Quality Of Service Objectives Research Articles

Multi-Agent Deep Reinforcement Learning Based Resource Allocation for Ultra-Reliable Low-Latency Internet of Controllable Things

As a promising technology in the 5G era, the artificial intelligence (AI) enabled Internet of controllable things (IoCT) is expected to be an integral part of heterogeneous networks (HetNets) in the future. However, the realization of ultra-reliable low-latency communications (URLLC) in IoCT communications underlaid HetNet has stringent quality of service (QoS) requirements, resulting in unprecedented challenges for existing wireless resource allocation methods. In this paper, we first describe a cellular HetNet model with uplink IoCT communications, then formulate a dynamic mixed-integer nonlinear programming (MINLP) resource allocation problem for maximizing the long-term average energy efficiency under URLLC requirements including reliability, latency, and transmission rate. To solve the problem, we propose a decentralized MADRL-based resource allocation algorithm with a decentralized partially observable Markov decision process (dec-POMDP) and a mixed-centralized-decentralized (MCD) framework to address the partial observability and the scalability issues, respectively. In addition, we design a reward function featuring the objective decomposition, baseline-guided scaling, and QoS violation penalty so that the agents are coordinated. Extensive experiments demonstrate the convergence, scalability, and robustness of the proposed algorithm. Besides, the proposed algorithm substantially outperforms conventional resource allocation methods and different agent communication mechanisms in terms of maximizing energy efficiency.

PROMPT: Learning dynamic resource allocation policies for network applications

A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads. This practice requires strict resource allocation between workloads to reduce contention and maintain Quality-of-Service (QoS) guarantees. Prior work demonstrated promising opportunities to dynamically allocate resources based on workload demand, but may fail to meet QoS objectives in more stringent operating environments due to the presence of resource allocation cliffs, transient fluctuations in workload performance, and rapidly changing resource demand. We therefore propose PROMPT, a novel resource allocation framework using proactive QoS prediction to guide a reinforcement learning controller. PROMPT enables more precise resource optimization, more consistent handling of transient behaviors, and more robust generalization when co-scheduling new best-effort workloads not encountered during policy training. Evaluation shows that the proposed method incurs 4.2x fewer QoS violations, reduces severity of QoS violations by 12.7x, improves best-effort workload performance, and improves overall power efficiency over prior work.

Two evolutionary approaches with objective-specific variation operators for vehicle routing problem with time windows and quality of service objectives

This paper addresses a variant of the vehicle routing problem with time windows where the goal is to maximize the quality of service delivered to the customer. In the literature, this problem contains three objectives targeted at improving the quality of service. In this paper, we have proposed two evolutionary approaches, viz., a steady-state grouping genetic algorithm and a discrete differential evolution algorithm, to address this problem. The crossover and mutation operators are designed by considering the characteristics of each objective. The proposed approaches are incorporated with various heuristics that provide a set of better initial solutions in comparison to purely random initial solutions. We have also proposed two bounds for each objective. The approaches presented in this paper are tested on the Solomon instances which are considered as the standard benchmark instances for the vehicle routing problem with time windows in the literature. The proposed approaches are compared with the state-of-the-art approach available in the literature. The computational results demonstrate that our approaches are better in terms of solution quality and execution time than the state-of-the-art approach.

A Districting Application with a Quality of Service Objective

E-commerce sales have led to a considerable increase in the demand for last-mile delivery companies, revealing several problems in their logistics processes. Among these problems, are not meeting delivery deadlines. For example, in Chile, the national consumer service (SERNAC) indicated that in 2018, late deliveries represented 23% of complaints in retail online sales and were the second most common reason for complaints. Some of the causes are incorrectly designed delivery zones because in many cases, these delivery zones do not account for the demographic growth of cities. The result is an imbalanced workload between different zones, which leads to some resources being idle while others fail to meet their workload in satisfactory conditions. The present work proposes a hybrid method for designing delivery zones with an objective based on improving the quality of express delivery services. The proposed method combines a preprocess based on the grouping of demand in areas according to the structure of the territory, a heuristic that generates multiple candidates for the distribution zones, and a mathematical model that combines the different distribution zones generated to obtain a final territorial design. To verify the applicability of the proposed method, a case study is considered based on the real situation of a Chilean courier company with low service fulfillment in its express deliveries. The results obtained from the computational experiments show the applicability of the method, highlighting the validity of the aggregation procedure and improvements in the results obtained using the hybrid method compared to the initial heuristic. The final solution improves the ability to meet the conditions associated with express deliveries, compared with the current situation, by 12 percentage points. The results also allow an indicative sample of the critical service factors of a company to be obtained, identifying the effects of possible changes in demand or service conditions.

