Quality Of Service Loss Research Articles

Achieving local differential location privacy protection in 3D space via Hilbert encoding and optimized random response

The widespread use of spatial location-based services not only provides considerable convenience, but also exposes the downsides of location privacy leakage. Most of the existing user-side location privacy protection techniques are limited to planar locations. However, the extensive use of aircraft, sensor equipment and acquisition devices with positioning functions promotes the urgency of protecting the privacy of 3D spatial locations. Therefore, this study suggests a local differential privacy protection approach for 3D spatial locations. A 3D spatial decomposition and Hilbert encoding method are designed to reduce the 3D location data into one-dimensional encoding. The optimized random response mechanism was utilized to perturb the dimensional-reduced location encoding, which not only achieves user-side location privacy protection but also improves the accuracy of aggregated data on the server-side. Experiments on the real spatial location datasets show that the suggested method can reduce spatial location service quality loss, maintain the availability of perturbed spatial location and improve the operation efficiency of the spatial location perturbation algorithm.

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

PROACTIVE HORIZONTAL SCALING METHOD FOR KUBERNETES

Context. The problem of minimizing redundant resource reservation while maintaining QoS at an agreed level is crucial for modern information systems. Modern information systems can include a large number of applications, each of which uses computing resources and has its own unique features, which require a high level of automation to increase the efficiency of computing resource management processes.
 Objective. The purpose of this paper is to ensure the quality of IT services at an agreed level in the face of significant dynamics of user requests by developing and using a method of proactive automatic application scaling in Kubernetes.
 Method. This paper proposes a proactive horizontal scaling method based on the Prophet time series prediction algorithm. Prometheus metrics storage is used as a data source for training and validating forecasting models. Based on the historical metrics, a model is trained to predict the future utilization of computation resources using Prophet. The obtained time series is validated and used to calculate the required number of application replicas, considering deployment delays.
 Results. The experiments have shown the effectiveness of the proposed proactive automated application scaling method in comparison with existing solutions based on the reactive approach in the selected scenarios. This method made it possible to reduce the reservation of computing resources by 47% without loss of service quality compared to the configuration without scaling.
 Conclusions. A method for automating the horizontal scaling of applications in Kubernetes is proposed. Although the experiments have shown the effectiveness of this solution, this method can be significantly improved. In particular, it is necessary to consider the possibility of integrating a reactive component for atypical load patterns.

Quality of Service Analysis on EaTrain Application at A Restaurant on A Train

One of the services on the train is a dining car that sells food and drinks. However, the current system has problems such as the unclear position and operating hours of the dining car, the lack of menu information, and the difficulty of officers in giving change. The purpose of this research is to make it easier for passengers to order food and drinks, increase the officer’s efficiency in managing menu, and determine the network quality while using the EaTrain application on the train. The EaTrain application was built using android studio with cloud firestore as a database for storing data, and integrated with midtrans payment gateway in making payments. Wireshark software is used to measure the network quality of EaTrain application in train restaurant. The test results show that the EaTrain application can run well on Android 7.0 devices and above. The value of delay, throughput, and packet loss in Quality of Service (QoS) testing while making payments is determined by network quality of each provider. Provider A has average delay of 123ms, packet loss is 10.72%, and throughput is 3974 bps. While provider B has average delay at 1343ms, packet loss is 50.88%, and throughput is 33628 bps.

Effect of Perceived Organisational Support, Perceived Co-Worker Support and Work-Family Conflict on Turnover Intention Among Nurses in Bangladesh

Nurses are the largest occupational group responsible for delivering high-quality patient care. However, a higher nurses’ turnover rate negatively affects the nurses’ morale and quality of care and increases the workload of remaining nurses, resulting in more nurses’ turnover. In Bangladesh, hospitals do not have enough nurses. The nurses’ turnover rate (15-18%) will lead to substantial financial and service quality loss. The nurses’ turnover rate is approximately 17% in Bangladesh. Therefore, this study aims to identify how to mitigate turnover intention among nurses in the healthcare sector in Bangladesh. Still, very little research has been conducted so far on the linkage between perceived organisational support, perceived co-worker support and turnover intention. The findings of these variables are inconclusive, necessitating further research. Moreover, most of the research so far has been conducted in Western settings. However, the relationship has yet to be thoroughly examined in the emerging Asian context, especially in Bangladesh. In addition, previous studies have overlooked the mediating effect of work-family conflict in the relationship between perceived organisational support, perceived co-worker support and turnover intention. The proposed research framework is supported by social exchange theory and conservation of resources theory.

