DOSM: Demand-Prediction based Online Service Management for Vehicular Edge Computing Networks

We consider resource allocation for semi-elastic applications such as video conferencing. We propose to model user utility as a sigmoid function of the average bit rate over multiple time slots. The goal is to maximize the total expected user utility over time through allocation of downlink power and subcarriers in each time slot. We propose resource allocation that considers both the average rate achieved so far and the future expected rate, and show how the future expected rate can be estimated by modeling the probability that a user will be allocated a subcarrier in a future time slot. The algorithm can be implemented in a distributed fashion by an exchange of price and demand amongst users, the network, and an intermediate power allocation module. To reduce the complexity, we also propose a greedy algorithm to maximize incremental utility in the current time slot. The performance is illustrated using numerical results.

Several frameworks have been developed to assist the analysis of service management systems. However, most of these frameworks only capture limited aspects of a service-oriented enterprise. This paper first reviews several popular service management frameworks. An enterprise architecture approach that is commonly used in information systems planning has been adapted to integrate various service management frameworks and tools, as well as to support the planning and documentation of a service-oriented enterprise. The proposed service-oriented enterprise architecture (SOEA) includes the following aspects: service, process, people, information, and motivation. Each aspect can be viewed from a strategic, tactical, and operational level. The mapping of the conceptual constructs of existing service management frameworks to the proposed SOEA is discussed in detail.

Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations. However, some attacks assume full availability of the victim model, and some require a huge amount of computation, making them less feasible for real world applications. In this work, we make further explorations of the vulnerabilities of DRL by studying other aspects of attacks on DRL using realistic and efficient attacks. First, we adapt and propose efficient black-box attacks when we do not have access to DRL model parameters. Second, to address the high computational demands of existing attacks, we introduce efficient online sequential attacks that exploit temporal consistency across consecutive steps. Third, we explore the possibility of an attacker perturbing other aspects in the DRL setting, such as the environment dynamics. Finally, to account for imperfections in how an attacker would inject perturbations in the physical world, we devise a method for generating a robust physical perturbations to be printed. The attack is evaluated on a real-world robot under various conditions. We conduct extensive experiments both in simulation such as Atari games, robotics and autonomous driving, and on real-world robotics, to compare the effectiveness of the proposed attacks with baseline approaches. To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.

The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%.

The main objective of the Service Level Management process is to ensure that an agreed level of IT service is provided for all current IT services and that future services are delivered to agreed achievable targets. The number of agreed SLAs has rapidly increased in the IT industry while more and more IT customers have become aware of Service Level Management best practices. IT Infrastructure Library (ITIL) is the most widely used IT service management framework that provides best practices for implementing IT service management processes including Service Level Management. The research problem of this study is: how Service Level Agreements can be implemented with an IT service management tool. We use a combination of IT service management and knowledge management approach to study the problem. The main contribution of this paper is to present results of a case study that focused on improving Service Level Management practices in a Finnish IT service provider company Tieto.

A framework and associated metrics and measures that can be used to measure the performance of IT service management with an accompanying contingency theory of IT service management.

IT service catalogs are a pivotal element for managing IT services in many organizations, in accordance with common practice IT service management frameworks such as the IT Infrastructure Library. However, both in theory and practice, IT service catalogs are underspecified as much emphasis is put on the structure and content of the catalogs while the use and integration into IT service management processes are often neglected. Consequentially, the aim of the present paper is to propose a maturity model for IT service catalogs that covers structure and content as well as use and integration. This advances our understanding of this pivotal artifact in many IT service management systems and helps practitioners to identify improvement potentials for their IT service catalog-related practices.

