Target 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.

NPU-Accelerated Imitation Learning for Thermal Optimization of QoS-Constrained Heterogeneous Multi-Cores

Thermal optimization of a heterogeneous clustered multi-core processor under user-defined QoS targets requires application migration and DVFS. However, selecting the core to execute each application and the VF levels of each cluster is a complex problem because (1) the diverse characteristics and QoS targets of applications require different optimizations, and (2) per-cluster DVFS requires a global optimization considering all running applications. State-of-the-art resource management for power or temperature minimization either relies on measurements that are commonly not available (such as power) or fails to consider all the dimensions of the optimization (e.g., by using simplified analytical models). To solve this, ML methods can be employed. In particular, IL leverages the optimality of an oracle policy, yet at low run-time overhead, by training a model from oracle demonstrations. We are the first to employ IL for temperature minimization under QoS targets. We tackle the complexity by training NN at design time and accelerate the run-time NN inference using NPU. While such NN accelerators are becoming increasingly widespread, they are so far only used to accelerate user applications. In contrast, we use for the first time an existing accelerator on a real platform to accelerate NN-based resource management. To show the superiority of IL compared to RL in our targeted problem, we also develop multi-agent RL-based management. Our evaluation on a HiKey 970 board with an Arm big.LITTLE CPU and NPU shows that IL achieves significant temperature reductions at a negligible run-time overhead. We compare TOP-IL against several techniques. Compared to ondemand Linux governor, TOP-IL reduces the average temperature by up to 17 ˆC at minimal QoS violations for both techniques. Compared to the RL policy, our TOP-IL achieves 63 % to 89 % fewer QoS violations while resulting similar average temperatures. Moreover, TOP-IL outperforms the RL policy in terms of stability. We additionally show that our IL-based technique also generalizes to different software (unseen applications) and even hardware (different cooling) than used for training.

Strengthening The Quality of Madrasah Services through Human Resource Management

This study aims to analyze human resource management in improving the quality of madrasa services at MTs Negeri Model Palangkaraya. This research uses a qualitative case study approach, where the researcher uses interviews, observations, and documentation in collecting data. The data analysis was carried out by referring to the Miles and Huberman concept: data collection, data reduction, data presentation, and conclusion. The results of the study indicate that human resource management in improving the quality of madrasa services is carried out through; the determination of service quality targets, analysis of human resource needs, fulfillment of human resource needs, and evaluation of human resource performance. This study has implications for the importance of madrasah in preparing the quality of the human resources in them to provide excellent service to the community and meet expectations.

Challenges to improving patient satisfaction and patient loyalty in public hospitals during a pandemic

The COVID-19 pandemic impacts all sectors and aspects of life, including in health care facilities. To meet the demands of improving health care quality, public hospitals must continue to strive to increase patient satisfaction and loyalty. However, previous studies did not provide much insight into patient satisfaction and loyalty in public hospitals during the COVID-19 pandemic. This study reviewed at articles that discussed factors and strategies for increasing patient satisfaction and loyalty in public hospitals during the COVID-19 pandemic.This study employs a narrative review method to examine 12 research articles on patient satisfaction and patient loyalty published between January 2020 and December 2021 on the Science Direct, Google Scholar, and ProQuest databases. During a pandemic, the overall quality of health services contributes to patient satisfaction. Furthermore, hospital image, patient experience, patient trust, quality of service, customer value, perceived value, hospital health promotion, and service innovation all have an impact on patient satisfaction and loyalty. This review identified the determinants of patient loyalty and patient satisfaction in public hospitals during the COVID-19 pandemic, allowing them to meet the demands of meeting service quality targets.

Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum

User-facing services are now evolving towards the microservice architecture where a service is built by connecting multiple microservice stages. Since the entire service is heavy, the microservice architecture shows the opportunity to only offload some microservice stages to the edge devices that are close to the end users. However, emerging techniques often result in the violation of Quality-of-Service (QoS) of microservice-based services in cloud-edge continuum, as they do not consider the communication overhead or the resource contention between microservices and external co-located tasks. We propose Nautilus, a runtime system that effectively deploys microservice-based user-facing services in cloud-edge continuum. Nautilus ensures the QoS of microservice-based user-facing services while minimizing the required computational resources, which is comprised of a communication-aware microservice mapper, a contention-aware resource manager and an IO-sensitive and load-aware microservice migration scheduler. The mapper divides the microservice graph into multiple partitions based on the communication overhead and maps the partitions to appropriate nodes. On each node, the resource manager determines the optimal resource allocation for its microservices based on reinforcement learning that may capture the complex contention behaviors. Once the microservices are suffered from external IO pressure, the IO-sensitive microservice scheduler migrates the critical one to idle nodes. Furthermore, when the load of microservices changes dynamically, the load-aware microservice scheduler migrates microservices from busy nodes to idle ones to ensure the QoS goal of the entire service. Our experimental results show that Nautilus can guarantee the required QoS target under external shared resources contention while the state-of-the-art suffers from QoS violations. Meanwhile, Nautilus reduces the computational resource usage by 23.9% and the network bandwidth usage by 53.4%, while achieving the required 99%-ile latency.

Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks

Most networks strive to provide good security and an acceptable level of performance. Quality of service (QoS) plays an important role in the performance of a network. Mobile ad hoc networks (MANETs) are a decentralized and self-configuring type of wireless network. MANETs are generally challenging and the provision of security and QoS becomes a huge challenge. Many researchers in literature have proposed parallel mechanisms that investigate either security or QoS. This paper presents a security framework that is QoS-aware in MANETs using a network protocol called optimized link state routing protocol (OLSR). Security and QoS targets may not necessarily be similar but this framework seeks to bridge the gap for the provision of an optimal functioning MANET. The framework is evaluated for throughput, jitter, and delay against a sinkhole attack presented in the network. The contributions of this paper are (a) implementation of a sinkhole attack using OLSR, (b) the design and implementation of a lightweight-intrusion detection system using OLSR, and (c) a framework that removes fake routes and bandwidth optimization. The simulation results revealed that the QoS-aware framework increased the performance of the network by more than 70% efficiency in terms of network throughput. Delay and jitter levels were reduced by close to 85% as compared to when the network was under attack.

NURA

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize GPU resources, is still challenging. Some pieces of prior work (e.g. spatial multitasking) have limited opportunity to improve resource utilization, while others, e.g. simultaneous multi-kernel, provide fine-grained resource sharing at the price of unfair execution. This paper proposes a new multi-application paradigm for GPUs, called NURA, that provides high potential to improve resource utilization and ensure fairness and Quality-of-Service(QoS). The key idea is that each streaming multiprocessor (SM) executes the Cooperative Thread Arrays (CTAs) that belong to only one application (similar to spatial multi-tasking) and shares its unused resources with the SMs running other applications demanding more resources. NURA handles resource sharing process mainly using a software approach to provide simplicity, low hardware overhead, and flexibility.We also perform some hardware modifications as an architectural support for our software-based proposal. Our conservative analysis reveals that the hardware area overhead of our proposal is less than 1.07% with respect to the whole GPU die. Our experimental results over various mixes of GPU workloads show that NURA improves throughput by 26% compared to the state-of-the-art spatial multi-tasking, on average, while meeting QoS targets. In terms of fairness, NURA has almost similar results to spatial multitasking, while it outperforms simultaneous multi-kernel by 76%, on average.

NURA

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize GPU resources, is still challenging. Some pieces of prior work (e.g., spatial multitasking) have limited opportunity to improve resource utilization, while other works, e.g., simultaneous multi-kernel, provide fine-grained resource sharing at the price of unfair execution. This paper proposes a new multi-application paradigm for GPUs, called NURA, that provides high potential to improve resource utilization and ensures fairness and Quality-of-Service (QoS). The key idea is that each streaming multiprocessor (SM) executes Cooperative Thread Arrays (CTAs) belong to only one application (similar to the spatial multi-tasking) and shares its unused resources with the SMs running other applications demanding more resources. NURA handles resource sharing process mainly using a software approach to provide simplicity, low hardware cost, and flexibility. We also perform some hardware modifications as an architectural support for our software-based proposal. We conservatively analyze the hardware cost of our proposal, and observe less than 1.07% area overhead with respect to the whole GPU die. Our experimental results over various mixes of GPU workloads show that NURA improves GPU system throughput by 26% compared to state-of-the-art spatial multi-tasking, on average, while meeting the QoS target. In terms of fairness, NURA has almost similar results to spatial multitasking, while it outperforms simultaneous multi-kernel by an average of 76%.