Reputation-Aware Supplier Assessment for Blockchain-Enabled Supply Chain in Industry 4.0

In Industry 4.0, the development of intelligent supply chains is the key to improving enterprise efficiency and customer satisfaction. At present, the strategy with blockchain to optimize the supply chain ensures the security of data sharing and interaction between different suppliers. However, the quality of service (QoS) loss in time efficiency and supplier cooperation is still a severe problem in these strategies. Therefore, it is essential to conduct reputable supplier selection to reduce QoS loss under the premise of blockchain security. Meanwhile, choosing a reputable supplier as the primary peer in the blockchain consensus process can also effectively reduce transaction latency. To solve this problem, a reputation-aware supplier assessment system, named SAS, is proposed. Specifically, Canopy and K-medoids are utilized to classify suppliers preliminarily. Then backpropagation neural network is deployed to evaluate supplier reputation. The results show that SAS is effective in supplier reputation evaluation through extensive experiments.

SYSTEM ANALYSIS OF QUALITY ASSURANCE OF CUSTOMS AND LOGISTICS SERVICE OF GOODS OF DIFFERENT NOMENCLATURE DURING INTERNATIONAL TRANSPORTATION

Aim. The study intends to optimize customs clearance formalities for goods of various nomenclature in the territory of customs checkpoints of freight customs complexes. The proposed development will allow customs authorities to plan the staffing level of inspectors capable of ensuring the uninterrupted operation of the infrastructure facility, taking into account the utilized capacity and the cargo flow structure. Methods. The quality assessment of the customs and logistics service of goods of various nomenclature in the organization of international transportation is carried out on the basis of a simulation model developed in the GPSS software environment. Results. The practical experience of customs authorities shows that the customs regime in which the goods are moved and their nomenclature bring about changes in how long customs formalities take. Accordingly, the procedures related to customs clearance and customs control by the types of goods will differ in their complexity, and therefore will affect the customs inspector`s workload. In turn, customs authorities` working efficiency will shape the capacity of the freight customs complex and its structural subdivisions. The main segment of customs and logistics service customers chooses infrastructure facilities, where there are small waiting lines and minimal procedure time without the loss of service quality. Scientific novelty. The developed simulation model of customs inspectors` work in the territory of the freight customs complex makes it possible to evaluate the effectiveness of the customs checkpoint as well as to determine the optimal staffing level to ensure the operation of the infrastructure facility. Practical implications. The practical implications of the work lie in the fact that the proposed model makes it possible to assess customs inspectors` workload taking into account the structure of the cargo flow, which may include goods of different nomenclature that are moved in different customs regimes by large-, medium-, small- and micro-sized enterprises. Based on the results of the simulation, the owner of a freight customs complex will have the opportunity to plan its material, technical and staffing support in order to provide high-quality customs and logistics services, and accordingly, the State Customs Service will organize the work of the customs checkpoint taking into account the demand of entities engaged in foreign economic activities for completing customs formalities.

Ідентифікація ризиків процесів системи управління якістю калібрувальної лабораторії

The article is devoted to research on the introduction of a risk-oriented approach in the processes of the management system and the activities of calibration laboratories. The issues of definition, identification and processing of risks (opportunities) arising in the process of ensuring the functioning of calibration laboratories management system are considered. The article substantiates approaches to the identification of innovative risks of calibration laboratories in accordance with the requirements of international standards that regulate the requirements for the activity of testing and calibration laboratories. Risk management activities cover various aspects of working with risks, from the identification and analysis of risks and opportunities to their acceptability assessment, determination of potential opportunities for risk reduction through the selection, implementation and control of appropriate management actions. The issue of identifying metrological risks is particularly relevant, as the main risks that determine the degree of controllability of calibration processes, the level of control of the accuracy of the results of these processes, and, therefore, the costs of their provision. The main goal of identifying metrological risks is to systematize possible inconsistencies that may arise during measurements and create conditions for ranking the processes of metrological activity of testing and calibration laboratories according to the level of loss of service quality due to the uncertainty of measurements. The implementation of modern metrological risk management systems will contribute to increasing the effectiveness of measurement systems and reducing costs while ensuring the quality of metrological services. For the effective application of the metrological risk management system, it is necessary to create a list of indicators of metrological risks, as well as the appropriate regulatory support for their justified application in quality management systems. Actual problems in the metrological sphere of the enterprise operation are identification, analysis and reduction of risks for the processes of the quality management system in the calibration laboratory.

