Quality Of Service Requirements Of Applications Research Articles

Delay-sensitive Quality of Service Routing with Integrated Admission Control for Wireless Mesh Network

Wireless mesh networks (WMNs) extend and improve broadband Internet connectivity for the end-users roaming around the edges of the wired network. Amid the explosive escalation of users sharing multimedia content over the Internet, the WMNs need to support the effective implementation of various multimedia applications. The multimedia applications require assured quality of service (QoS) to fulfill the user requirements. The QoS routing in WMNs needs to guarantee the QoS requirements of multimedia applications. Admission control (AC) is the primary traffic control mechanism used to provide QoS provisioning. AC admits a new flow only if the QoS requirements of already admitted flows are not violated, even after the admission of a new flow. We propose a new QoS routing protocol integrated with AC called Delay-Sensitive QoS Routing with integrated Admission Control (DSQRAC) to control the admission of delay-sensitive flows. A delay-aware cross-layer routing metric is used to find the feasible path. DSQRAC is implemented using ad-hoc on-demand distance vector (AODV) routing protocol, where a delay-sensitive controlled flooding mechanism is used to forward the route request packets. In the proposed work, we adjust/reassign the channels to aid the QoS routing to increase the likelihood of accepting a new flow. The simulation results show that the performance of the proposed QoS routing protocol is better than the existing schemes.

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks.

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

To address the problems of low network centralized management, weak interaction, limited hardware scalability, compatibility issues and challenges in expansion in the static deployment of traditional in-vehicle networks (IVNs), a new IVN architecture is designed. At the same time, to better meet the IVN Quality of Service (QoS) requirements and improve the real-time guarantee of data transmission, the QoS routing optimization mechanism under the new architecture is established. First, a new IVN architecture including a forwarding plane, control plane and application plane is designed by introducing software defined network (SDN) technology and combining it with the IVN itself. Second, the end-to-end delay optimization model of IVN is established by introducing network calculus theory, defining system parameters, creating a network model, calculating latency, considering queuing and congestion. the traditional routing algorithm is improved, and the DBROA algorithm has been proposed, which enhances the performance of QoS routing by introducing features such as distributed routing decisions, beacon mechanisms, optimization algorithms, and adaptability. This improvement allows it to better meet the QoS requirements of various applications and services, thereby enhancing existing QoS routing algorithms. Finally, an IVN routing optimization system is built and implemented, and the performance of different algorithms is compared and analyzed. The experimental results show that compared with the traditional Dijkstra and ECMP algorithms, the DBROA algorithm can effectively reduce the data forwarding delay and packet loss rate, improve the overall performance of IVN, and provide better QoS guarantees for IVN real-time data transmission.

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing.

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services

Ultra-reliable and low-latency communications (URLLC), as one of the major communication services of the fifth-generation (5G) and the sixth-generation (6G) cellular networks, is critical to supporting a variety of emerging mission-critical applications. However, the modern mobile networks could not satisfy the latency and reliability requirements, as well as other Quality of Service (QoS) requirements, including spectrum efficiency, energy efficiency, capacity, jitter, round-trip delay, network coverage, etc. To fulfill diverse QoS requirements for various URLLC applications, machine learning (ML) solutions are promising for future 6G networks. In this article, we first categorize the 6G URLLC vision into three connectivity characteristics, including ubiquitous connectivity, deep connectivity, and holographic connectivity, with their corresponding unique QoS requirements. We then identify potential challenges in meeting these connectivity requirements, and investigate promising ML solutions to achieve the intelligent connectivity for the 6G URLLC service. We further discuss how to implement the ML algorithms to guarantee the QoS requirements for different URLLC scenarios, including mobility URLLC, massive URLLC, and broadband URLLC. Finally, we present a case study of downlink URLLC channel access problems, solved by centralized deep reinforcement learning (CDRL) and federated DRL (FDRL), respectively, which validates the effectiveness of machine learning for URLLC services.