One for All and All for One: Distributed Learning of Fair Allocations With Multi-Player Bandits

Consider N cooperative but non-communicating players where each plays one out of M arms for T turns. Players have different utilities for each arm, represented as an N×M matrix. These utilities are unknown to the players. In each turn, players select an arm and receive a noisy observation of their utility for it. However, if any other players selected the same arm in that turn, all colliding players will receive zero utility due to the conflict. No communication between the players is possible. We propose two distributed algorithms which learn fair matchings between players and arms while minimizing the regret. We show that our first algorithm learns a max-min fairness matching with near- O(logT) regret (up to a loglogT factor). However, if one has a known target Quality of Service (QoS) (which may vary between players) then we show that our second algorithm learns a matching where all players obtain an expected reward of at least their QoS with constant regret, given that such a matching exists. In particular, if the max-min value is known, a max-min fairness matching can be learned with O(1) regret.

Managing price and fleet size for courier service with shared drones

With the rapid development of modern logistics systems, the unmanned aerial vehicle (a.k.a. drone) based delivery service emerges as a technology-driven innovation, and it is now pilot running in many regions across the globe. The drone delivery system aims to reduce the labor cost in the current labor-intensive courier industry, as well as avoid the disturbance caused by geographic and demographic features. For the drone delivery service providers to survive and prosper, a pool of shared drones is integrated into the ecological chain, where the courier service providers focus on the delivery operations. For such a drone sharing system, revenue management becomes vital in terms of pricing, drone hiring cost, and service-related cost. To better depict the system dynamics, in the present paper, a time-varying and price-sensitive queueing model is formulated, where the customer behaviors are taken into account, such as balking and abandonment. To guarantee a preset service level, the probability of abandonment and expected delay are considered as the control targets, and the pricing for courier service and the maintained fleet size are considered simultaneously. For the low and moderate quality of service(QoS) target, a fluid control model is constructed with a closed-form solution. For the high QoS target, a modified approximation algorithm is designed to numerically tackle the problem. Based on the simulation, it is observed that the proposed approximation methods not only provide a high-quality joint strategy but also help stabilize the system performance. In addition, only mild price change is needed to reach the optimal condition, and a time lag exists between the optimal fleet sizing and the demand variation, which may shed light on the demand-driven fleet sizing under more general settings.

QoS-Oriented Dynamic Power Allocation in NOMA-Based Wireless Caching Networks

Non-orthogonal multiple access (NOMA) based wireless caching network (WCN) is considered as a promising technology for next-generation wireless communications since it can significantly improve the spectral efficiency. In this letter, we propose a quality of service (QoS)-oriented dynamic power allocation strategy for NOMA-WCN. In content placement phase, base station (BS) sends multiple files to helpers by allocating different powers according to the different QoS targets of files, for ensuring that all helpers can successfully decode the two most popular files. In content delivery phase, helpers serve two users at the same time by allocating the minimum power to far user according to the QoS requirement, and then all the remaining power is allocated to near user. Hence, our proposed method is able to increase the hit probability and drop the outage probability compared with conventional methods. Simulation results confirm that the proposed power allocation method can significantly improve the caching hit probability and reduce the user outage probability.

E2bird: Enhanced Elastic Batch for Improving Responsiveness and Throughput of Deep Learning Services

We aim to tackle existing problems about deep learning serving on GPUs in the view of the system. GPUs have been widely adopted to serve online deep learning-based services that have stringent QoS(Quality-of-Service) requirements. However, emerging deep learning serving systems often result in poor responsiveness and low throughput of the inferences that damage user experience and increase the number of GPUs required to host an online service. Our investigation shows that the poor batching operation and the lack of data transfer-computation overlap are the root causes of the poor responsiveness and low throughput. To this end, we propose E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird, a deep learning serving system that is comprised of a GPU-resident memory pool, a multi-granularity inference engine, and an elastic batch scheduler. The memory pool eliminates the unnecessary waiting of the batching operation and enables data transfer-computation overlap. The inference engine enables concurrent execution of different batches, improving the GPU resource utilization. The batch scheduler organizes inferences elasticallyto guarantee the QoS. Our experimental results on an Nvidia Titan RTXGPU show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> bird reduces the response latency of inferences by up to 82.4 percent and improves the throughput by up to 62.8 percent while guaranteeing the QoS target compared with TensorFlow Serving.