Towards cost-effective and robust AI microservice deployment in edge computing environments

As a newly emerged promising computing paradigm, Multi-access Edge Computing (MEC) is capable of energizing massive Internet-of-Things (IoT) devices around us and novel mobile applications, especially the computing-intensive and latency-sensitive ones. Meanwhile, featured by the rapid development of cloud-native technologies in recent years, delivering Artificial-Intelligence (AI) capabilities in a microservice way in the MEC environments comes true nowadays. However, currently MEC systems are still restricted by the limited computing resources and highly dynamic network topology, which leads to high service deployment/maintenance cost. Therefore, how to cost-effectively and robustly deploy edge AI microservices in failure-prone MEC environments has become a hot issue. In this study, we consider an edge AI microservice that can be implemented by composing multiple Deep Neural Networks (DNN) models, in this way, features of different DNN models are aggregated and the deployment cost can be further reduced while fulfilling the Quality-of-Service (QoS) constraint. We propose a Three-Dimension-Dynamic-Programming-based algorithm (TDDP) to yield cost-effective multi-DNN orchestration and load allocation plans. For the robust deployment of the yield orchestration plan, we also develop a robust microservice instance placement algorithm (TLLB) by considering the three levels of load balance including applications, servers, and DNN models. Experiments based on real-world edge environments have demonstrated that the proposed orchestration and placement methods can achieve lower deployment costs and less QoS loss when faced with edge node failures than traditional approaches.

A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

Reinforcement learning-based sensitive semantic location privacy protection for VANETs

Location-based services (LBS) in vehicular ad hoc networks (VANETs) must protect users' privacy and address the threat of the exposure of sensitive locations during LBS requests. Users release not only their geographical but also semantic information of the visited places (e.g., hospital). This sensitive information enables the inference attacker to exploit the users' preferences and life patterns. In this paper we propose a reinforcement learning (RL) based sensitive semantic location privacy protection scheme. This scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack history. This scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service (QoS) loss without being aware of the current inference attack model in a dynamic privacy protection process. To solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables, a deep deterministic policy gradient-based semantic location perturbation scheme (DSLP) is developed. The actor part is used to generate continuous privacy budget and perturbation angle, and the critic part is used to estimate the performance of the policy. Simulations demonstrate the DSLP-based scheme outperforms the benchmark schemes, which increases the privacy, reduces the QoS loss, and increases the utility of the vehicle.

What prevents consumers that are aware of Airbnb from using the platform? A mixed methods approach

The hospitality sector is now characterised by the co-existence of traditional providers and sharing economy enterprises. It is important to better understand what prevents the use of peer-to-peer accommodation rentals in the tourism marketplace. Adopting a mixed-methods approach, this study first examines travellers’ Airbnb use constraints, and then profiles 252 Airbnb non-users based on their constraints and characteristics. The findings suggest that travellers do not use Airbnb because of distrust in the providers, in the platform and in other users, as well as perceived risk and unfamiliarity. Loss of service quality, lack of local experience, legal and regulatory issues, and disinterest also prevented consumers to book Airbnb accommodations. A cluster analysis divided non-users into three segments: Traditional travellers, Sharing economy misbelievers and Airbnb prospective users. Significant differences based on age confirmed that young travellers are more confident in using peer-to-peer accommodation platforms and more interested in the philosophy.

