User-level Quality Of Service Research Articles

Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

In this paper, the power transmission and energy efficiency (EE) in downlink multi-cell massive multiple-input-multiple-output (MIMO) systems are investigated and optimized. Most of the existing works do not take into account different user's quality of service (QoS) requirements. These models also depend on a fixed transmit power consumption, which cannot reflect the actual EE levels concerning QoS. Therefore, in this paper, a new base station (BS) transmit power adaptation is firstly introduced, termed the BSTPA method. The transmitted power is adapted to channel condition and user-level QoS including data rate requirement and maximum allowable outage probability to minimize the total BS radiated power. An analytical closed-form expression of the average BS transmit power adaptation is derived. Then, a corresponding iterative optimization algorithm is proposed to maximize the average EE per BS and obtain the optimal design parameters. The proposed optimization algorithm aims to globally achieve the optimal EE value with the optimal amount of data rate, the number of BS antennas, and users. Simulation results are demonstrated to verify our analytical findings. For a wide range of different design parameters, the results indicate that the proposed method obtains remarkably higher EE levels compared to the conventional scenario, particularly if per-antenna circuit power is very small. The optimization results show that the case with lower per-antenna circuit power can achieve about 4.5 times better EE gain than the case with higher per-antenna circuit power with 13.3% optimum data rate improvement.

Deadline Constrained Task Scheduling Method Using a Combination of Center-Based Genetic Algorithm and Group Search Optimization

Abstract The present paper describes a hybrid group search optimization (GSO) and center-based genetic algorithm (CBGA)-based model for task scheduling in cloud computing. The proposed hybrid model combines the GSO, which has been successful in its application in task scheduling, with the use of the CBGA. The basic scheme of our approach is to utilize the benefits of both the GSO algorithm and CBGA excluding their disadvantages. In our work, we introduce the hybrid clouds, which are needed to determine which task to be outsourced and to what cloud provider. These choices ought to minimize the expense of running an allotment of the aggregate task on one or various public cloud providers while considering the application prerequisites, e.g. deadline constraints and data requirements. In the hybridization approach (HGSOCBGA), each dimension of a solution represents a task and the solution as a whole signifies all the task priorities. The vital issue is how to allocate the user tasks to exploit the profit of the infrastructure as a service (IaaS) provider while promising the quality of service (QoS). The generated solution proficiently assures the user-level QoS and improves the IaaS providers’ credibility and economic benefit. The HGSOCBGA method also designs the hybridization process and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.

An Efficient Hybridization Algorithm Based Task Scheduling in Cloud Environment

The present paper describes a hybrid self-adaptive learning global search algorithm and firefly algorithm (HSLGSAFA)-based model for task scheduling in cloud computing. The proposed hybrid model combines gravitational search algorithm (GSA), which has been successfully scheduling the task in the application, with the use of SL strategy and the FA. The basic scheme of our approach is to utilize the benefits of both SLGSA algorithm and firefly algorithm and not including their disadvantages. In HSLGSAFA, each dimension of a solution represents a task and a solution as a whole signifies all tasks’ priorities. The vital issue is how to allocate users’ tasks to exploit the income of Infrastructure as a Service (IaaS) provider while promising Quality-of-Service (QoS). The generated solution is proficient to assure user-level QoS and improve IaaS providers’ credibility and economic benefit. The HSLGSAFA method also used to design the hybridization process and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.

Quality-of-Experience-Aware Design in Next-Generation Wireless Networks [Guest Editorial

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users’ perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure.

A stochastic fluid model for on-demand peer-to-peer streaming services

On-demand video streaming services have become popular in recent years. In current streaming services, however, the growth of user population leads to the lack of the upload rate of the video server. This mainly causes starvation in the playout buffer at a client, resulting in the degradation of user-level quality of service (QoS). In this paper, we consider an on-demand streaming service based on a peer-to-peer (P2P) technology. Focusing on the stochastic behavior of streaming data contents in the playout buffer at a client peer, we consider an analytical stochastic fluid model, which takes into account the heterogeneity among peer nodes and the peer churn. We derive the starvation probability that the playout buffer is empty. Numerical examples show that the starvation probability increases when the population of peer nodes grows. It is also shown that even when the population of peer nodes is extremely large, a small increase in the upload rate at ordinary-peer nodes significantly improves the QoS of P2P streaming services.

Online adaptive firewall allocation in internet data center

The emerging applications service providers, such as e-commerce and search engine, are increasingly hosting their services in Internet Data Centers (IDCs). At the same time, IDCs become the target of malicious attacks over the Internet. Firewalls as a crucial infrastructure are chosen to protect IDCs. However, the current static firewall allocation scheme may lead to low resource utilization or poor user level quality of service (QoS), even worse, may reduce system security level. In this paper, we first derive the relationship among the number of firewalls, the number of packet filtering rules in firewall, and the user level QoS. Based on the relationship, we propose an on-line adaptive firewall allocation algorithm that computes the number of firewalls required at different times to meet the given security and user level QoS requirements while achieving resource savings. The performance of the proposed algorithm is evaluated via trace-driven simulation and compared with the static firewall allocation scheme. Example findings include (1) with the same resource (firewall) and the same number of packet filtering rules, the adaptive algorithm provides better QoS by adaptively allocating firewalls according to the dynamic request load; (2) to achieve the same QoS requirement, with the same number of packet filtering rules, the adaptive algorithm requires less resource than the static method; and (3) the average number of firewalls decreases as the expected delay increases. Moreover, we extend our study to explore the impacts of the key algorithm and system parameters (e.g. control time scale and migration time) on the adaptive firewall allocation scheme. We find that both control time scale and migration time have minor impacts on the performance of the proposed adaptive firewall allocation scheme. The proposed scheme can be applied to the next generation IDCs which are physically wired once but can be rewired programmatically.

Quantitative assessment of user-level QoS and its mapping

This paper proposes a scheme for quantitative assessment of user-level (or perceptual) quality of service (QoS) for audio-video transmission by means of two psychometric methods: the method of paired comparisons and the law of comparative judgment. Moreover, we discuss QoS mapping from application-level QoS to user-level QoS by principal component analysis and multiple regression analysis. In the assessment, we simulate the transmission of an audio-video stream over a loaded network. In order to investigate the effect of the contents on QoS mapping, we treat two types of audio-video streams. By experiment, we demonstrate that our scheme can construct an interval scale as the user-level QoS parameter for each stream and represent it as a function of two application-level QoS parameters with high accuracy. We notice that the multiple regression line depends on the contents. We also propose the concept of control gain by media synchronization, which indicates how much media synchronization control subjectively lightens the average network load.

User-level QoS and traffic engineering for 3G wireless 1xEV-DO systems

Third-generation (3G) wireless systems such as 3G1X, 1×EV-DO, and 1xEV-DV provide support for a variety of high-speed data applications. The success of these services critically relies on the capability to ensure an adequate quality of service (QoS) experience to users at an affordable price. With wireless bandwidth at a premium, traffic engineering and network planning play a vital role in addressing these challenges. We present models and techniques that we have developed for quantifying the QoS perception of 1×EV-DO users generating file transfer protocol (FTP) or Web browsing sessions. We show how user-level QoS measures may be evaluated by means of a Processor-Sharing model that explicitly accounts for the throughput gains from multi-user scheduling. The model provides simple analytical formulas for key performance metrics such as response times, blocking probabilities, and throughput. Analytical models are especially useful for network deployment and in-service tuning purposes due to the intrinsic difficulties associated with simulation-based optimization approaches. © 2003 Lucent Technologies Inc.