QoS-aware congestion control with online learning

In emerging applications such as industrial control and autonomous driving, end-to-end deterministic quality of service (QoS) transmission guarantee has become an urgent problem to be solved. Internet congestion control algorithms are essential to the performance of applications. However, existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements. To enable data delivery within application QoS constraints, we leverage an online learning mechanism to design Crimson, a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS. Crimson can change rates adaptively that satisfy application QoS requirements as a result. Across many emulation environments and real-world experiments, our proposed scheme can efficiently balance the different trade-offs between throughput, delay and loss rate. Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

Efficient Multimedia Content Transmission Model for Disaster Management using Delay Tolerant Mobile Adhoc Networks

Natural and manmade disasters such as earthquakes, floods, unprecedented rainfall, etc. pose several threats to our society. The citizens upload disaster information in the form of multimedia content such as pictures, audio, and videos. Efficient information and communication framework are critical for disaster management. Mobile Adhoc Networks (MANET) have been used effectively for disaster management. However, Disaster management prerequisites following Quality of Service (QoS) requirements such as bandwidth, high delivery ratio, low overhead, and minimal latency; however, the existing data transmission scheme induces high latency and overhead among intermediate devices; In order to meet the QoS requirement of disaster management applications in this paper, High Delivery Efficiency and Low Latency Multimedia Content Transmission (HDELL-MCT) scheme for MANETs is presented. Then, an improved buffer management scheme is presented for meeting disaster management performance and latency prerequisites. The experiment is conducted using ONE Simulator, the outcome shows the HDELL-MCT scheme achieves very good performance considering different QoS metrics such as improving delivery ratio by 38.02%, reducing latency by 7.53% and minimizing hop communication overhead by 65.1% in comparison with existing multimedia content transmission model.

Review and analysis of secure energy efficient resource optimization approaches for virtual machine migration in cloud computing

Computing operations such as databases, networks, hardware, programs, analytics, and so on are all part of the cloud computing service. As such, it may serve as an option to in-house hardware and software. However, dynamic consolidation of VMs is required to improve power consumption, load balance, the frequency of migrations, Quality of Service (QoS), and the rate at which SLA violations are addressed, all of which contribute to better resource use. VM technology has quickly become a pillar of data centers and cluster systems because to its utility in partitioning, consolidating, and moving workloads. VM placement is another thing that affects the quality of consolidation. It is important to design a system that improves energy efficiency by allocating resources to applications in a smart way while still meeting QoS requirements for applications. Moreover, the security of information that is being processed during the migration process is also one of the important tasks. Hence, in this paper, review and analysis based on parameters and metrics for the VM migration under the influence of energy optimization, consolidation, and security have been investigated in a wider manner. Moreover, some open issues that are still prevalent in the current field have also been highlighted.

QDRL: QoS-Aware Deep Reinforcement Learning Approach for Tor's Circuit Scheduling

Tor is a popular anonymity network adopted by more than two million users to preserve their privacy. Tor was mainly developed as a low-latency network to support interactive web browsing and messaging applications. However, bandwidth acquisitive applications such as BitTorrent consume a considerable percentage of Tor traffic. This results in an unfair allocation of the available bandwidth and significant degradation in the Quality-of-service (QoS) delivered to users. This paper presents a QoS-aware deep reinforcement learning approach for Tor's circuit scheduling (QDRL). We propose a design that coalesces the two scheduling levels originally presented in Tor and addresses it as a single resource allocation problem. We use the QoS requirements of different applications to set the weight of active circuits passing through a relay. Furthermore, we propose a set of approaches to achieve the optimal trade-off between system fairness and efficiency. We designed and implemented a reinforcement-learning-based scheduling approach ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRLS</i> ), a convex-optimization-based scheduling approach ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CVX-OPT</i> ), and an average-rate-based proportionally fair heuristic ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AR-PF</i> ). We also compare the proposed approaches with basic heuristics and with the implemented scheduler in Tor. We show that our reinforcement-learning-based approach (TRLS) achieved the highest QoS-aware fairness level with a resilient performance to the changes in an environment with a dynamic nature, such as the Tor network.

Where do all my smart home data go? Context-aware data generation and forwarding for edge-based microservices over shared IoT infrastructure

With the explosion of the Internet of Things (IoT) devices, the advent of the edge computing paradigm, and the rise of intelligent applications for smart infrastructure surveillance, in-network data management is gaining a lot of research attention these days. The challenge lies in accommodating multiple application microservices with varying Quality of Service (QoS) requirements to share the sensing infrastructure for better resource utilization. In this work, we propose a novel data collection framework, CaDGen (Context-aware Data Generation) for such a shared IoT infrastructure that enables integrated data filtration and forwarding towards minimizing the resource consumption footprint for the IoT infrastructure. The proposed filtration mechanism utilizes the contextual information associated with the running application for determining the relevance of the data. Whereas the proposed forwarding policy aims to satisfy the diverse QoS requirements for the running applications by selecting the suitable next-hop forwarder based on the microservices distribution across different edge devices. A thorough performance evaluation of CaDGen through a testbed implementation as well as a simulation study for diverse setups reveals promising results concerning network resource utilization, scalability, energy conservation, and distribution of computation for optimal service provisioning. It is observed that the CaDGen can achieve nearly 35% reduction in the generated data for a moderately dynamic scenario without compromising on the data quality.

