Strict Service Research Articles

A reliability-based methodology for resilient spare parts planning and control

Geo-political conflicts, financial crises, natural disasters, pandemics, human operational errors, and other disruptive events can affect supply chain integrity with unpredictable consequences. In addition, intermittent demand patterns and strict service requirements enhance the complexity of Spare Parts Supply Chains (SPSCs). SPSC resilience has become a significant challenge in today’s increasingly competitive business environment.This study presents a reliability-based methodology with a customer-oriented perspective to support industrial companies in spare parts planning and control. This original approach provides resilience to SPSCs by defining a statistical variable to study supply chain variabilities and externalities, aiding stockout risk control, and suggesting spare parts management plans. The application of the proposed methodology in a case study of a packaging machine company is illustrated.

High-strength titanium alloy with hierarchical-microstructure design via in-situ refinement-splitting strategy in additive manufacturing

Microstructure design of high-strength materials based on single-principal-microstructure advantage can hardly meet the increasingly strict service requirements of modern aviation and aerospace industries. Compared with conventional forming methods, additive manufacturing (AM) technology introduces more regulatory variables, making it possible to gain multiple microstructure advantages simultaneously for critical components. This study introduces a universal process strategy in laser-powder bed fusion (L-PBF) to achieve hierarchical-microstructure optimization for titanium alloys. Specifically, a higher energy density can refine (R) martensite lath, and an appropriate scanning strategy can split (S) both coarse prior-β grains and α’-colony. For L-PBF-fabricated Ti-6Al-4 V (Ti64) alloy, the proposed refinement-splitting (R & S) optimization strategy improved yield strength (up to ∼1.3 gigapascals) by 30% and the total elongation increase by about 1.9 times compared to the alloy samples with a conventional rich α’-colony microstructure. The split prior-β grains also help with the complex service environment by reducing the anisotropy-induced deterioration of mechanical properties. The R&S approach is, in principle, applicable also to other titanium alloys beyond Ti64.

Kualitas Peningkatan Pelayanan Publik Menuju Aparatur Sipil Negara (ASN) Bertaraf Internasional

The purpose of this research is to examine initiatives to enhance the quality of public services. in order to achieve service standards that are on par with international standards for the State Civil Apparatus (ASN). This study employs a qualitative method with a case study at a local government agency. that has been recognized as the agency with the best quality public services. In-depth interviews with government officials were used to acquire data., employees and customers, as well as analisis of documents related to the implemented public service improvement program. According to the study's findings, local government agencies have succeeded in improving the quality of public services by implementing a number of strategic initiatives and programs. Among them are the effective use of information technology to improve efficiency and effectiveness accessibility of public services, training and human resource development to improve employee skills and professionalism, and implementing strict service standards to measure and ensure customer satisfaction. In an effort to achieve international civil state standards (ASN), local government agencies have set several main goals, including improving communication and speaking foreign languages, mastery of advanced public information technology and application of ethics and integrity in public administration. serve. function. service assignment. In addition, international recognition ISO (International Organization for Standardization) and others awards for quality of public services are successful goals pursued by these bodies.

Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

As the essential infrastructure and important transportation hubs, airport terminals play a strategic role in sustainable cities and urbanization. Due to the strict service requirement and round-the-clock operation, the high energy consumption poses unprecedented challenges to developing environment-friendly airport terminals. Actually, passenger flow as a root cause decides the startup and operation of service facilities including air-conditioning systems. But due to spatial connectivity, parameter coupling, and passenger mobility, resource allocation and system tuning have not been effectively related to passenger travel. In this work, Guangzhou Baiyun International Airport Terminal 2 was taken as an object, and a passenger spatiotemporal distribution model was developed and its contribution to fresh air energy saving was estimated. Among them, zone partition and expression rules were formulated based on insect intelligent building (I2B) architecture to construct Wi-Fi positioning system; Next, for domestic and international passenger, their arrival patterns were fitted by Chi-Square distribution with R2 > 0.993, and their spatiotemporal distribution was predicted with R2 over 0.74 and 0.67 respectively; Lastly, a hybrid reset strategy for fresh air volume set-point combing fuzzy and linear rules was proposed with results showing that HVAC systems running on variable occupancy would lead to considerable energy savings.