Real-Time QoS Optimization for Vehicular Edge Computing With Off-Grid Roadside Units

To sustainably provide low-latency communication and edge computing for connected vehicles, a promising solution is using Solar-powered Roadside Units (SRSUs), which consist of small cell base stations and Mobile Edge Computing servers. However, due to the intermittent nature of solar power, SRSUs may suffer from a high risk of power deficiency, which will lead to severe disruption of vehicular edge computing applications. In this paper, we aim to address this challenge of Quality of Service (QoS) loss (i.e., edge computing service outage for vehicle users (VUs)). We formulate a QoS optimization problem for VUs and solve it in two phases: an offline solar energy scheduling phase, and an online user association and SRSU resource allocation phase. We simulate our proposed technique in a dense SRSU network environment with real-world urban vehicular traffic data and solar generation profile. The simulation results show that our proposed approach can significantly reduce QoS loss of vehicular edge computing applications using SRSUs, compared to existing techniques. Further, the results are beneficial to service providers and city planners to identify adequate SRSU configurations for expected solar energy generation and edge computing service demands.

A modified quality loss model of service life prediction for products via wear regularity

It is very important to consider the life change regularity of products during full life-cycle based on product geometric quality characteristics. The relationship between product quality loss and service life distribution is analyzed to estimate the average life of products during service time, and the range of concentrated distribution of product service life is determined. To estimate the quality loss of products more accurately, the viewpoint is put forward that the main quality characteristic values should obey the nonlinear change during service time. Then, the service quality loss function (QLF) is modified based on the wear regularity. The effect of different parameters on the average service life of products is discussed with practical case, such as design, production and service parameters. A new research method is proposed on the full life-cycle variation rule of mechanical products.

A Novel Method for Location Privacy Protection in LBS Applications

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss Lmax, which is the user’s tolerance. Lmax can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.

Optimal pricing strategies for delivery alliance in online retailing platform

PurposeNowadays, some online retailing platforms emerge to integrate transport capacity to provide standard distribution service for sellers. Such an integrated form of service is defined as delivery alliance (DA). To have a better understanding of how to price the service, this study aims to fixate on the seller’s problems and builds a series of profit maximization models in accordance with the two-sided market pricing theory within a platform business model.Design/methodology/approachIn the present study, some optimization models are built in the two-sided market type and the optimal solutions are found in a three-dimensional decision space. By using the basic model as the benchmark, some optimization problems of DA in realistic situations are discussed. Particularly, a power-law-distribution model is established to deal with the uncertainty in forecasting. Also, a price-sensitive model and a loss-aversion model are presented to describe the various reactions of sellers to charging modes. Finally, some combined situations are discussed and the strategies are compared under the mentioned models.FindingsBy selecting the basic model as the benchmark, the specific pricing strategies are found for each context to yield the optimal profits. The flexibility of pricing strategy in the basic model and rigid pricing strategies in extended models, are discussed. As a result, the guidelines for the online retailing platforms are developed on designing and pricing the DA service.Research limitations/implicationsFirst, it would be interesting to expand the pricing plan of the platform. For instance, menu pricing and quantity discount have not been considered, which are common in practice. The time discounting has also been ignored. If the time value were calculated, the contract fees would be more critical due to the earliest of collecting money. Finally, those joiners who have huge order sizes are crucial for the ecosystem indeed, but arouse no attention. While in reality, they may have more power to bargain with the platform. Thus, how the platform competition affects the pricing strategies needs future research.Originality/valueThe optimal pricing strategies under these models are analytically found out, and it is shown that the presented models result in the same scale of joiners and profits in optimization. This suggests that DA works well in various behavioral contexts. This also suggests that DA is a significant controller in service quality improvement. Then, the optimal pricing strategies are compared among all the models. During this, it is discovered that the realistic contexts might reduce the profit, whereas an appropriate pricing strategy can pull this back without loss of service quality.

