Fluctuations In Quality Of Service Research Articles

Temporal pattern-aware QoS prediction by Biased Non-negative Tucker Factorization of tensors

Dynamic quality of service (QoS) data contain rich temporal patterns of user-service interactions, which are vital for better understanding user behaviors and service conditions. Canonical polyadic (CP)-based latent factorization model has proven to be capable of capturing such patterns. However, it models the relations among latent features of user, service and time in a rigid and unnatural way, causing its failures in capturing the complex patterns when the target QoS data become massive. To address that issue, this paper proposes a Biased Non-negative Tucker Factorization of 3D tensors (BNTucF) model with the four-fold ideas: (1) utilizing a core tensor for modeling the complex interactions among latent features; (2) incorporating linear biases into the model for accurate descriptions on QoS fluctuation; (3) constraining the model to be non-negative for describing QoS non-negativity; (4) deducing a single latent factor-dependent, multiplicative updating scheme for training the model in an efficient density-oriented way. Empirical studies demonstrate that the proposed BNTucF can learn complex dynamic user-service interaction patterns more accurately, hence achieving accurate predictions on missing QoS data.

Spatial Context-Aware Time-Series Forecasting for QoS Prediction

With the explosive growth of web services, the increasing sparse and dynamic QoS (quality of service) data pose a great challenge to QoS prediction in service recommendation. How to fully utilize the contextual information of service invocation and their potential relationships becomes the key to improving the accuracy of QoS prediction. In this paper, a spatial context-aware time series forecasting (SCATSF) framework is proposed for QoS prediction by considering both the temporal and the spatial context of users and services. SCATSF has three main parts: time-aware neighbors selection, spatial context-aware interaction learning and time series forecasting for QoS prediction. First, a novel time series similarity (TSS) is proposed to measure the similarity of users or services based on time-varying QoS fluctuation, in order to select appropriate neighbors for the target user or service. Furthermore, the contextual information of neighbors, as well as the contextual information of users and services, are both integrated to enrich the spatial context of service invocation, and a pairwise multi-layer deep network (PMLDN) is developed to consolidate multiple features and to learn feature interactions in pairs. With the help of spatial contextual information and time-aware QoS information, a spatial context-aware GRU model (SCA-GRU) is finally present to complete the time series forecasting for QoS prediction. Experimental results on the prediction of response time in a real-world dataset demonstrate that our SCATSF approach can effectively utilize spatio-temporal contextual information, so that it can achieve higher accuracy than many existing methods.

Quantifying Resource Contention of Co-located Workloads with the System-level Entropy

The workload co-location, such as deploying offline analysis workloads with online service workloads on the same node, has become common for modern data centers. Workload co-location deployment improves data center resource utilization significantly. Still, it also introduces resource contention, resulting in the online service’s quality of service fluctuation, which we call performance interference. As the online service is a tail-latency-sensitive workload, the tail-latency metric can reflect the performance interference degree of the co-location workloads at the application level. However, to guide system design and evaluation, quantitatively evaluating the resource contention of the co-located workloads at the system level is also essential. This article proposes a novel metric called System-Level Entropy (SLE). As a system-level metric, SLE can measure quantitatively resource contention of the co-location systems and perform the apples-to-apples comparison between systems. The experimental results show that SLE can accurately reflect the performance interference of workloads and then evaluate the system resource contention. We also demonstrate two case studies of the SLE. We quantify the affinity of different co-location combinations, including three online services and five offline workloads. Furthermore, we evaluate the effects of state-of-the-art isolated mechanisms (the container and the CPU–affinity binding) with these co-location combinations.

A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^a</xref> with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>^a</label> <uri>https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/</uri> </fn>

PDMA: Probabilistic service migration approach for delay‐aware and mobility‐aware mobile edge computing

AbstractAs a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay‐aware and mobility‐aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%–20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.

Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors.

Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.

Using NearestGraph QoS Prediction Method for Service Recommendation in the Cloud

With the advent of the mobile network, the fusion of cloud computing and fog computing is becoming feasible to promise lower latency and short-fat connection. However, there are a lot of redundant cloud-aware services with identical functionalities but a different quality of service (QoS) in the fog cloud environment. In fact, since QoS information is stored in distributed fog servers rather than remote cloud, it is hard for individuals to make recommendation and selection with sparse QoS information. Collaborative filtering is an important method for the sparsity problems and has been widely adopted on the prediction of missing QoS values. Focusing on the fact that existing researchers often ignore the QoS fluctuation in a wide range in the fog cloud environment, a novel neighbor-based QoS prediction method is proposed for service recommendation, in which a concept and calculation method is put forward to describe the stable status of services and users with quantifiable QoS values, and a NearestGraph algorithm is further designed to recognize stable or unstable candidate along with their popularity by a nearest neighbor graph structure which can help to make missing QoS values prediction in a certain order to improve final prediction accuracy. Experimental results confirm that the proposed method is effective in predicting unknown QoS values in terms of service recommendation accuracy and efficiency.

Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

Abstract QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale-free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow.

A user mode CPU–GPU scheduling framework for hybrid workloads

Cloud platforms composed of multi-core CPU and many-core Graphics Processing Unit (GPU) have become powerful platforms to host incremental CPU–GPU workloads. In this paper, we study the problem of optimizing the CPU resource management while keeping the quality of service (QoS) of games. To this end, we propose vHybrid, a lightweight user mode runtime framework, in which we integrate a scheduling algorithm for GPU and two algorithms for CPU to efficiently utilize CPU resources with the control accuracy of QoS. vHybrid can maintain the desired QoS with low CPU utilization, while being able to guarantee better QoS performance with little overhead. Our evaluations show that vHybrid saves 37.29% of CPU utilization with satisfactory QoS for hybrid workloads, and reduces three orders of magnitude for QoS fluctuations, without any impact on GPU workloads.

Does Fast Adaptive Modulation Always Outperform Slow Adaptive Modulation?

Link adaptation techniques are important modern and future wireless communication systems to cope with quality of service fluctuations in fading channels. These techniques require the knowledge of the channel state obtained with a portion of resources devoted to channel estimation instead of data and updated every coherence time of the process to be tracked. In this paper, we analyze fast and slow adaptive modulation systems with diversity and non-ideal channel estimation under energy constraints. The framework enables to address the following questions: (i) What is the impact of non-ideal channel estimation on fast and slow adaptive modulation systems? (ii) How to define a proper figure of merit which considers both resources dedicated to data and those to channel estimation? (iii) Does fast adaptive always outperform slow adaptive techniques? Our analysis shows that, despite the lower complexity and feedback rate, slow adaptive modulation (SAM) can achieve higher spectral efficiency than fast adaptive modulation (FAM) in the presence of energy constraint, diversity, and non-ideal channel estimation. In addition, SAM satisfies bit error outage requirements also in FAM-denied region.

Dynamic QoS Provisioning in DiffServ Domains Using Fuzzy Logic Controllers

Most work on Differentiated Services (DiffServ) handles Quality of Service (QoS) provisioning on a per node basis, which assumes that this strategy would provide QoS in the entire domain. Nevertheless, this approach could fail in large domains with multiple flow aggregation and unexpected input traffic. Therefore, provisioning techniques should be used to avoid unexpected overloads that result in QoS fluctuations. A proposal using fuzzy controllers to reconfigure DiffServ nodes according to both incoming traffic and the actual QoS is given. However, it is not easy to specify fuzzy rule bases and membership functions that optimize controller performance. Thus, we also propose a methodology to choose fuzzy controller parameters using the Wang---Mendel and genetic algorithms. Finally, we evaluate the performance of this model by simulation of an IP Telephony application in a DiffServ domain.

Scalable video coding and transport over broadband wireless networks

With the emergence of broadband wireless networks and increasing demand of multimedia information on the Internet, wireless multimedia services are foreseen to become widely deployed in the next decade. Real-time video transmission typically has requirements on quality of service (QoS). However, wireless channels are unreliable and the channel bandwidth varies with time, which may cause severe degradation in video quality. In addition, for video multicast, the heterogeneity of receivers makes it difficult to achieve efficiency and flexibility. To address these issues, three techniques, namely, scalable video coding, network-aware adaptation of end systems, and adaptive QoS support from networks, have been developed. This paper unifies the three techniques and presents an adaptive framework, which specifically addresses video transport over wireless networks. The adaptive framework consists of three basic components: (1) scalable video representations; (2) network-aware end systems; and (3) adaptive services. Under this framework, as wireless channel conditions change, mobile terminals and network elements can scale the video streams and transport the scaled video streams to receivers with a smooth change of perceptual quality. The key advantages of the adaptive framework are: (1) perceptual quality is changed gracefully during periods of QoS fluctuations and hand-offs; and (2) the resources are shared in a fair manner.

A QoS adaptive multimedia transport system: design, implementation and experiences

The long awaited `new environment' of high speed broadband networks and multimedia applications is fast becoming a reality. However, few systems in existence today, whether they be large scale pilots or small scale test-beds in research laboratories, offer a fully integrated and flexible environment where multimedia applications can maximally exploit the quality of service (QoS) capabilities of supporting networks and end-systems. In this paper we describe the implementation of an adaptive transport system that incorporates a QoS oriented API and a range of mechanisms to assist applications in exploiting QoS and adapting to fluctuations in QoS. The system, which is an instantiation of the Lancaster QoS Architecture, is implemented in a multi ATM switch network environment with Linux based PC end systems and continuous media file servers. A performance evaluation of the system configured to support video-on-demand application scenario is presented and discussed. Emphasis is placed on novel features of the system and on their integration into a complete prototype. The most prominent novelty of our design is a `distributed QoS adaptation' scheme which allows applications to delegate to the system responsibility for augmenting and reducing the perceptual quality of video and audio flows when resource availability increases or decreases.