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

This paper explores the use of multi-agent deep learning as well as learning to cooperate principles to meet strict service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network algorithms and evaluate their performances in different network, traffic, and routing scenarios, highlighting both the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across different scenarios, decreasing silver and bronze flow median waiting delays by more than 50 % and reducing the SLA violations of the latter by nearly 60 %, with respect to a classic priority queuing approach.

Microstructure and properties of composite coatings by laser cladding Inconel 625 and reinforced WC particles on non-magnetic steel

Due to the extremely complex and strict service conditions of non-magnetic drill collars, the friction, wear and corrosion will lead to their damage and failure. Therefore, the surface modification technology can be used to substantially improve the surface performance and effectively extend the service life of non-magnetic drill collars. Under this engineering background, the laser cladding technology was employed to fabricate pure Inconel 625 and Inconel 625/WC coatings on the surface of TWZ-2 non-magnetic steel substrate, and the influences of WC content on the microstructure, phase composition, element distribution, microhardness and wear and corrosion resistance of these coatings were analyzed. The experimental results show that the microstructures of pure Inconel 625 and Inconel 625/WC coatings are all composed of planar grain, cellular grain, columnar grain and equiaxed grain successively. The phases of pure Inconel 625 coating are composed of γ-Ni phase and Laves phase. With WC particles mixed into Inconel 625 powder, various carbide phases appear in the composite coatings. The higher the WC content, the more the precipitated carbides formed by the reaction, thus aggravating the segregation of the solid solution strengthening elements. Besides, W and C elements present severe local segregation due to the undissolved WC particles. In terms of the hardening and strengthening effects of WC particles, the microhardness of the coatings gradually increases with the rise of WC content. The wear mechanisms of substrate and coatings are all compound wears, and different wear forms have different priorities. Accordingly, the wear mechanisms would change with the WC content. The higher the WC content, the lower the wear loss of coating, and the better the wear and friction resistance. Moreover, the corrosion resistance of coatings is better than that of substrate. With the increase of WC content, the corrosion resistance of the composite coatings decreases gradually.

Deficit Round-Robin: A Second Network Calculus Analysis

Deficit Round-Robin (DRR) is a widespread scheduling algorithm that provides fair queueing with variable-length packets. Bounds on worst-case delays for DRR were found by Boyer et al., who used a rigorous network calculus approach and characterized the service obtained by one flow of interest by means of a convex strict service curve. These bounds do not make any assumptions on the interfering traffic hence are pessimistic when the interfering traffic is constrained by some arrival curves. For such cases, two improvements were proposed. The former, by Soni et al., uses a correction term derived from a semi-rigorous heuristic; unfortunately, these bounds are incorrect, as we show by exhibiting a counter-example. The latter, by Bouillard, rigorously derive convex strict service curves for DRR that account for the arrival curve constraints of the interfering traffic. In this paper, we improve on these results in two ways. First, we derive a non-convex strict service curve for DRR that improves on Boyer et al. when there is no arrival constraint on the interfering traffic. Second, we provide an iterative method to improve any strict service curve (including Bouillard’s) when there are arrival constraints for the interfering traffic. As of today, our results provide the best-known worst-case delay bounds for DRR. They are obtained by using the method of the pseudo-inverse.

Enabling Deterministic Tasks with Multi-Access Edge Computing in 5G Networks

Deterministic executions of computation-intensive and delay-sensitive tasks are vital for realizing Industry 4.0. Multi-access edge computing (MEC) enables critical tasks to be offloaded to the network edge for quickly responding and processing tasks locally. However, during workload peaks at the resource-limited edge, the best effort IP forwarding in the typical network architecture incurs long-tail latency at both the access and interconnection networks of MEC systems. The long-tail latency results in strict service level agreements (SLAs) unable to be guaranteed in end-to-end communication. In this article, we propose a task deterministic network (TDN) architecture that provides communication with bounded low latency and zero jitter for critical tasks among MEC systems. In TDN, the cross-domain collaboration mechanisms of the layer 2/layer 3 deterministic networking and 5G are jointly designed to eliminate the long-tail latency in typical MEC system networks. In addition, we design a seamless working scheme for the time-sensitive networking controller, the MEC orchestrator, and other coordinators to enable edge collaboration and global deterministic forwarding. Moreover, the main functions of TDN are detailed, including clock synchronization, network slicing, cycle mapping, and multi-layer collaboration. Finally, we present simulation results to substantiate the performance of TDN for accommodating critical service flows among MEC systems.