Q-Flag: QoS-Aware Flow-Rule Aggregation in Software-Defined IoT Networks

Software-defined IoT (SDIoT) is a promising approach to address the requirements of the Internet of Things (IoT), such as network management, Quality of Service (QoS), and resource utilization. The advantages of SDIoT are facilitated by the separation of the data- and the control-planes using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flow-rules</i> , that allow fine-grained control over individual flows. However, the number of flow-rules that can be placed at the switches is limited, leading to scalability issues in SDIoT. Existing approaches to flow-rule management either do not consider the impact on QoS or are applicable only to a particular topology. In this article, we propose a QoS-aware flow-rule aggregation scheme for generic network topologies, which aims to achieve a satisfactory tradeoff among flow-rule compression and its impact on the QoS of IoT traffic flows. Specifically, the proposed scheme adaptively aggregates flow-rules while considering different QoS requirements of IoT applications in the network, and the flow-rule capacity of the switches. The proposed scheme consists of the following components—1) a path selection heuristic to increase the total number of flow-rules that can be accommodated in the network and 2) a multiarm bandit-based flow-rule aggregation scheme capable of reducing the number of flow-rules, while maintaining adequate performance in terms of QoS. Experimental results using IoT traffic show that, on average, the proposed scheme is capable of reducing the average end-to-end delay and QoS-violated flows in the network by 22% and 30%, respectively, compared to the state-of-the-art schemes.

Fuzzy logic based cross-layer design to improve Quality of Service in Mobile ad-hoc networks for Next-gen Cyber Physical System

5G standard by 3GGP has provision for Device-to-Device (D2D) communication over a Wireless Mobile Ad-hoc Network (MANET) framework. Recent research and development in 6G Networks and beyond has support for integration of MANET to the Mobile communication infrastructure to cater services to many potential future-proof applications like Smart Vehicular Networks using Internet of Vehicles (IoV) and Flying Ad-hoc Networks (FANET) for drone networks and military Ad-hoc networks. MANET being an Ad-hoc and infrastructure-less network poses major deployment challenge in resource allocation and management to facilitate strict Quality of Service (QoS) requirements for future Internet of Things (IoT) based Next-generation Cyber Physical System (NG-CPS). Most of MANETs use Contention-based Distributed Coordination Function (DCF) random Access Media Access Control (MAC) and a generic TCP/IP stack implementation. It is a daunting task to have deterministic model of network parameters for specific QoS requirements of IoT applications hence a soft-computing approach would be most viable solution considering network parameters across all five layers of TCP/IP stack that is a Cross-Layer design approach to improve QoS. In this paper, we have proposed a Fuzzy Logic system based Cross-Layer (FLS-CL) design for MANET to improve Network QoS parameters like Throughput, Packet Delivery ration (PDR) and End-to-End (E2E) delay. The proposed system has been implemented using Fuzzy Logic based parameters modeling using MATLAB and the Network simulation study using QualNet® simulator. The results are promising with average 27.64 % improvement in PDR and 76.04 % less E2E delay for connection-less UDP-based CBR applications and 3.28 % improvement in PDR and 58.81 % less E2E delay for connection-oriented TCP-based FTP applications. The QoS improvements are achieved with proposed FLS-CL as compared to state-of-the-art IEEE 802.11 based MANET without any FLS cross-layer optimization.