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

The expansion of Internet of Things (IoT) services and the huge amount of data generated by different sensors signify the importance of cloud computing services such as Storage as a Service more than ever. IoT traffic imposes such extra constraints on the cloud storage service as sensor data preprocessing capability and load-balancing between data centers and servers in each data center. Furthermore, service allocation should be allegiant to the quality of service (QoS). In the current work, an algorithm is proposed that addresses the QoS in storage service allocation. The proposed hybrid multi-objective water cycle and grey wolf optimizer (MWG) considers different QoS objectives (e.g., energy, processing time, transmission time, and load balancing) in both the fog and cloud Layers, which were not addressed altogether. The MATLAB script is used to simulate and implement our algorithms, and services of different servers, e.g., Amazon, Dropbox, Google Drive, etc., are considered. The MWG has 7%, 13%, and 25% improvement, respectively, in comparison with multi-objective water cycle algorithm (MOWCA), k-means based GA (KGA), and non-dominated sorting genetic algorithm (NSGAII) in metric of spacing. Moreover, the MWG has 4%, 4.7%, and 7.3% optimization in metric of quality in comparison to MOWCA, KGA, and NSGAII, respectively. The new hybrid algorithm, MWG, not only yielded to the consideration of three objectives in service selection but also improved the performance compared to the works that considered one or two objective(s). The overall optimization shows that the MWG algorithm has 7.8%, 17%, and 21.6% better performance than MOWCA, KGA, and NSGAII in the obtained best result by considering different objectives, respectively.

MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties, it is essential, but at the same time challenging, to develop a long-sighted optimization scheme that efficiently models the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network-based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively.

User defined weight based budget and deadline constrained workflow scheduling in cloud

AbstractCloud computing is a technology that is being used by scientists for the execution of huge scale workflows as it has various features such as availability, faster, cheaper, elasticity, and high accessibility. In addition, the users are charged on a pay per use basis which makes it more efficient to be used by various clients. Scheduling workflows in the cloud is a difficult task as it takes into account the dependencies of workflows along with quality of service (QoS) demands. The problem becomes more challenging when there are multiple QoS objectives which are often disagreeing to each other. A lot of research has been done to minimize makespan and execution cost under deadline and budget constraints. This article presents a workflow scheduling algorithm based on Jaya to minimize them both and returns solutions according to the weights a user gives. The algorithm has been compared with Min‐Min, Max‐Min, Round Robin, heterogeneous earliest finish time first, first come first serve, minimum completion time, dynamic heterogeneous earliest finish time first in WorkflowSim. The results are compared based on both cost and time. In the end, a trade‐off between the objectives has been shown. Results show that Jaya performs much better than other algorithms when averaged over several iterations.

MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming at efficiently utilizing compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties it is important, but at the same time challenging, to develop a long-sighted optimization scheme with efficient modelling of the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively.

A Formal Model for Quality-Driven Decision Making in Self-Adaptive Systems

Maintaining an acceptable level of quality of service in modern complex systems is challenging, particularly in the presence of various forms of uncertainty caused by changing execution context, unpredicted events, etc. Although self-adaptability is a well-established approach for modelling such systems, and thus enabling them to achieve functional and/or quality of service objectives by autonomously modifying their behavior at runtime, guaranteeing a continuous satisfaction of quality objectives is still challenging and needs a rigorous definition and analysis of system behavioral properties. Formal methods constitute a promising and effective solution in this direction in order to rigorously specify mathematical models of a software system and to analyze its behavior. They are also largely adopted to analyze and provide guarantees on the required functional/non-functional properties of self-adaptive systems. Therefore, we introduce a formal model for quality-driven self-adaptive systems under uncertainty. We combine high-level Petri nets and plausible Petri nets in order to model complex data structures enabling system quality attributes quantification and to improve the decision-making process through selecting the most plausible plans with regard to the system's actual context.

Quality of service (QoS): measurements of image formats in social cloud computing

With the development of multimedia and the popularity of social cloud (SC), Quality of Service (QoS) is used to measure the technical parameters of service delivery. QoS is mostly used for measuring various kinds of multimedia data. Sharing (uploading) on SC has become the daily activity of end-users. As a result of this activity, it provides an open challenge for service providers. As a service provider, the host delivers productive infrastructure, allowing end-users to upload and share their high-quality images. To evaluate the QoS of image compression, we conduct objective QoS techniques to measure the efficiency of image quality and QoS performance of service delivery via uploading /downloading of images on three popular SC, (Facebook, Twitter, and Instagram). These social clouds and image services are compared by the mean, standard deviation (STDEV),mean square error (MSE), and signal-to-noise ratio (SNR). So we can achieve better results about the SC image quality. The results show that Instagram and Facebook compressed images more as compared to Twitter. However, Twitter less supports image formats and provides an acceptable quality of compressed images as compared to others. Therefore, Facebook supports all image file formats and enhanced (QoE) levels of end-users, but Twitter provided the best QoS of compressed images as compared to Instagram. Further, we found that the decrease of MSE and increase of SNR both have a high impact on image resolutions as compared to original image parameters, which has a higher effect on quality. For the specific quality assurance of image tasks acquired for robust automated image analysis research, we are also using deep learning techniques (DLT) to classify the quality of images. We also have identified the opportunity to simplify the Convolutional Neural network (CNN) with smaller resampling grids and making this process more suitable for at least five thousand (5000) of the enormous datasets available in the future.

