Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing

Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’

A Reinforcement Learning Based Model for Adaptive ServiceQuality Management in E-Commerce Websites

Providing high-quality service to all users is adifficult and inefficient strategy for e-commerce providers,especially when Web servers experience overload condi-tions that cause increased response time and requestrejections, leading to user frustration and reduced revenue.In an e-commerce system, customer Web sessions havediffering values for service providers. These tend to: givepreference to customer Web sessions that are likely tobring more profit by providing better service quality. Thispaper proposes a reinforcement-learning based adaptivee-commerce system model that adapts the service qualitylevel for different Web sessions within the customer’snavigation in order to maximize total profit. The e-com-merce system is considered as an electronic supply chainwhich includes a network of basic e- providers used tosupply e-commerce services for end customers. The learneragent noted as e-commerce supply chain manager(ECSCM) agent allocates a service quality level to thecustomer’s request based on his/her navigation pattern inthe e-commerce Website and selects an optimized combi-nation of service providers to respond to the customer’srequest. To evaluate the proposed model, a multi agentframework composed of three agent types, the ECSCMagent, customer agent (buyer/browser) and service provideragent, is employed. Experimental results show that theproposed model improves total profits through costreduction and revenue enhancement simultaneously andencourages customers to purchase from the Websitethrough service quality adaptation

Scheduling in cloud and fog architecture: identification of limitations and suggestion of improvement perspectives

Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.Calouste Gulbenkian Foundation, PhD scholarship No.234242, 2019.info:eu-repo/semantics/publishedVersio

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate

Can we Take User Responses at Face Value? Exploring Users’ “Self-stated” and “Derived” Importance of Utilitarian versus Hedonic Software Features

Empirical studies in the product development literature have shown that the users’ self-reported importance of product attributes differs from the derived importance of product attributes obtained through the attributes’ correlation with an external criterion such as user satisfaction. However, no study has examined this phenomenon in the context of software products. This investigation is important because the present-day software requirement-prioritization techniques are based on capturing users’ self-reported importance of new software product features. As such, I develop a method in the study to capture the derived user importance of new features. The findings show that the implicitly derived importance of software attributes differs from the importance rankings assigned to them using requirement prioritization techniques. Further, I found that the implicitly derived user importance to identify the determinants of user satisfaction more accurately than the prioritization techniques based on self-stated user importance. I discuss the implications of this promising new approach for practice and future research in requirements prioritization

In-depth assessment of the public agricultural extension system of Ethiopia and recommendations for improvement:

Eighty-three percent of the population of Ethiopia depends directly on agriculture for their livelihoods, while many others depend on agriculture-related cottage industries such as textiles, leather, and food oil processing. Agriculture contributes about 46.3 percent of gross domestic product (GDP) (World Bank 2008) and up to 90 percent of total export earnings. As part of the current five-year (2006–2011) Plan for Accelerated and Sustained Development to End Poverty (PASDEP), the government is continuing to invest heavily in agriculture. A core part of the government's investment in agriculture is the public agricultural extension system. This study was conducted to assess the strengths and constraints of the public extension system and to provide suggestions on “best fit” solutions and their scale-up opportunities. The review used a variety of analytical tools to develop the overall findings, including extensive field visits to six of nine regions in Ethiopia; interviews with farmer trainees at farmer training centers (FTCs), more than 100 extension personnel, extension experts, nongovernmental organization (NGO) groups, and government representatives; and a literature review on Ethiopian extension. The study assessed strengths and constraints in the field-level extension system, the ATVET system, and the extension institutional environment. The researchers also considered the overall enabling environment within which extension operates. The field-level extension service has a strong foundation of FTCs and trained development agents (DAs) already in place in the field. Roughly 8,489 FTCs have been created throughout Ethiopia, and about 62,764 DAs have been trained in total, with a reported 45,812 staffed on location. Woreda (district) and regional offices are adequately staffed. DAs and woreda staff have strong technical skills and theoretical knowledge, and are generally trained as specialists. Pockets of entrepreneurialism and innovation exist in specific FTCs and woredas. While acknowledging these strengths, the researchers also identified several sets of constraints within the field-level extension system that will require attention. Basic infrastructure and resources at the FTC and woreda level remain a major constraint, particularly in relation to operating funds: the vast majority of FTCs and kebeles do not have operating equipment or inputs to pursue typical extension activities on the demonstration farm. There are major “soft” skill gaps for DAs and subject matter specialists (SMSs) in the FTCs and woredas, and their ability to serve farmers is limited given a lack of practical skills. Finally, the overall field-level system is often limited in its ability to meet farmer needs and demands; mechanisms to make it more farmer-driven and market-oriented would yield greater results. The authors employed a similar approach at the ATVET level to identify strengths and constraints. Strengths at the ATVET level include a strong record of training broad groups of DAs, a strong technical curriculum, and some pockets of innovation and practical training, including linkages to markets and farmers. Constraints include limited success in enabling DAs to gain practical experience, particularly related to their internships at the woreda level; limited linkages to the broader educational system and research system in Ethiopia; and a general lack of resources to effectively transmit the required skill set to DAs. The countrywide enabling environment in which extension operates is critical to extension efforts. Various aspects of the enabling environment were considered, including seed and other inputs, water management, and credit systems, as well as producer groups. Constraints were also assessed, leading to the conclusion that the enabling environment requires strengthening, particularly in the areas of seed and credit, if extension is to achieve its full potential impact.trained development agents (DA), farmer training centers (FTC), ATVET system, Extension, Agriculture,

PriorityNet App: A mobile application for establishing priorities in the context of 5G ultra-dense networks

The devices and implementations of 5G networks are continuously improving, and people will probably use them daily in the near future. 5G networks will support ultra-dense networks. In the literature, several works apply 5G networks in smart cities and smart houses. One of the most common features of these works is to use priorities in tasks, such as the management of electrical consumption at houses, waste collection in cities, or pathfinding in self-driving cars. The proper management of priorities facilitates that urgent service requests are rapidly attended. However, to the best of our knowledge, the literature lacks appropriate mechanisms for considering users’ priorities in the 5G ultra-dense networks. In this context, we propose a mobile application that allows citizens to request smart city services with different priority levels. The experiments showed the high performance of the app and its scalability when increasing priority list sizes. This app obtained 72.3% of usability in the system usability scale and 82.9% in the ease-of-use dimension of the usefulness, satisfaction, and ease of use questionnaire

Some topics in web performance analysis

This thesis consists of four papers on web performance analysis. In the first paper we investigate the performance of overload control through queue length for two different web server architectures. The simulation result suggests that the benefit of request prioritization is noticeable only when the capacities of the sub-systems match each other. In the second paper we present an M/G/1/K*PS queueing model of a web server. We obtain closed form expressions for web server performance metrics such as average response time, throughput and blocking probability. The model is validated through real measurements. The third paper studies a queueing system with a load balancer and a pool of identical FCFS queues in parallel. By taking the number of servers to infinite, we show that the average waiting time for the system is not always minimized by routing each customer to the expected shortest queue when the information used for decision is stale. In the last paper we consider the problem of admission control to an M/M/1 queue under periodic observations with average cost criterion. The problem is formulated as a discrete time Markov decision process whose states are fully observable. A proof of the existence of the average optimal policy by the vanishing discounted approach is provided. We also show that the optimal policy is nonincreasing with respect to the observed number of customers in the system