Plug-in over Plug-in Evaluation in Heterogeneous 5G Enabled Networks and Beyond

With the cool upcoming wave of 5G, currently, the networking and telecommunication industries are facing various digital transformations, which are changing the very fundamental nature of the existing network management infrastructure. Besides the Internet of Things (IoT) domain, we also notice that the 5G network in itself is composed of millions of heterogeneous physical entities and nodes, multiple domains, complex protocols and technologies, different gateways, and so on. This heterogeneity imposes critical impacts on the application specific quality of service (QoS) requirements, performance and utilization of network resources, and data and user security. In order to alleviate the above impacts, researchers propose to use different technologies such as software-defined networking, network function virtualization, blockchain, and artificial intelligence in 5G-enabled IoT networking. We notice that the layers over layers (of protocols and technologies) act like a plug-in over plug-in (PoP) in the network in order to accomplish various aims, including meeting QoS demands, enhancing security, load balancing, and so on. On one hand, we agree that this integration of different technologies in 5G networks bring numerous advantages, but on the other hand, we realize that this has posed a lot of unique critical issues in modern 5G network management. In this article, we point out that this straightforward approach of PoP is eventually not a healthy approach for network transformation. In this regard, using open source MANO (OSM), we provide a proof of concept (PoC) to show that at varying degrees of heterogeneity, PoP adds the delay in the VNF deployment process and further impacts the VIM CPU performance. This eventually affects the QoS requirements of IoT nodes or applications. Following this, we propose a high-level holistic approach that helps to alleviate the PoP issue. Finally, in this context, we also discuss the associated challenges and research opportunities.

A Framework for Managing Dynamic Routing in Industrial Networks Driven by Software-Defined Networking Technology

In order to meet strict Quality-of-Service (QoS) constraints imposed by some industrial applications, the configuration of industrial networks must address the requirements of traffic flows with different priorities such as minimum delay and packet loss. The performance is affected significantly if the end-to-end delay and packet loss surpasses a specific limit, and may become unbefitting for the destination. In this paper, we select Software-Defined Networking (SDN) technology to manage centralized devices and design an optimal path to guarantee the QoS requirements by taking into consideration two types of traffic flows: the first is low-delay while the second is both low-delay and low-loss. By using this optimal path, the Reactive Flow Installation (RFI) method increases the time delay in the forwarding of packets. So as to solve this issue, we propose a Mixed Flow Installation (MFI) method based on caching the flow rules which correspond to the optimal paths in the hash table deployed in the SDN controller memory in order to reduce the computation time of the forwarding path and the load at the SDN controller. Alternatively, the pre-configured flow rules in switches by the Proactive Flow Installation (PFI) method achieves the delay-sensitive. However, the PFI is not modified when the network status or the traffic type changes till the timeout value expires. This can affect the QoS requirements for industrial applications. To handle this challenge, we propose a PFI Re-routing (PFIR) method that redefined faster a new optimal path according to change without waiting the SDN controller for new flow rules. With the care of wireless and wired networks, we have built a simulation network via the OpenDayLight SDN controller and conducted an experimental testbed. The framework results are demonstrated by the performances through reduction of end-to-end delay, packet loss rate, and packet violation ratio.

Wireless Networks Performance Monitoring Based on Passive-active Quality of Service Measurements

Monitoring of the performance of wireless network is of vital importance for both users and the service provider which should be accurate, simple and fast enough to reflect the network performance in a timely manner. The aim of this paper is to develop an approach which can infer the performance of wireless ad hoc networks based on Quality of service (QoS) parameters assessment. The developed method considers the QoS requirements of multimedia applications transmitted over these kind of networks. This approach is based on the ideas of combination of both active and passive measurement methods. This approach uses an in-service measurement method in which the QoS parameters of the actual application (user) are estimated by means of dedicated monitoring packets (probes). Afterwards, these parameters are combined to produce and assess the application’s overall QoS using the fuzzy logic assessment and based on the measured QoS parameters estimated using the probe traffic. The active scheme is used to generate monitoring probe packets which are inserted between blocks of target application packets at regular intervals. While the passive monitoring is utilized to act as a traffic meter which performs as a counter of user packets (and bytes) that belong to the application (user) traffic flow that is subjected to monitoring. After simulating the developed technique, it offered a good estimation for the delay, throughput, packet losses and the overall QoS when using different probe rates.

A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems

Cloud computing is one of the rapidly growing distributed computing technologies, and cloud-based applications have increased significantly in recent years. The amount of cloud resources and the number of cloud user are important metrics that affect the management of the cloud-based applications. Since the volume of traffic to cloud-based applications grows, the resource provisioning as one of challenging issues to serve time-varying and heterogeneous workloads in resource management scope to be considered. In this paper, we propose a workload clustering-based resource provisioning mechanism for executing cloud-based applications with heterogeneous workloads. Our proposed mechanism utilized biogeography-based optimization (BBO) technique with K-means clustering to classify the cloud workloads according to their quality of service (QoS) requirements. Besides, we used Bayesian learning technique to specify suitable resource provisioning actions to satisfy the QoS requirements of cloud-based applications. The simulation results obtained through simulation demonstrate that the proposed solution reduces the delay, SLA violation ratio, cost, and energy consumption compared with workload clustering-based resource provisioning mechanisms.

A Multi-criteria based handover algorithm for vehicle-to-infrastructure communications

Maintaining seamless quality of service (QoS) requirements for ongoing delay sensitive applications during handover processes for Vehicle-to-Infrastructure (V2I) communication over heterogeneous networks is a challenging task. The situation becomes even more challenging to deal with for multi-tier heterogeneous networks with various network sizes. To overcome the above challenges, we propose a multicriteria-based handover algorithm for V2I communications (V2I-MHA) to be used in multi-tier heterogeneous network environments. V2I-MHA selects the most appropriate underlying target network for handover based on QoS profiles of the ongoing applications and services. Moreover, our proposed algorithm filters out the inappropriate candidate networks using a multi-criteria decision-making technique based on Simple Additive Weighting (SAW) approach. However, our approach ensures that the most appropriate candidate network for handover processes is selected not only based on QoS requirements of the ongoing applications, but also on the knowledge of candidate network parameters (e.g. bandwidth, packet latency, packet losses, and service pricing). A multi-mode vehicle On-Board Unit (OBU) containing Long Term Evolution-Advanced (LTE-A) and Wi-Fi network interface cards is developed in the Riverbed (previously OPNET) simulator for system performance evaluation. The simulation results show that the proposed V2I-MHA outperforms (in terms of handover failure rate and packet losses) the existing handover methods.

Cellular-Assisted Device-to-Device Communications for Healthcare Monitoring Wireless Body Area Networks

We envisage each person will wear an individual wireless body area network (WBAN) for healthcare monitoring. Two WBANs in proximity may collide. Such WBAN collisions can significantly degrade communication quality of service (QoS), in terms of throughput and latency of healthcare applications. To avoid collisions, we propose to use cellular-assisted device-to-device (D2D) communications to connect on-body sensors to a hub within WBAN. These D2D transmissions are performed concurrently with cellular transmissions and thus, interference is imposed on each other. A radio resource allocation scheme has been developed to manage the interference. The scheme uses a combination of difference of convex optimization and Hungarian algorithm to control transmit power and to assign communication channel to both cellular and D2D transmissions. The objective is to maximize the sum of cellular throughput while avoiding collisions among co-located WBANs and satisfying QoS requirements of healthcare applications. Evaluation results confirm that WBAN collisions can be avoided at a crowded pedestrian walkway, even when there are as many as 6 pedestrian arrivals per second.

A Graph-Based Fault-Tolerant Approach to Modeling QoS for IoT-Based Surveillance Applications

Node scheduling provides an effective way to prolong the network lifetime of Internet-of-Things (IoT) networks comprising of energy-constrained sensor nodes. Barrier scheduling is a special type of node scheduling scheme that targets IoT-based surveillance applications. An efficient barrier scheduling scheme must address the key Quality-of-Service (QoS) requirements of smart surveillance applications, such as coverage, connectivity, and energy efficiency. Moreover, such a scheme must be capable of dynamically adapting its execution strategy in the event of node failures caused due to faults arising out of unexpected battery depletion. This article proposes a fault-tolerant barrier scheduling scheme that satisfies the key QoS requirements of surveillance applications in the event of such faults. The approach is based on a novel fully weighted dynamic graph model. This article suggests two novel heuristics to guarantee fault tolerance and recovery. Extensive simulation studies are conducted to evaluate and compare the performance and effectiveness of this scheme with other such approaches.

HMB-I/O: Fast Track for Handling Urgent I/Os in Nonvolatile Memory Express Solid-State Drives

Differentiated I/O services for applications with their own requirements are very important for user satisfaction. Nonvolatile memory express (NVMe) solid-state drive (SSD) architecture can improve the I/O bandwidth with its numerous submission queues, but the quality of service (QoS) of each I/O request is never guaranteed. In particular, if many I/O requests are pending in the submission queues due to a bursty I/O workload, urgent I/O requests can be delayed, and consequently, the QoS requirements of applications that need fast service cannot be met. This paper presents a scheme that handles urgent I/O requests without delay even if there are many pending I/O requests. Since the pending I/O requests in the submission queues cannot be controlled by the host, the host memory buffer (HMB), which is part of the DRAM of the host that can be accessed from the controller, is used to process urgent I/O requests. Instead of sending urgent I/O requests into the SSDs through legacy I/O paths, the latency is removed by directly inserting them into the HMB. Emulator experiments demonstrated that the proposed scheme could reduce the average and tail latencies by up to 99% and 86%, respectively.