bulk service queue, with modified bulk service rule, say rule, wherein customers are served in batches of maximum batch size K with minimum threshold value L. Further, we allow the customers to enter the service if the server has begun the service and is having less than K customers. In addition, service rates of the batches are taken to be dependent on the size of the batch. Analytic expressions for the joint probability distribution of the number of customers in the queue, and with the server, the marginal probability distribution of the number of customers in the queue, system and with the server is derived. A thorough numerical investigation of the effect of various parameters on the distributions obtained above is done and numerical results are presented in the form of tables and graphs. We also obtain various performance measures (both in closed-form as well as numerically) such as the average number of customers in the queue, system, with the server, etc.

A decentralized agent‐based semantic service control and self‐adaptation in smart health mobile applications

AbstractIn this article, we propose a decentralized agent‐based autonomic semantic service adaptation controller and reconfiguration (ASSACR) to solve the problem of context‐aware service selection and deployment for distributed healthcare mobile applications, where functional and nonfunctional users' needs are expressed using domain‐independent ontology. The main objective of this approach is to provide optimal personalized services as fast as possible. Indeed, a new ontology for semantic description and parallel management of the contextual services is presented. At first, a new dominance operator‐based constraints violation degree is used to reduce the search space. Then, optimal personalized paths that meet user's needs, preferences and devices' availability are discovered and selected in a smaller search space more effectively and efficiently using dynamic services selection algorithm, based on new ASSACR multiagent strategies. To validate the performance and efficiency of the proposed approach, we compared the proposed approach with existing multiagent strategies of the diabetic follow‐up use cases. Experimental results show that the proposed approach effectively addressed both the semantic service representation and agent aspects in terms of performance and quality service selection.

Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles

The expected huge amount of connected cars and applications with varying Quality of Service (QoS) demands still depend on agile/flexible networking infrastructure to deal with dynamic service requests to the control plane, which may become a bottleneck for 5G and Beyond Software-Defined Network (SDN) based Internet of Vehicles (IoV). At the heart of this issue is the need for an architecture and optimization mechanisms that benefit from cutting edge technologies while granting latency bounds in order to control and manage the dynamic nature of IoV. To this end, this article proposes an autonomic software-defined vehicular architecture grounded on the synergy of Multi-access Edge Computing (MEC) and Network Functions Virtualization (NFV) along with a heuristic approach and an exact model based on linear programming to efficiently optimize the dynamic resource allocation of SDN controllers, ensuring load balancing between controllers and employing reserve resources for tolerance in case of demand variation. The analyses carried out in this article consider: (a) to avoid waste of limited MEC resources, (b) to devise load balancing among controllers, (c) management complexity, and (d) to support scalability in dense IoV scenarios. The results show that the heuristic efficiently manages the environment even in highly dynamic and dense scenarios.

Adaptive multi-objective service composition reconfiguration approach considering dynamic practical constraints in cloud manufacturing

Dynamic uncertainty factors such as equipment faults are common in practically implemented cloud manufacturing (CMfg) environments, often causing the manufacturing service to be invalidated. In that case, efficient reconfiguration of the original service composition under practical constraints is critical; however, existing research scarcely focuses on it. This paper proposes a dynamic service composition reconfiguration model to bridge the gap by considering practical constraints (DSCRPC) in a real-life cloud manufacturing environment. Based on the constraints considered in this study, the DSCRPC model redefines three objectives: time (T*), cost (C*), and product service quality (Q*S*). To optimize the DSCRPC model, this study developed an adaptive multi-population multi-objective whale optimization algorithm (AMPOWOA) based on the Pareto strategy. The algorithm adopts four balancing strategies and adaptively optimizes and adjusts the key parameters under various balancing strategies through well-designed reinforcement learning models. Finally, we conduct numerical experiments and actual application case tests to compare the performances of AMPOWOA and other algorithms (MOWOA, MOHHO, NSGA-II). The results show that DSCRPC can continuously tackle the cloud manufacturing service composition (CMSC) reconfiguration issue with constraints until an order is completed. Moreover, AMPOWOA is superior to the other algorithms optimizing the DSCRPC model. This significantly enhances the robustness of service composition reconfiguration in real-life CMfg.