User Quality Of Service Requirements Research Articles

Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services

Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software, emerges as a preferred technology. However, there are a large number of cloud service providers and they offer different pricing strategies for various applications in various regions. Therefore, it is of great importance for them that incoming service requests are assigned to the appropriate cloud services at minimum cost and provide maximum user satisfaction [quality of service (QoS) attributes]. Due to the issues, such as multiple cloud providers, different QoS requirements, different service level agreements and uncertainties in demand, price and availability, the optimization of resource allocation present further challenges. The objective of our study is to optimize the cost and performance of video on demand applications using cloud content delivery networks, storage and transcoders based on the QoS requirements of users. To solve the NP-hard problem, Particle Swarm Optimization (PSO) technique is used due to the easiness in its concept and coding, less sensitive to the nature of the objective function, limited number of parameters and generating high quality solution within a short time. We propose a new method in which the optimum solution is affected not only by the best solution of the particle and global best solution but also by the best solution of the neighborhood particles in that iteration. This ternary approach is implemented into the well-known discrete and constrained PSO, achieving the minimum cost with user satisfaction for allocation of video requests to cloud resources. Although the proposed method yields better results in terms of accuracy, execution time of the algorithm is not reasonable. To overcome this inefficiency; ternary approach is embedded into multi-swarm PSO and it is parallelized and combined with greedy heuristic algorithms. The results of the comparison with the benchmarking algorithms show that our proposed method yields better results from the standpoint of both accuracy and execution time.

STAR: SLA-aware Autonomic Management of Cloud Resources

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

End-to-end CNN-based dueling deep Q-Network for autonomous cell activation in Cloud-RANs

The fifth generation (5G) technology is expected to support a rapid increase in infrastructure and mobile user subscriptions with an increase in the number of remote radio heads (RRHs) per unit area using cloud radio access networks (C-RANs). From the economic point of view, minimizing the amount of energy consumption of the RRHs is a challenging issue. From the environmental point of view, achieving “greenness” in wireless networks is one of the many goals of telecommunication operators. This paper proposes a framework to balance the energy consumption of RRHs and quality of service (QoS) satisfaction of users in cellular networks using a convolutional neural network (CNN)-based relational dueling deep Q-Network (DQN) scheme. Firstly, we formulate the cell activation/deactivation problem as a Markov decision process (MDP) and set up a two-layer CNN which takes raw captured images in the environment as its input. Then, we develop a dueling DQN-based autonomous cell activation scheme to dynamically turn RRHs on or off based on the energy consumption and QoS requirements of users in the network. Finally, we decouple a customized physical resource allocation for rate-constrained users and delay-constrained users from the cell activation scheme and formulate the problem as a convex optimization problem to ensure the QoS requirements of users are achieved with the minimum number of active RRHs under varying traffic conditions. Extensive simulations reveal that the proposed algorithm achieves faster rate of convergence than nature DQN, Q-learning and dueling DQN schemes. Our algorithm also achieves stability in mobility scenarios compared with DQN and dueling DQN without CNN. We also observe a slight improvement in balancing energy consumption and QoS satisfaction compared with DQN and dueling DQN schemes.

Achieving Fine-Grained QoS for Privacy-Aware Users in LBSs

LBSs have been prospering over recent years. Although many LPPMs are proposed to solve specific problems under certain settings, it is still hard to satisfy the different demands of users due to the diverse requirements for quality of service (QoS) and privacy preservation. In this article, we propose an adaptive scheme to achieve fine-grained QoS for privacy-aware users in LBSs. Specifically, we construct a Bayesian-based classification model to identify the QoS requirements of users under different circumstances. QoS and privacy degrees provided by LBSs are carefully defined based on the concept of rate-distortion theory, which plays an important role in bridging the LPPMs with users' requirements. When users request the location service from the built-in GPS module in smart devices, we first specify their circumstances and requirements, then reply with the modified locations. We also design an efficient algorithm to dynamically choose the appropriate privacy strategy according to the QoS requirements of users in different circumstances. Experimental results show that our scheme can identify QoS requirements accurately and protect location privacy adaptively.

BOOST: A User Association and Scheduling Framework for Beamforming mmWave Networks

The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling, due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively.