Collaborative edge computing for distributed CNN inference acceleration using receptive field-based segmentation

This paper studies inference acceleration using distributed CNNs in collaborative edge computing network. To ensure no inference accuracy loss in task partitioning, we propose receptive field-based segmentation. To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks. To find the optimal partition of a CNN model, we use dynamic programming, named as DPFP. To address computation heterogeneity of edge servers (ESs), we design a low-complexity search algorithm which can select the optimal subset of collaborative ESs for inference. The experimental results show that DPFP can accelerate inference up to 71% for ResNet-50 and 73% for VGG-16 compared to running the pre-trained models, which outperforms the existing works MoDNN and DeepSlicing. Moreover, we propose an analytical method to estimate the speedup ratio of different GPU platforms by using FLOPs and effective computing capacity. Furthermore, we evaluate the service failure probability under time-variant channel and variation of image sizes, which shows that DPFP is effective to ensure high service reliability with strict service deadline.

Dynamic QoS-Aware Traffic Planning for Time-Triggered Flows in the Real-Time Data Plane

Many networked applications, e.g., in the domain of cyber-physical systems, require strict service guarantees, usually in the form of jitter and latency bounds, for time-triggered traffic flows. It is a notoriously hard problem to compute a network-wide traffic plan that satisfies these requirements, and dynamic changes in the flow set add even more challenges. Existing traffic-planning methods are ill-suited for dynamic scenarios because they either suffer from high computational cost, can result in low network utilization, or provide no explicit guarantees when transitioning to a new traffic plan that incorporates new flows. Therefore, we present a novel approach for dynamic traffic planning of time-triggered flows. Our conflict-graph based modeling of the traffic planning problem allows to reconfigure active flows to increase the network utilization, while also providing per-flow QoS guarantees during the transition to the new traffic plan. Additionally, we introduce a novel heuristic for computing the new traffic plans. Evaluations of our prototypical implementation show that we can efficiently compute new traffic plans in scenarios with hundreds of active flows for a wide range of scenarios.

Port Community Systems: Accelerating the Transition of Seaports toward the Physical Internet—The Portuguese Case

Supply chains are complex systems that have grown in dimension and spread worldwide. In supply chains, physical and information flows have strict service quality requirements, namely transparency conditions and traceability. Seaports, connecting land and maritime transport, are special components of supply chains where multiple players interact with different perspectives, and conflicting goals may arise. Port community authorities invest in electronic platforms to foster communication and integration with the companies that interact with the seaport, guiding the digitization of the seaport business. In main European and world ports, the Port Community System (PCS) is the platform that supports the creation of a network composed of shipping agents, shippers, freight forwarders, transporters, terminals, logistics platforms, and public entities. PCS focuses on service level, partner networks, maritime services, freight services, logistical services, and advanced port services. These features have an impact on seaport operations, which affects supply chain performance. Digitization within the scope of the PCSs has fostered the development of horizontal collaboration between seaport community partners. The Physical Internet (PI) is an innovative concept that seeks new logistics solutions requiring integration and interoperability between partners in the supply chain, including maritime and land transport. This paper focuses on (i) the evolution guidelines of PCSs and (ii) on the PCS Business Factors that can drive the supply chain into a significant improvement in performance. A survey was sent to a sample of Portuguese supply chain experts regarding the causal relationship between PCS Business Factors and supply chain performance in the next 10 to 20 years. From the data collected, recent services evolving at PCS are promoting a mindset change aligned to the implementation of a Physical Internet. Establishing a Physical Internal in Portuguese seaports could take decades, but it will support the transition to a new phase of PCS, accelerating the transition from Isolated Supply Chains (I-SCs) to Open Supply Chains (O-SCs), thus improving supply chain performance.