Interval-based resource usage verification by translation into Horn clauses and an application to energy consumption

AbstractMany applications require conformance with specifications that constrain the use of resources, such as execution time, energy, bandwidth, etc. We present a configurable framework for static resource usage verification where specifications can include data size-dependent resource usage functions, expressing both lower and upper bounds. Ensuring conformance with respect to such specifications is an undecidable problem. Therefore, to statically check such specifications, our framework infers the same type of resource usage functions, which safely approximate the actual resource usage of the program, and compares them against the specification. We review how this framework supports several languages and compilation output formats by translating them to an intermediate representation based on Horn clauses and using the configurability of the framework to describe the resource semantics of the input language. We provide a detailed formalization and extend the framework so that both resource usage specification and analysis/verification output can include preconditions expressing intervals for the input data sizes for which assertions are intended to hold, proved, or disproved. Most importantly, we also extend the classes of functions that can be checked. We also report on and provide results from an implementation within the Ciao/CiaoPP framework, as well as on a practical tool built by instantiating this framework for the verification of energy consumption specifications for imperative/embedded programs. Finally, we show as an example how embedded software developers can use this tool, in particular, for determining values for program parameters that ensure meeting a given energy budget while minimizing the loss in quality of service.

Time-dependent load-balancing service degradation in optical data center networks

Optical data center networks (ODCNs) are constructed by connecting geographically distributed data centers with optical networks. Due to the superiorities in computing and transmission performances, ODCNs have been widely acknowledged as a promising network paradigm to support the popular enterprise IT applications such as cloud computing and 3D rendering. However, the ever-increasing traffic burst and the expensive hardware upgrading impose more severe challenges on the design of resource allocation scheme in ODCNs, which is urgently required to take into account not only the service quality but also the economical profit of service provider. In this paper, we focus on the service degradation in ODCNs which has been proven efficient in reducing the denied requests in the overloaded network scenario by releasing portion of the occupied network resources with the acceptable service quality loss. Unlike the service degradation scheme in previous works, we first consider the impact of the time characteristics of service requests on the resource utilization and propose a novel time-dependent load-balancing service degradation (TD-LBSD) framework. In the TD-LBSD framework, a time-dependent link weight scheme is invented for the allocation of bandwidth resource to avoid the potential traffic congestion that is usually induced by the ignored dynamic nature of service requests. In order to support the high-level prepaid requests with unknown holding time, we also propose a statistical method to estimate the residual time of the occupied lightpaths based on the probability distribution of the holding time of past requests. Simulation results verified that our proposed service degradation framework outperforms the previous schemes with the profit gain of 6.2% and the reduced traffic congestions of 16.39% at best.

Modeling and QoS analysis of the IEEE 802.11p broadcast scheme in vehicular ad hoc networks

Quality of service (QoS) and queue management are critical issues for the broadcast scheme of IEEE 802.11p systems in vehicular ad hoc networks (VANETs). However, existing 1- dimensional (1-D) Markov chain models of 802.11p systems are unable to capture the complete QoS performance and queuing behavior due to the lack of an adequate finite buffer model. We present a 2-dimensional (2-D) Markov chain that integrates the broadcast scheme of the 802.11p system and the queuing process into one model. The extra dimension, which models the queue length, allows us to accurately capture the important QoS measures, delay and loss, plus throughput and queue length, for realistic 802.11p systems with finite buffer under finite load. We derive a simplified method to solve the steady state probabilities of the 2-D Markov chain. Our 2-D Markov chain model is the first finite buffer model defined and solved for the broadcast scheme of 802.11p systems. The 2-D model solutions are validated by extensive simulations. Our analyses reveal that the lack of binary exponential backoff and retransmission in the 802.11p system results in poor QoS performance during heavy traffic load, particularly for large VANETs. We demonstrate that our model provides traffic control guidelines to maintain good QoS performance for VANETs.

MILENA: a model for implementing distributed multiplayer games in computing clouds

Cloud computing environments are characteristically composed of distributed infrastructure, heterogeneous and virtualized resources, besides serving concurrently a wide range of customers whose service level agreements may require different levels of Quality of Service (QoS). Distributed multiplayer games, in turn, are collaborative distributed applications that must have an acceptable level of QoS to maintain justice among its players. This article proposes MILENA, a model for implementing distributed multiplayer games on computer clouds. The proposed model meets the requirements of QoS, fault tolerance and scalability. As a case study for verification and validation of the proposal, we have developed a game and evaluated its performance by SLA violations and response time of requests metrics. The results show that the MILENA scales up to 100 players without QoS loss, thus improving justice in distributed multiplayer games.