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

An intelligent resource dynamic allocation method for UAV wireless mobile network which supports QoS

The development of Unmanned Aerial Vehicle (UAV) business requires that the wireless mobile network can provide the highest possible data rate with limited wireless resources and harsh channel environment, while ensuring the requirements of different quality of service (QoS). The present algorithm only considers one or two spatial sub channels with the maximum eigenvalue, and does not consider how to satisfy multiple requirements with limited resources, nor does it consider the problem of admission control. In order to solve the above problems, this paper proposes an intelligent resource dynamic allocation method based on QoS. In this paper, resources are managed effectively through resource reservation mechanism and selective QoS adjustment mechanism. Then, all non-zero eigenvalue space sub channels are used for data transmission to maximize channel capacity under the condition that the total transmission power is limited and user QoS requirements are met. The simulation results show that the algorithm can effectively improve the throughput of the system by making full use of multi-user diversity on the basis of satisfying the QoS requirements of users.

Improved Teaching Learning-Based QoS-Aware Services Composition for Internet of Things

The emergence of Internet of Things (IoT) paradigm has led to a continuous increase in the number of services that have similar functionalities but different quality of service (QoS). The IoT services composition, therefore, becomes an important challenge that aims at creating added-value services by combining several services offered by thousands of smart devices in the IoT environment to meet the user's QoS requirements. In this context, an improved teaching learning-based QoS-aware services composition algorithm (ITL-QCA) is proposed to find a very close to the optimal composition in a reasonable amount of time. Unlike the evolutionary computation-based and swarm intelligence-based approaches, the proposed services composition algorithm is characterized by a few tuning parameters and a high exploration capability of the search space of compositions. This allows obtaining compositions with a high optimality in terms of QoS and without requiring some hard tuning parameters. The simulation results show clearly that the ITL-QCA algorithm performs better in terms of the composition optimality and has a reasonable execution time in a large-scale environment compared to other services composition approaches proposed in the literature.

Co-Tier and Cross-Tier Spectrum Allocation Scheme Based on Interval Shapley and Banzhaf Values

Current evolution of 5G heterogeneous network (HetNet) leads to unprecedented traffic demands and user quality of service requirements. It is extremely challenging to meet these needs for future generation communications. Multi-flow carrier aggregation (MCA) is an emerging technique that is implemented to improve the capacity of cellular networks. In this paper, we present an effective spectrum allocation scheme for the HetNet platform under the MCA technology. For the joint co-tier and cross-tier collaborations in HetNets, we adopt the ideas of interval Shapley and Banzhaf values as solution concepts. To strike an appropriate performance balance while dynamically distributing the limited spectrum resource, our scheme is formulated as a coalition game model, and both interval values are applied for different type applications. Based on the main features of cooperative game theory, the proposed scheme takes various benefits in a rational way. Through extensive simulations, we compare the performance of the proposed scheme with existing state-of-the-art spectrum allocation protocols, and confirm the superiority of our coalitional game based approach. Finally, the conclusions are presented, and some research challenges are identified for the future study.

How much the wavelength dimensioning methods and a tightened QoS provision impact on the dynamic WDM optical networks capacity?

This article gives an insight on the importance to assign a different capacity to each network link and to tighten the Quality of Service (QoS) provision in the network, in dynamic WDM Optical Networks with wavelength continuity constraints. In the text, several examples illustrate the performance of different strategies. The usual procedure to compute the number of wavelengths of each network link consists of choosing the same capacity to each link on the network. This decision is selected, in most cases, by simplicity and simulation limitations. Notwithstanding, there are no technical motives to force a uniform capacity on the network. In consequence, we analyze the impact on the network capacity of designating the minimum amount of wavelengths to each network link, while satisfying each user QoS requirement. Moreover, despite the chosen dimensioning strategy, it is usual to offer a much better QoS than requested for some users, with the corresponding waste of resources. We can amend this issue by limiting which wavelengths are available to each user according to its particular QoS constraint. This technique, known as Wavelength Grouping, seeks to provide to each user a QoS level closer to the one defined in the Service Level Agreement. By doing so, it is possible to reduce the network capacity needed to allow communication to its user, or diminishing the network blocking probability for a given wavelength capacity. In spite of the usual approach, in this work, we demonstrate that a non-uniform dimensioning strategy and a tighten QoS provision allows to save significant networks capacity, while simultaneously provisioning to each user the QoS established in its Service Level Agreement. This fact is very relevant nowadays, where an impending capacity crunch in optical networks is an important issue.