QoS-driven scheduling in the cloud

Priority-based scheduling policies are commonly used to guarantee that requests submitted to the different service classes offered by cloud providers achieve the desired Quality of Service (QoS). However, the QoS delivered during resource contention periods may be unfair on certain requests. In particular, lower priority requests may have their resources preempted to accommodate resources associated with higher priority ones, even if the actual QoS delivered to the latter is above the desired level, while the former is underserved. Also, competing requests with the same priority may experience quite different QoS, since some of them may have their resources preempted, while others do not. In this paper we present a new scheduling policy that is driven by the QoS promised to individual requests. Benefits of using the QoS-driven policy are twofold: it maintains the QoS of each request as high as possible, considering their QoS targets and available resources; and it minimizes the variance of the QoS delivered to requests of the same class, promoting fairness. We used simulation experiments fed with traces from a production system to compare the QoS-driven policy with a state-of-the-practice priority-based one. In general, the QoS-driven policy delivers a better service than the priority-based one. Moreover, the equity of the QoS delivered to requests of the same class is much higher when the QoS-driven policy is used, particularly when not all requests get the promised QoS, which is the most important scenario. Finally, based on the current practice of large public cloud providers, our results show that penalties incurred by the priority-based scheduler in the scenarios studied can be, on average, as much as 193% higher than those incurred by the QoS-driven one.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). This is the first work to study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring that the achievable rate of each user meets the quality of service (QoS) requirement for all possible channel error realizations. With unit-modulus and rate constraints, this problem is non-convex. The imperfect CSI further increases the difficulty of solving this problem. By using approximation and transformation techniques, we convert the optimization problem into a squence of semidefinite program (SDP) subproblems that can be efficiently solved. Numerical results show that the proposed robust beamforming design can guarantee the required QoS targets for all the users.

Robust Design for IRS-Aided Communication Systems With User Location Uncertainty

In this letter, we propose a robust design framework for IRS-aided communication systems in the presence of user location uncertainty. By jointly designing the transmit beamforming vector at the BS and phase shifts at the IRS, we aim to minimize the transmit power subject to the worse-case quality of service (QoS) constraint, i.e., ensuring the user rate is above a threshold for all possible user location error realizations. With unit-modulus, this problem is not convex. The location uncertainty in the QoS constraint further increases the difficulty of solving this problem. By utilizing techniques of Taylor expansion, S-Procedure and semidefinite relaxation (SDP), we transform this problem into a sequence of semidefinite programming (SDP) sub-problems. Simulation results show that the proposed robust algorithm substantially outperforms the non-robust algorithm proposed in the literature, in terms of probability of reaching the required QoS target.

The Meshing of the Sky: Delivering Ubiquitous Connectivity to Ground Internet of Things

Nowadays, unmanned aerial vehicles (UAVs) are being used in several novel applications, especially in the telecommunication domain. However, ensuring UAV communication and networking for the purpose of a specific application is still challenging. Indeed, due to the mobility of a UAV in a vast area, permanent connectivity over the backhaul is very sporadic and might be lost. In this article, we consider an aerial mesh network where each UAV can serve as a flying base station to boost terrestrial base station in case of damaged infrastructure case for example, or/and provide connectivity for uncovered or poorly covered nodes, and behaves as a relay to establish communication between two components owing to a lack of reliable direct communication link between them. We then detail a case study where a UAV-fleet is used to collect data from the ground Internet-of-Things (IoT) devices and forward it to the cloud for further processing passing by a remote gateway. We aim here to build a queueing framework, including network layer, MAC layer, and physical layer, and investigate both uplink and downlink communication links. Next, we derive some closed forms allowing us to predict the network performance in terms of traffic intensity at every UAV of the aerial mesh network, end-to-end (E2E) throughput, and E2E delay of ongoing streams. Next, we conduct extensive simulations to illustrate the benefit of our framework. Results discussion and numerous insights on parameter setting, target quality of service, and design consideration are also drawn.

GQoS: A QoS-Oriented GPU Virtualization with Adaptive Capacity Sharing

Currently, the virtualization technologies for cloud computing infrastructures supporting extra devices, such as GPU, require additional development and refinement. This requirement is particularly evident in the area of resource sharing and allocation under some performance constraints, like the quality of service (QoS) guarantee, in light of the closed GPU platform. This deficiency significantly limits the applicability range of the cloud platform, which aims to support the efficient and fluent execution of business and academic workloads. This paper introduces gQoS, an adaptive virtualized GPU resource capacity sharing system under the QoS target, which can share and allocate the virtualized GPU resource among workloads adaptively, guaranteeing the QoS level with stability and accuracy. We evaluate the workloads and compare our gQoS strategy with other allocation strategies. The experiments show that our strategy guarantees much better accuracy and stability in QoS control and that the total GPU resource utilization under gQoS can be rewarded with at most a 25.85 percent reduction compared with other strategies.

A Reinforcement Learning-Based Duty Cycle Adjustment Technique in Wireless Multimedia Sensor Networks

Multimedia delivery support has recently been added to Wireless Sensor Networks (WSN) and has led to increased interest in Wireless Multimedia Sensor Networks (WMSN). WMSNs are expected to be crucial to the success of applications related to the Internet of Things (IoT), such as smart health, smart surveillance, smart homes, etc. Alongside their improved multimedia capabilities, WMSNs inherit WSN limitations such as energy and processing constraints. Additionally, WMSNs have significant Quality of Service (QoS) requirements, since multimedia delivery requires increased network performance in terms of bandwidth, latency, etc. Balancing energy efficiency and QoS is a fundamental challenge for WMSN users and operators alike. This paper proposes Reinforcement Learning based Duty Cycle (rlDC), an innovative learning-based scheme to adjust the duty cycle and contention window of WMSN nodes in order to meet energy efficiency and QoS targets. By employing rlDC, WMSN sensor nodes intelligently adapt their operation according to network delivery performance and application requirements. The proposed rlDC scheme was evaluated under different use cases in a simulation environment, and testing results show it outperforms other state-of-the-art duty-cycle-based protocols for WMSNs.

Maestro: Autonomous QoS Management for Mobile Applications Under Thermal Constraints

Power densities of modern mobile system-on-a-chip designs can quickly exceed the thermal design limits during typical application use such as gaming or Web browsing. Resulting high temperatures lead to frequent thermal throttling and significant loss in quality-of-service (QoS) delivered to users. Thus, a joint consideration of thermal constraints and QoS requirements is essential to maximize the overall user experience. Prior techniques either rely on users to determine the best tradeoff point between QoS and temperature, or greedily utilize the thermal headroom to maximize performance, causing QoS to drop below user tolerable levels over extended durations of use. This paper introduces the MAESTRO framework to automatically manage QoS at runtime depending on application characteristics and thermal constraints. MAESTRO builds on the observation that increased temperatures can be tolerated for applications with bursty compute patterns due to idle periods between activities, while causing large QoS degradations for long-running applications with continuous computations. MAESTRO: 1) detects such continuous computations that are susceptible to throttling; 2) proactively finds a QoS level to balance user experience and temperature; and 3) performs closed-loop DVFS and thermally efficient thread mapping to meet the target QoS on a heterogeneous multicore CPU. Such application-adaptive control of QoS-temperature tradeoffs allows MAESTRO to sustain a target QoS level within a user tolerable range for longer durations without sacrificing the performance of latency-sensitive bursty computations. Evaluations on a real system prototype validates MAESTRO’s ability to accurately detect potential throttling-induced QoS degradations and demonstrates 41% to $6.7\boldsymbol {\times }$ longer durations of sustained QoS compared to state-of-the-art for a set of mobile applications.

Network-Wide Throughput Optimization for Highway Vehicle-To-Vehicle Communications

This letter analyzes the meta-distribution of the transmission success probability (TSP) for vehicular networks based on a one-dimensional Poisson point process (PPP). We also propose a method to maximize the throughput across the vehicular network. Compared to the conventional spatial average performance assessment, the meta-distribution reveals the fraction of vehicles that operate at a target success rate of transmission across the highway. To this end, we propose a per-vehicle rate selection scheme to keep a target quality of service (QoS) level for all vehicles. The results reveal that operating at the spatially-averaged maximum throughput may lead to excessive variation in the performance of individual vehicles. However, with the proposed meta-distribution-aware rate selection scheme, the throughput variation among the vehicles can be significantly reduced (e.g., up to a 60 % reduction).