Popularity-Aware Service Provisioning Framework in Cloud Environment

The widespread adoption of 5G cellular technology is becoming the major driver for the growth of IoT-based applications. In this paper we consider a Mobile Service Provider (MSP) that launches a smart city service based on IoT data readings. In order to serve IoT data collected across different locations, the MSP dynamically negotiates and rescales bandwidth and service functions. 5G network slicing functions are key to lease appropriate amount of resources over heterogeneous access technologies and different site types. Also, different infrastructure providers will charge slicing service depending on specific access technology supported across regional sites and IoT data collection patterns.We introduce a pricing mechanism based on Age of Information (AoI) to reduce the cost of MSPs. It provides incentives for devices to smooth traffic by shifting part of the traffic load from highly congested and more expensive locations to lesser charged ones, while meeting QoS requirements of the IoT service. The proposed optimal pricing scheme comprises a two-stage decision process, where the MSP determines the pricing of each location and devices schedule uploads of collected data based on the optimal uploading policy. Simulations show that the MSP attains consistent cost reductions tuning the trade-off between slicing costs and the AoI of uploaded IoT data.

Dynamic principal agent models are formulated based on constrained Markov decision process (CMDP), in which conditions are given that the state space of the system is countable and the agent chooses his actions from a countable action set. If the principal has finite alternative contracts to select, it is shown that the optimal contract solution and the corresponding optimal policy can be obtained by linear programming under the discounted criterion and average criterion.

Service providers and enterprises all over the world are rapidly deploying Voice over IP (VoIP) networks because of reduced capital and operational expenditure, and easy creation of new services. Voice traffic has stringement requirements on the quality of service, like strict delay and loss requirements, and 99.999% network availability. However, IP networks have not been designed to easily meet the above requirements. Thus, service providers need service quality management tools that can proactively detect and mitigate service quality degradation of VoIP traffic. In this paper, we present active and passive probes that enable service providers to detect service impairments. We use the probes to compute the network parameters (delay, loss and jitter) that can be used to compute the call quality as a Mean Opinion Score using a voice quality metric, E-model. These tools can be used by service providers and enterprises to identify network impairments that cause service quality degradation and take corrective measures in real time so that the impact on the degradation perceived by end-users is minimal

Network virtualization technology is generally envisaged as a promising technology to consequently satisfy various types of service requirements. On the other hand, non-orthogonal multiple access (NOMA) technology has the potential to significantly increase the spectral efficiency of the system. However, previous works that jointly address these two issues have not considered the dynamic resource allocation issue in this context. In this paper, we propose a slice-based virtual resources scheduling scheme with NOMA technology to enhance the quality-of-service (QoS) of the system. We formulate the power granularity allocation and subcarrier allocation strategies into a constrained Markov decision process problem, aiming at the maximization of the total user rate. In order to further avoid the curse of dimensionality and the expectation calculation in the optimal value function, we develop an adaptive resource allocation algorithm based on approximate dynamic programming to solve the problem. Extensive simulation works have been conducted under various system settings, and the results demonstrate that the proposed algorithm can significantly reduce the outage probability and increase the user data rate.

We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments. In this problem, the reward/utility functions and the state transition functions are both allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain known variation budgets. Designing safe RL algorithms in time-varying environments is particularly challenging because of the need to integrate the constraint violation reduction, safe exploration, and adaptation to the non-stationarity. To this end, we identify two alternative conditions on the time-varying constraints under which we can guarantee the safety in the long run. We also propose the Periodically Restarted Optimistic Primal-Dual Proximal Policy Optimization (PROPD-PPO) algorithm that can coordinate with both two conditions. Furthermore, a dynamic regret bound and a constraint violation bound are established for the proposed algorithm in both the linear kernel CMDP function approximation setting and the tabular CMDP setting under two alternative conditions. This paper provides the first provably efficient algorithm for non-stationary CMDPs with safe exploration.

In cooperative spectrum sharing networks, dynamic spectrum access is regraded as an important and challenging issue. In this paper, we consider a non-real-payoff cooperative spectrum sharing between one primary user (PU) and one secondary user (SU) over multiple time slots where the channel conditions are changeable due to the channels fading. However, the real-payoff means is that the cooperative spectrum sharing between the PU and the SU is over a time slot where the channel conditions are constant. There exists three access means: the PU's access including the direct and the relay links, and the SU's access. We propose the access decision algorithm considering the access reward and the switching cost for the non-real-payoff means. Herein, the access reward depends on the system's capacity based on the links' channel conditions and the switching cost represents the overheads such as feedbacks and signallings in the communication networks. The access decision problem is formulated as a constrained Markov decision process (CMDP). The objective is to maximize the overall reward with the access cost constraints including the users' energy constraints and the PU's quality of service (QoS) constraints. The value iteration and Q-learning algorithms are used to determine the optimal access policy.