Cost-driven workflow scheduling on the cloud with deadline and reliability constraints

Clouds are becoming an effective platform for scientific workflow applications. In the meantime, Cloud computing structures are moving towards being more heterogeneous. In heterogeneous service-oriented systems, managing the reliability of resources (e.g., processors and communication networks) is widely identified as a critical issue due to processor and communication failures affecting user quality of service requirements. Therefore, these types of failures should be taken into account when scheduling algorithms. The present paper proposes a scheduling approach which includes four algorithms for minimizing the workflow execution cost while also meeting the user-specified deadline and reliability. To meet the application’s requirements, the first algorithm partitions the workflow into several clusters based on a critical parent called CbCP. After that, the resource assignment algorithm, consisting of reliability and deadline distribution methods, satisfies the application’s constraints. Experimental outcomes on various workflows, generated at different scales in real and random fashion, demonstrate that the proposed heuristics meet the deadline and reliability. This ensures the minimal cost when performing a similar quality of service as opposed to the performance of the state-of-the-art DRR and QFEC+ algorithms.

Energy-Efficient Resource Allocation for Dynamic Priority-Based Vehicular Mobile-Health Communications

Owing to the developments of wireless communication technologies, mobile health (M-Health) has been postulated as a promising means to improve healthcare quality and save lives in emergencies. However, using wireless communications in M-Health faces the following challenges. First, the wireless transmission may generate electromagnetic interference (EMI) and trigger critical malfunctions to the medical devices. Second, different types of M-Health applications and time-varying patient conditions require dynamic quality of service (QoS). Third, energy efficiency (EE) should be optimized to guarantee the reliability of M-Health services, considering the limitations of the existing battery technologies. To address these challenges, this paper investigates the joint channel and power resource allocation problem for vehicular M-Health communications. The problem is formulated as a mixed-integer nonlinear program (MINLP) to maximize the system EE with the consideration of EMI constraints on medical equipment and dynamic QoS requirements of medical users. To find possible solutions, we reformulate the MINLP problem by relaxing the integer variables and transforming the objective to convex forms. Based on the dual-decomposition method, we first obtain the optimal power allocation and then recover the channel allocation variables to integers. To satisfy the QoS requirements, we develop two channel allocation strategies, i.e., the QoS fulfillment strategy and the QoS compensation strategy. The swap-blocking allocation concept is introduced to guarantee the optimality of the obtained solutions. Simulation results show that the proposed resource allocation scheme improves the system EE and service satisfaction degree.

A Reinforcement Learning Approach to Energy Efficiency and QoS in 5G Wireless Networks

Satisfying the huge demand for high bandwidth in 5G networks is in part achieved by vertical densification of the network infrastructure with so-called small-cell base stations. As a direct consequence, the performance of 5G networks intrinsically suffers from: 1) a huge inter-cell interference due to the network density and 2) a large energy waste that results from the high redundancy of lightly loaded, always-on, and small-cell base stations. In this paper, we use a game theoretic approach to design a distributed energy efficient bandwidth sharing mechanism for small-cell networks. To circumvent the combinatorial nature of searching in the huge strategy space, we invoke a reinforcement learning approach to intelligently guide the search toward good solutions. More precisely, our solution relies on dynamically learning good strategies for the user-equipment association and orthogonal frequency division multiple access (OFDMA) scheduling to strike a balance between energy efficiency and high bandwidth utilization. Our model takes into account inter-cell interference and the diversity of user quality of service requirements.

A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

Cognitive radio has emerged as an enabling technology in the realization of a spectrum-efficient and delay-sensitive industrial wireless communication where nodes are capable of responding in real-time. However, particularly for time-critical industrial applications, because of the link-varying channel capacity, the random arrival of a primary user (PU), and the significant delay caused by spectrum handoff (SH), it is challenging to realize a seamless real-time response which results in a quality of service (QoS) degradation. Therefore, the objectives of this paper is to increase spectrum utilization efficiency by allocating channel based on the priority of a user QoS requirements, to reduce SH delay, to minimize latency by preventing avoidable SHs, and to provide real-time response. To achieve an effective spectrum utilization, we proposed an integrated preemptive/non-preemptive priority scheme to allocate channels according to the priority of user QoS requirements. On the other hand, to avoid significant SH delays and substantial latency resulting from random PU arrival, a unified spectrum sensing technique was developed by integrating proactive sensing and the likelihood estimation technique to differentiate between a hidden and a co-existence PU, and to estimate the mean value of the busy and the idle periods of a channel respectively. Similarly, to prevent poor quality channel selection, a channel selection technique that jointly combines a reward system that uses metrics, e.g. interference range, and availability of a common channel to ranks a set of potential target channels, and a cost function that optimizes the probability of selecting the channel with the best characteristics as candidate channels for opportunistic transmission and for handoffs was developed. The simulation results show a significant performance gain of the delay-PritSHS in terms of number of SHs, Latency, as well as throughput for time-critical industrial applications in comparison to other schemes.