Design automation is undergoing a new generation of changes caused by artificial intelligence technologies represented by deep learning and reinforcement learning. Notably, the advantages of deep reinforcement learning in addressing solution optimisation and decision-making tasks with cognitive automation functionality have garnered attention in design. In the context of surrogate model-driven engineering design optimisation, this paper addresses current research challenges such as reliance on domain knowledge for local development, shortcomings in the self-learning and adaptive capabilities of optimisation algorithms for global exploration, etc. Centred around the deep reinforcement learning model, Deep Q-learning, and complemented by self-organising maps and neural network technologies, we propose a methodology considering multi-fidelity simulation data for design space exploration. This approach effectively reduces sampling costs and enables the optimisation model to learn the optimal direction for high-precision predictions and achieve rapid, accurate optimisation. Finally, the effectiveness of the proposed method is comprehensively validated through four typical optimisation scenarios and a case study involving the optimisation of a wheeled robot's suspension swing arm structure. This work will be a crucial reference for applying deep reinforcement learning in simulation-driven engineering design optimisation.

IT service management and incident management is a hot topic in every company which serves IT services and they require human effort to manage. In ITIL framework for IT service management, it's always useful to link the incidents with configuration items, in other words the assets or components necessary to deliver IT services and this task is managed manually by IT support technicians in many IT service management tools. The aim of the study is to remove this manual linking step by applying text classification methods and instead to provide an automatic assignment of CI's to incidents. Four of the text classification methods, Naive Bayes Multinomial, k-Nearest Neighbor, Support Vector Machine and J48 decision tree classifiers, are used on three different sets of incidents extracted from the same database by using different filters. The impact of some pre-processing steps is compared on different sizes of datasets.

This paper investigates stock portfolios by application of Deep Reinforcement Learning (DRL) Models to achieve an optimal tactical asset allocation. The research problem is described as an optimization scenario that seeks to maximize the portfolio risk adjusted returns for a given portfolio asset allocation. The problem is set up with an initial capital investment which is invested in a set of assets. The initial strategic allocation is determined, which in our case is the equal weight allocation, and all of the capital is invested in the set of assets. At each point in time, the assets are reallocated according to the allocation which will increase the portfolio value. Two DRL models are implemented. The performance of the DRL models is compared with the uniform weights portfolio. The results show that, generally, two DRL models have higher cumulative returns.

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure service offloading method, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

In this paper we discuss existing frameworks for the management of ICT services and their limitations in the context of emerging enterprise computing environment characterized by use of externally sourced services. We identify the requirements for a service management framework with particular focus on definition and categorization of ICT services that facilitates the development of a service catalogue. The main section of this paper describes our approach to ICT service management as embodied in the SPSPR framework.

The increasing demand for fast, reliable, and robust network services has driven the telecommunications industry to design novel network architectures that employ Network Functions Virtualization and Software Defined Networking. Despite the advancements in cellular networks, there is a need for an automatic, self-adapting orchestrating mechanism that can manage the placement of resources. Deep Reinforcement Learning can perform such tasks dynamically, without any prior knowledge. In this work, we leverage a Deep Deterministic Policy Gradient Reinforcement Learning algorithm, to fully automate the Virtual Network Functions deployment process between edge and cloud network nodes. We evaluate the performance of our implementation and compare it with alternative solutions to prove its superiority while demonstrating results that pave the way for Experiential Network Intelligence and fully automated, Zero touch network Service Management.

Avionics system consists of a series of determined hardware and software components, providing computing, com-munication and other services for the system, as well as con-nection interfaces for connecting with peripheral devices to fully realize the various functions specified in the requirements. In order to solve the problems that barriers exist between avionics resources, resources are not managed in a unified manner and cannot be allocated flexibly. In this paper, we propose a three-layer cross-platform service management framework for avionics systems, which hides the heterogeneity of the hardware and the data. We develop relevant standard specifications and carry out research on service modelling for multi-platform avionics systems, build a standardized encapsulation method for services and a mechanism for service combination, and develop relevant service models. This service management framework integrates distributed combat resources and achieves reasonable scheduling of resources and services based on scheduling algorithms to accomplish various complex tasks. The paper concludes by testing the framework functionality and performance to show that the framework can be used for service management of avionics systems.

The ETSI's Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM's potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security.