Linear goal programming in estimation of classification probability

As an important part of the Internet of Things (IOT) and the special case of device-to-device (D2D) communication, wireless body area network (WBAN) gradually becomes the focus of attention. Since WBAN is a body-centered network, the energy of sensor nodes is strictly restrained since they are supplied by battery with limited power. In each data collection, only one sensor node is scheduled to transmit its measurements directly to the access point (AP) through the fading channel. We formulate the problem of dynamically choosing which sensor should communicate with the AP to maximize network lifetime under the constraint of fairness as a constrained markov decision process (CMDP). The optimal lifetime and optimal policy are obtained by Bellman equation in dynamic programming. The proposed algorithm defines the limiting performance in WBAN lifetime under different degrees of fairness constraints. Due to the defect of large implementation overhead in acquiring global channel state information (CSI), we put forward a distributed scheduling algorithm that adopts local CSI, which saves the network overhead and simplifies the algorithm. It was demonstrated via simulation that this scheduling algorithm can allocate time slot reasonably under different channel conditions to balance the performances of network lifetime and fairness.

Cloud computing gradually becomes an issue for modern IT business because of its flexibility, convenience, and low cost. It provides resources with guaranteeing QoS(Quality of Service) to cloud users and revenues to service providers. Due to the dynamics of user demands, service providers need VM(Virtual Machine) provisioning mechanism to predict the amount of resources demanded by cloud users and to estimate the amount of the resources prepared by service providers in the near future. In this paper, a new VM provisioning mechanism is proposed to elastically estimate the amount of resource to be prepared. We present an optimization model of the estimation by maximizing a provider's revenue while satisfying users' QoS requirements and minimizing the penalty for SLA violation. To evaluate the effectiveness of our solution, we consider many practical situations of a cloud system with a series of real trace data. It shows that our model elastically adapts to the dynamic environment while maximizing the revenue of a service provider.

Motivated by the urgent demand for the electric vehicle (EV) fast refueling technologies, battery swapping and charging stations (BSCSs) are envisioned as a promising solution to provide timely EV refueling services. However, inappropriate battery charging operation in BSCSs cannot only incur unnecessary high charging cost but also threaten the reliability of the power grid. In this paper, we aim at obtaining an optimal charging operation policy for a single BSCS to minimize its charging cost while ensuring its quality-of-service. Leveraging the novel queueing network model, we propose to formulate the charging operation problem as a constrained Markov decision process and derive the optimal policy by the standard Lagrangian method and dynamic programming. To avoid the curse of dimensionality in practical large-scale systems, we further analyze the structure of the optimal policy and transform the dynamic programming procedure into an equivalent threshold optimization problem with a discrete separable convex objective function. Numerical results validate our theoretical analysis and the computational efficiency of our proposed algorithms. This paper also shows the impact of the system parameters (e.g., numbers of batteries and chargers) on the average cost under the optimal charging policy, which gives rich insights into the infrastructure planning of future BSCS networks.

On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach

The problem of dynamically accessing a set of parallel channels occupied by primary users is considered. The secondary user is allowed to sense and to transmit in a single channel. By exploiting idle periods between bursty transmissions of primary users, and by using a periodic sensing strategy, optimal dynamic access is achieved by maximizing the throughput of the secondary user while constraining collision probability with the primary user. The optimal dynamic spectrum access problem can then be formulated within the framework of constrained Markov decision processes (CMDPs). The optimal control policy is identified via a linear program, and its performance is analyzed numerically and through Monte Carlo simulations. Finally, we compare the optimal scheme to an ideal benchmark case when simultaneous sensing of all channels is assumed.

A cost and demand sensitive adjustment algorithm for service function chain in data center network

Evaluation of Quality of Healthcare: To What Extent Can We Rely on Patient Expectations and Preferences

Background: Patients’ loyalty to visit and use the services provided by the primary health centers (PHCs) is an important requirement of a patient referral system in many countries. The aim of this study was to examine the influence of internal service factors (service provider, service process, and service environment) on service quality and behavioural loyalty of patients in Indonesian PHCs.Methods: A cross-sectional study was conducted in 14 districts in Aceh Province, Indonesia between September and December 2020. Data were collected in 102 PHCs that were selected randomly from 137 PHCs that have an Inpatient Unit in the province. A proportional number of patients were recruited from each PHC and 389 patients were included. The demographic data, three components of internal service factors (service provider, service process, and service environment), the service quality and behavioural loyalty were assessed using a validated questionnaire. Hypothesis testing was conducted by using the structural equation model (SEM).Results: Our data suggested that two elements of internal service factors (service provider and service environment) had a positive and significant influence on service quality of the PHCs with p<0.001 and p=0.021, respectively. Service quality had a positive and significant influence of behavioural loyalty of patients to the PHCs (p=0.003). Service quality however did not serve as an intervening variable between internal service factors (service provider, service process, and service environment) and behavioural loyalty of patients, with p=0.091, p=0.230 and p=0.260, respectively. Conclusions: Service provider and service environment are two main factors that influence the service quality and the service quality directly influence the behavioural loyalty on PHC users. Therefore, to increase the patients’ loyalty to use the PHC services, the quality of the services should be improved by levelling up the quality of providers and both physical and social environments in the PHCs.