Finding a Pareto Optimal Solution for a Multi-Objective Problem of Managing Radio Resources in LTE-A Systems: A QoS Aware Algorithm

Due to ever increasing users demands, fulfilling the needs of growing users is challenging with constrained resources for Long Term Evolution- Advanced (LTE-A). In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multi-objective optimization techniques for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel algorithm that selects an optimal solution out of a set of a representative Pareto front in accordance with the users QoS requirements. It is completely novel as it uses the multi-objective optimization algorithms for achieving desired QoS in LTE-A system. The efficiency and performance of the proposed work has been further improved by autonomously assigning and managing resources among various users and applications. The results have been discussed that showcase efficient system performance and better user experience.

Optimal Deployment of Configurable Business Processes in Cloud Federations

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment , e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

Quality-of-Service Aware Game Theory-Based Uplink Power Control for 5G Heterogeneous Networks

The fifth-generation (5G) mobile communication system is expecting to support users with diverse data rate requirements by densely deploying small cells. The users attached with small cells make use of the same frequency band as the existing macro cell users, that causes severe co-channel interference and degrades the performance. To overcome this challenge, we propose a game theoretical framework for the optimal uplink power allocation for small cells, i.e., femtocell deployed underlaid macrocell. In this paper, femtocell users play a non-cooperative game to choose the optimal power to maximize the sum-rate of the system. Furthermore, an iterative quality-of-service (QoS)-aware game theory based power control (QoS-GTPC) scheme is proposed to optimize the femtocell user power taking into account macrocell user QoS requirements. Simulation results verify that the proposed QoS-GTPC scheme significantly improves the sum-rate and reduces outage and interference, as compared with conventional power control scheme.

Virtual sensor as a service: a new multicriteria QoS-aware cloud service composition for IoT applications

A large number of connected devices made Internet of Things (IoT). IoT devices may provide the same service but with different quality parameters such as high availability, cost and delay. Nowadays cloud infrastructures provide an entry point for discovery, selection, fusion and consuming such distributed IoT services. Hence, a new kind of middleware service should be devised in cloud to select and compose the required services based on the end user quality of service requirements. This new kind of cloud service for IoT is named as virtual sensor. In this paper, we propose an architecture for such a virtual sensor service in cloud and propose a multi-objective metaheuristic algorithm for sensor-service selection and composition in cloud middleware. In particular, a quantum-inspired genetic algorithm-based approach is used to address the problem. Simulations with sample IoT workflows were conducted to evaluate efficiency and performance of the proposed method. Our proposed approach for selection and composition of IoT services yields about 60% improvement in overall quality of service of the virtual sensor compared to rival algorithms.

Recent Advances on Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention fromresearchers in recent years.[...]

A Secure Resource Optimization Strategy Based on Utility Dominant in Vehicular Networks

Prospectively, vehicular networks are envisioned to support vehicular-based, road-based, and traffic-based data sensing, transmitting and processing for intelligent transportation system applications, and eventually evolve towards a new paradigm, named vehicular networks (VNs), which bundle the characteristics of networks into vehicular networks. In VNs, since the conflict between resource utility and the quality of service (QoS), it remains an ongoing challenge about how to reasonably and effectively allocate resources that can meet QoS and fairness requirements at the same time which causes security problem because of the conflicts. To this end, we propose a utility-based dominant resource allocation optimization strategy in this paper to achieve security in VNs. We first establish a mapping model between user QoS requirements and resource demands, and then apply the improved dominant resource fairness scheme to obtain optimal allocation results. The effectiveness of this security strategy is proved theoretically through the constructed utility function and the mapping model. Experimental results demonstrate that our security strategy can not only maximize the ratio of provision over demand of users and the satisfied degree of services but also achieve the QoS and fairness requirements of users.