Recommending Mobile Services with Trustworthy QoS and Dynamic User Preferences via FAHP and Ordinal Utility Function

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes’ impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.

Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints

SummaryDynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low‐cost computing in cloud data centers. Quality‐of‐service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy‐efficient and QoS‐aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service‐level agreement violations, and other cost factors.

A Multiobjective QoS Model for Trading Cloud of Things Resources

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either cloud computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect, particularly when managing quality of service (QoS) as resource providers and application developers have different priorities. This article focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms. The evaluation results show the efficiency and dynamism of the proposed model in optimizing CoT resource allocation based on diverse QoS objectives, including resource cost, energy consumption, response time, fault tolerance, and resource coverage.

Perceptual quality assessment of video using machine learning algorithm

User experience has become the most reliable and trustworthy source for service providers to assess system performance. To fulfill customer requirements, service providers require an efficient quality of experience (QoE) estimation model. QoE is a subjective metric that deals with user perception and can vary dramatically due to various factors such as emotions, the degree of annoyance, past experience, and aesthetics. Moreover, subjective QoE evaluation is expensive and time-consuming because of human participation. Therefore, a model is required to objectively measure QoE with reasonable accuracy. In the context of service and network providers fulfilling user requirements, with a reasonable quality of service (QoS), needs to be provided to the relevant services/applications. However, QoS parameters do not reflect subjective opinion of the user accurately. Therefore, it is necessary to compute the mapping or correlation between QoS and QoE. The mapping may help service providers to understand the behavior of the overall network on user experience and efficiently manage the network resources. In this paper, a new feature number of displayed frames impacted (NoDFI) along with a machine learning based model is presented to compute a correlation between QoS and QoE. A publically available dataset is used to represent the correlation between objective QoS parameters and subjective QoE metric. The result of experiments showed that proposed feature NoDFI proved to be a valuable addition when compared with previously proposed models.

Variation-Aware Cloud Service Selection via Collaborative QoS Prediction

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

Two-Level Master–Slave RFID Networks Planning via Hybrid Multiobjective Artificial Bee Colony Optimizer

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the costefficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.

A novel QoS aware medium access control scheduler for LTE-advanced network

LTE-A specification mandates Medium Access Control (MAC) scheduler entity to ensure strict guaranteed quality of service (QoS) both in downlink and uplink direction. To the best of our knowledge the LTE-A MAC schedulers proposed so far constitute of an objective function dependent on single constraints like number of radio resources, user throughput, user channel conditions, user data buffer or user's downlink power requirement. In real world scenario if all the above constraints are not simultaneously taken into consideration, then it would be difficult to meet LTE-A QoS requirements. In this regard, to the best found knowledge, for first time in this paper, we propose a Multi Objective QoS aware LTE-A Downlink-MAC Scheduler (MOQDS) algorithm which adhere to two level QoS and fairness requirements of LTE-A specification. MOQDS algorithm does scheduling at two levels with each level has its objective function with its multiple operational constraints. Every transmission time interval, MOQDS uses multi-objective optimization to selects right user(s) and its corresponding application(s) to meet LTE-A QoS requirements. Simulation results are being compared with well referred LTE-A schedulers like modified largest weighted delay first, exponential rule proportional fairness and log rule under various ITU channel models. MOQDS achieves an average of 50% reduction in packet drop rate and minimum three times increased cell throughput as compared to above mentioned schedulers’ types and also with respect to all the other MAC schedulers mentioned in the references.

Mixed integer linear programming for quality of service optimization in Clouds

The analysis of the Quality of Service (QoS) level in a Cloud Computing environment becomes an attractive research domain as the utilization rate is daily higher and higher. Its management has a huge impact on the performance of both services and global Cloud infrastructures. Thus, in order to find a good trade-off, a Cloud provider has to take into account many QoS objectives, and also the manner to optimize them during the virtual machines allocation process. To tackle this complex challenge, this article proposed a multiobjective optimization of four relevant Cloud QoS objectives, using two different optimization methods: a Genetic Algorithm (GA) and a Mixed Integer Linear Programming (MILP) approach. The complexity of the virtual machine allocation problem is increased by the modeling of Dynamic Voltage and Frequency Scaling (DVFS) for energy saving on hosts. A global mixed-integer non linear programming formulation is presented and a MILP formulation is derived by linearization. A heuristic decomposition method, which uses the MILP to optimize intermediate objectives, is proposed. Numerous experimental results show the complementarity of the two heuristics to obtain various trade-offs between the different QoS objectives.

A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees--loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min---max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm.

A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.