Interleaved Weighted Round-Robin: A Network Calculus Analysis

Weighted Round-Robin (WRR) is often used, due to its simplicity, for scheduling packets or tasks. With WRR, a number of packets equal to the weight allocated to a flow can be served consecutively, which leads to a bursty service. Interleaved Weighted Round-Robin (IWRR) is a variant that mitigates this effect. We are interested in finding bounds on worst-case delay obtained with IWRR. To this end, we use a network calculus approach and find a strict service curve for IWRR. The result is obtained using the pseudo-inverse of a function. We show that the strict service curve is the best obtainable one, and that delay bounds derived from it are tight (i.e., worst-case) for flows of packets of constant size. Furthermore, the IWRR strict service curve dominates the strict service curve for WRR that was previously published. We provide some numerical examples to illustrate the reduction in worst-case delays caused by IWRR compared to WRR.

A Preliminary Study on Strict and Speedy Customs Service for Aircraft Stores: A Comparative Study of Korea, Japan, and Hong Kong

A Preliminary Study on Strict and Speedy Customs Service for Aircraft Stores: A Comparative Study of Korea, Japan, and Hong Kong

A Cooperative Caching and Computing-Offloading Method for 3C Trade-Off in VR Video Services

Virtual reality (VR) video services are becoming increasingly popular in Internet of Things (IoT) owing to its immersive experience. The realization of immersive experience relies on extremely large amount of data transmitted, restricting its application. To meet the transmission requirement of VR video services, this paper designs a user-centric network based on the mobile edge computing (MEC) framework, where the 2D/3D field of view (FOV) files can be cached and the projection process from 2D FOV to 3D FOV can be computed at the VR device or MEC servers collaboratively. Based on this, a cooperative caching and computing-offloading strategy is formulated as a decision matrix, and its optimal closed-form expression is obtained by minimizing the transmission requirement under the strict service time constraint to realize the trade-off between communication, caching and computing (3C trade-off). Besides, the effect of the transmission requirement on the network density is also analyzed to provide useful guidelines for network design. Simulation results demonstrate that the proposed method can achieve quasi-optimal performance compared with other contrasting schemes.

Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Rational perspectives on risk and certainty for dentistry during the COVID-19 pandemic

Rational perspectives on risk and certainty for dentistry during the COVID-19 pandemic

Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G

This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

SDATP: An SDN-Based Traffic-Adaptive and Service-Oriented Transmission Protocol

In this paper, a software-defined networking (SDN) based adaptive transmission protocol (SDATP) is proposed to support applications in fifth generation (5G) communication networks. For time-critical services, such as machine-type communication services for industrial automation, high reliability and low latency are required. To guarantee the strict service requirements, a slice-level customized protocol is developed with in-network intelligence, including in-path caching-based retransmission and in-network congestion control. To further reduce the delay for end-to-end (E2E) service delivery, we jointly optimize the placement of caching functions and packet caching probability, which reduces E2E delay by minimizing retransmission hops. Since the joint optimization problem is NP-hard, we transform the original problem to a simplified form and propose a low-complexity heuristic algorithm to solve the simplified problem. Numerical results are presented to validate the proposed probabilistic caching algorithm, including its adaptiveness to network dynamics and its effectiveness in reducing retransmission hops. Simulation results demonstrate the advantages of the proposed SDATP over the conventional transport layer protocol with respect to E2E packet delay.

Repetition-Based Cooperative Broadcasting for Vehicular Ad Hoc Networks Under the Rayleigh Fading Channel

In vehicular ad hoc networks (VANETs), safety applications require a reliable and delay-guaranteed broadcasting service to disseminate safety messages. However, channel fading and the high mobility of vehicles make it very challenging for a broadcasting scheme to meet the strict service demands of safety messages. On the other hand, cooperative retransmission is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. Therefore, this paper proposes a repetition-based cooperative broadcasting (RBCB) scheme for safety messages in VANETs. The proposed scheme enables the selected helper vehicles to perform the cooperative rebroadcasting along with the source vehicle during the source vehicle’s slot in order to increase the transmission reliability of safety messages and reduce rebroadcasting times. The performance of RBCB scheme is mathematically analyzed in terms of packet delivery probability and average packet delay under the Rayleigh fading channel. Moreover, extensive simulations are conducted to evaluate the performance of RBCB scheme. Both mathematical analysis and simulation results demonstrate that RBCB scheme significantly improves the packet delivery probability and decreases the average packet delay.

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements.