QoS Levels Research Articles

ReadGuard: Integrated SSD Management for Priority-Aware Read Performance Differentiation

When multiple apps with different I/O priorities share a high-performance SSD, it is important to differentiate the I/O QoS level based on the I/O priority of each app. In this paper, we study how a modern flash-based SSD should be designed to support priority-aware read performance differentiation. From an in-depth evaluation study using 3D TLC SSDs, we observed that existing FTLs have several weaknesses that need to be improved for better read performance differentiation. In order to overcome the existing FTL weaknesses, we propose ReadGuard , a novel priority-aware SSD management technique that enables an FTL to manage its blocks in a fully read-latency-aware fashion. ReadGuard leverages a new read-latency-centric block quality marker that can accurately distinguish the read latency of a block and ensures that higher-quality blocks are used for higher-priority apps. ReadGuard extends an existing suspend/resume technique to handle collisions among reads. Our experimental results show that a ReadGuard -enabled SSD is effective in supporting differentiated read performance in modern 3D flash SSDs.

A Reliable Publish–Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses

Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish–subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. Nevertheless, IoT technology is required to effectively handle a considerable volume of subscriptions or topic adjustments from several clients concurrently. In addition, subscription throughput is an essential factor of the pub-sub mechanism, as it directly influences the speed at which messages may be sent to subscribers. The primary focus of this paper pertains to a performance assessment of the proposed message categorization architecture for the Message Queue Telemetry Transport (MQTT) broker. This architecture aims to establish a standardized approach to pub-sub topics and generate new topics with various performance characteristics. We also standardize the form of MQTT protocol broker topic categorization and payload based on greenhouse specifications. The establishment of topic classification enhances the operational effectiveness of the broker, reduces data volume, and concurrently augments the number of messages and events transmitted from the greenhouse environment to the central server on a per-second basis. Our proposed architecture is validated across multiple MQTT brokers, including Mosquitto, ActiveMQ, Bevywise, and EMQ X, showing enhanced flexibility, extensibility, and simplicity while maintaining full compatibility with greenhouse environments. Key findings demonstrate significant improvements in performance metrics. The message processing time for the proposed Active MQ broker was increased approximately five-fold across all QoS levels compared to the original. Subscription throughput for the Bevywise MQTT Route 2.0 broker at QoS0 reached 1453.053, compared to 290.610 for the original broker. The number of messages in the Active MQ broker at QoS0 surged from 394.79 to 1973.95. These improvements demonstrate the architecture’s potential for broader IoT applications in pub-sub systems.

C-SLA-MLO: Enhancing SLA Compliance in Industrial Wi-Fi through Cooperative Multilink Operation

In the dynamic landscape of Industry 4.0, robust wireless connectivity emerges as a critical enabler for Industrial Internet of Things (IIoT) applications. With the advent of Wi-Fi 7’s multilink operation (MLO), the demand for schedulers capable of meeting diverse Service Level Agreement (SLA) requirements within industrial environments becomes paramount. Addressing this need, this paper introduces Cooperative SLA-MLO (C-SLA-MLO), a novel distributed multilink scheduler that balances data across MLO channels to fulfill the SLA of STAs operating within the same BSS. We provide a simulation-based evaluation where we compare C-SLA-MLO with other benchmark approaches, in terms of SLA compliance when varying the SLA characteristics, the level of QoS, or background traffic, showcasing an average reduction of 90% in SLA deviation in the industrial use case. This study contributes practical insights for enhancing wireless connectivity in IIoT, offering potential benefits for industrial network optimization by exploiting the MLO feature brought by the new IEEE 802.11be.

Supporting VoIP communication in IEEE 802.11ax networks: A new admission control and scheduling resource allocation scheme

The current IEEE 802.11ax standard enhances Wi-Fi networks with a series of new features, such as multi-user (MU) transmission, an Orthogonal Frequency Division Multiple Access (OFDMA) scheme and the ability to multiplex traffic from different access categories (ACs). These features can be utilized to enhance the QoS support for VoIP traffic and optimize the usage of IEEE 802.11ax network resources. This work proposes a new scheme to multiplex VoIP calls in IEEE 802.11ax MU frames and a new scheduler and resource allocation algorithm specifically designed for VoIP data traffic. Our scheduler allocates VoIP packets requiring longer transmission times into the same frame(s), minimizing the channel air-time assigned to VoIP transmission. In addition, unused radio resources in the MU frame are leveraged to transmit best-effort packets along with VoIP packets. For completeness, we also define a call admission control (CAC) algorithm that anticipates channel saturation conditions to ensure VoIP users can maintain a guaranteed level of QoS. Based on simulation results, our scheme is more efficient in reducing channel utilization than other schedulers such as multi-user round-robin (RR) (implemented by ns-3) or single-user FIFO. For example, for 30 VoIP stations using the G.711 codec under mixed channel conditions, our scheme reduces by 30% the air-time required to transmit VoIP packets. When coupled with the ability to also send best-effort packets along with VoIP packets, this translates into a higher throughput (i.e. 10 Mbit/s vs 4 Mbit/s) and more simultaneous VoIP users with guaranteed QoS (up to 46 VoIP users vs 26 and 28 users for the multi-user RR and single-user FIFO scheduling algorithms, respectively).

BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments.

Comparative Analysis of Power Consumption between MQTT and HTTP Protocols in an IoT Platform Designed and Implemented for Remote Real-Time Monitoring of Long-Term Cold Chain Transport Operations

IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to provide quantitative results that help maximize battery life in IoT transportation systems. Although is well known that MQTT consumes less power than HTTP, a comparative analysis of their power consumption with long-time tests and different conditions has not yet been conducted. In this sense, a design and validation of an electronic cost-efficient platform system for remote real-time monitoring is proposed using a NodeMCU module, in which experimentation is carried out for HTTP and MQTT with different QoS levels to make a comparison and demonstrate the differences in power consumption. Furthermore, we characterize the behavior of the batteries in the systems and compare the theoretical analysis with real long-time test results. The experimentation using the MQTT protocol with QoS 0 and 1 was successful, resulting in power savings of 6.03% and 8.33%, respectively, compared with HTTP, demonstrating many more hours in the duration of the batteries, which could be very useful in technological solutions for the transport industry.

An Economy-Oriented GPU Virtualization With Dynamic and Adaptive Oversubscription

GPU is becoming attractive around multiple academic and industrial area because of its massively parallel computing ability. However, there are still some obstacles which the GPU virtualization technologies should overcome to reach their maturity. These obstacles mainly include the problem of resource allocation strategy to guarantee possible higher yield. This shortage has already become an obvious barrier to the practical GPU usage in the cloud for satisfying business and academical requirements. There are many mature pieces of research in the area of oversubscribed cloud computing to enhance economic efficiency. However, the study on GPU oversubscription is almost blank for the just started use of GPU in cloud computing. This paper introduces gOver, an economy-oriented GPU resource oversubscription system based on the GPU virtualization platform. gOver is able to share and modulate GPU resource among workloads in an adaptive and dynamic manner, guaranteeing the QoS level at the same time. We evaluate the proposed gOver strategy with designed experiments with specific workload characteristics. The experimental results show that our dynamic GPU oversubscription solution improves the economic efficiency by 20% over traditional GPU sharing strategy, and outperforms the static oversubscription method by much better stability in QoS control.

Multi-user edge service orchestration based on Deep Reinforcement Learning

The fifth generation (5G) of mobile network offers a remarkable degree of flexibility to mobile operators, enabling them to provide users with effective and tailored network services. Software Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing have given the operator the opportunity to easily bring computational capacity to the edge and to support latency-sensitive services. While 5G standards have defined the technological and architectural frameworks to orchestrate services, finding effective resources management and QoS optimization policies is still an open research issue.In this paper, we propose an online orchestration methodology for a multi-user edge service. The orchestrator goal is to simultaneously maximize the QoS, and minimize the amount of resources needed.We provide a mathematical formulation to compute an optimal offline policy and derive an online approach based on a model-free Deep Reinforcement Learning (DRL) framework. As a novel feature, the DRL agent action is modeled as a parametric combinatorial problem. A tailored multi-objective reward function leads the agent towards an effective choice of parameters for such a model. Our models are built, trained and fine-tuned by exploiting real data.Extensive simulations in diverse scenarios show that our DRL online approach produces solutions with small gaps to the optimal offline ones, enabling the operator to both save resources and grant the users an adequate QoS level.

Tuneman: Customizing Networks to Guarantee Application Bandwidth and Latency

We examine how to provide applications with dedicated bandwidth and guaranteed latency in a programmable mission-critical network. Unlike other SDN approaches such as B4 or SWAN, our system Tuneman optimizes both routes and packet schedules at each node to provide flows with sub-second bandwidth changes. Tuneman uses node-level optimization to compute node schedules in a slotted switch and does dynamic routing using a search procedure with Quality of Service– (QoS) based weights. This allows Tuneman to provide an efficient solution for mission-critical networks that have stringent QoS requirements. We evaluate Tuneman on a telesurgery network using a switch prototype built using FPGAs and also via simulations on India’s Tata Network. For mission-critical networks with multiple QoS levels, Tuneman has comparable or better utilization than SWAN while providing delay bounds guarantees.

Access selection in heterogeneous wireless networks based on user preferences

Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users' requirements (in terms of resources, QoS, user's preferences), and operator policies that aim to maximise the utilisation of its network capacity and to deliver services with acceptable QoS levels for the largest number of customers. Thus, in this paper, we propose a new selection algorithm based on user's preferences. The comparison between the proposed scenarios is given based on several performance indicators. The results show the improvement achieved by increasing the resource utilisation and therefore the overall system capacity.

DEVELOPMENT OF THE CONCEPT FOR THE COMPUTATIONAL RESOURCES MANAGEMENT SOFTWARE IN THE CUSTOMER SERVICE SYSTEMS

Background. To date, there is no customer service system that does not involve information and computer systems. One of the most important issues in ensuring the reliability and reliability of such systems is the task of dynamic scaling and providing the required amount of computing resources at any time. This study was focused on the planning and deployment of computing infrastructure that is able to respond to significantly increased volumes of request flows, changes in the dynamics of load intensity, strict requirements for the quality of their service, etc.
 Objective. The purpose of the paper is to create a concept of virtual computing space to meet the needs of distributed customer service system, which takes into account the peculiarities of service, the computing node load nature, service quality requirements, and provides energy efficient. Developed models, methods will control the performance of distributed computing infrastructure and flow maintenance processes, reduce downtime of computing resources and provide services to end users at a given level of quality.
 Methods. Analysis of the operation of the node load assessment mechanism, which consists in a dynamic change in the intensity of control of the state of function nodes, showed the effectiveness of planning for a group of computing nodes..
 Results. The proposed approach to managing a heterogeneous computation environment to improve the efficiency of the service maintenance process in new generation systems is a unified solution for highly loaded distributed systems. The developed concept made it possible to avoid a decrease in the quality of service during surges of congestion and to maintain the indicators of the quality of service at a given level, provided that the resource utilization ratio is kept within the given limits
 Conclusions. In summary, a mathematical model of the problem of determining the maximum allowable load volume with a QoS level guarantee for a service node in a heterogeneous telecommunications environment was proposed.

Application Relocation in an Edge-Enabled 5G System: Use Cases, Architecture, and Challenges

With the growing development of 5G and its new services, edge computing is becoming the cornerstone of the ongoing network transformation. Its integration into 5G network infrastructure brings new research opportunities related to the design and implementation of high-performance systems, enabling the accomplishment of the three main promises of 5G: very high speed, low latency, and massive connectivity. This trend has generated strong interest in realizing effective life cycle management of latency-sensitive edge applications in order to achieve a high level of QoS while ensuring the service continuity in the case of user mobility. This article deals with the relocation of edge services, commonly called edge relocation, which aims to relocate edge application instances between edge clusters in order to ensure uninterrupted service. To achieve our objective, we propose a cloud-native edge-enabled 5G solution that complies with ETSI and 3GPP standards. The latter performs pre-relocation and relocation of edge applications in order to support the service continuity. Various scenarios of edge delocation are discussed, and illustrative use cases are provided. Next, a proof of concept is described to demonstrate how edge relocation can be implemented and leveraged to ensure service continuity in the case of infrastructure performance degradation. Finally, unique research opportunities are identified.

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing.

Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as “pay as per usage”. However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly.

Performance Evaluation of Different Raspberry Pi Models as MQTT Servers and Clients

Performance analysis for devices in Internet of Things (IoT) environments is an important consideration, especially with their increasing integration in technological solutions, worldwide. The Single Board Computers (SBCs) of the Raspberry Pi Foundation have been widely accepted by the community, and hence, they have been incorporated in numerous IoT projects. To ease their integration, it is essential to assess their network performance. In this paper, we made an empirical performance evaluation of one of the most popular network protocols for IoT environments, named the Message Queuing Telemetry Transport (MQTT) protocol, on Raspberry Pi. To do so, we set up two different testbeds scenarios and assessed the performance with benchmarks. At the software level, we focused on Mosquitto, a popular open-source MQTT broker implementation and client library. Our principal metric is the transmission time, but we also investigated the throughput. In our experiments, we varied several parameters, such as the size of the payload of the published messages, the WiFi bandwidth, the QoS level, the security level (MQTT vs. MQTT with TLS), and the hardware for the clients and broker. We focus mainly on packet sizes ranging from 100 to 25,000 bytes. We also investigate how these low-cost devices handle a TCP SYN flood attack. In the research work presented within this paper, we aim to guide developers, researchers, network administrators, and hobbyists who plan to use these low-cost devices in an MQTT or IoT network by showing the performance that they should expect according to different Raspberry Pi options.

Toward Energy-efficient Communication Protocol in WBAN: A Dynamic Scheduling Policy Approach

Wireless body area network is an emerging technology that has been able to provide a better experience of mobility and flexibility for humans using tiny and low power sensors inside, outside, or around the body compared to the traditional wired monitoring systems. Due to numerous constraints in size, energy consumption, and security of implant devices in the human body, it is still a significant research challenge to design these systems in a reliable and energy-efficient fashion. To provide quality of service, timely and secure delivery of real-time data needs be done without any loss. This paper attempts to provide a communication protocol in order to upgrade QoS levels in WBANs and reduce energy consumption in sensor nodes. To do so, the EDF real-time scheduling algorithm and its combination with the LLF scheduling algorithm are employed to prioritize sensor nodes for sending data packets. The proposed method could optimize the system performance when it is in the event of an overload and tasks miss their deadlines in a row. The OMNET++ simulation environment is used to evaluate the proposed solution's efficiency which checks packet delivery rate and mean-power consumption evaluation criteria in the sink and sensor nodes. This is done with different numbers of nodes in the network. The results show that the proposed strategy could provide an appropriate improvement in sending and receiving packets for body area networks.

Implementation of MQTT Protocol in Health Care Based on IoT Systems: A Study

Internet of things IoT systems have become one of the most promising technologies in all fields. Data transmission is one of the important aspects, and the tendency to messaging protocols is an important aspect of IoT systems. One of these most important protocols is MQTT. This protocol depends on the Publish/Subscribe model, and it is a lightweight protocol. Reliability, simplicity, quality of service levels, and being Resource-constrained make MQTT common in the IoT industry. This paper designed an IoT device that consists of the sensor MLX 90614 non-contact IR Temperature connected to a development board (Node MCU ESP8266). A person's temperature is one of the important vital signs. This system measures human temperature values and transmits the measured values to the Mosquitto broker by using the MQTT protocol in real-time. The technology used is Wi-Fi. The person or the doctor can read the patient’s temperature remotely through a program (Flutter Android Client) representing the subscriber. Also, MQTT protocol control packets of the system were analyzed using Wireshark. The three levels of QoS were used in subscriber clients to compare the throughput. The results indicate that QoS2 is more reliable and offers more throughput but more delay. The results also show that the average round trip time (RTT) of the MQTT protocol is five milliseconds which means optimal performance for IoT applications.

Chain RTS/CTS scheme with Opportunistic Channel Allocation

Current Wi-Fi standards lack in terms of providing reliable multi-hop links for bandwidth-hungry applications. As a result, aerial networks which need reliable & high bandwidth multi-hop communication links, fail to maintain QoS levels. In this paper, we take a deeper look at our earlier work, Chain RTS/CTS, which makes channel reservations by repeating the RTS frame of the source of the e2e link towards the destination node using alternating channels. After receiving the RTS frame, destination node starts backward CTS chain where CTS frames are repeated similarly using the same route with forward RTS chain towards the source node of e2e link. Additionally, we introduce Opportunistic Channel Allocation designed to increase fairness when operating at 5 GHz band. Our simulations show 37% throughput improvement in multi-hop links with 68% increased link setup time when compared to simple channel hopping enhancement scheme for single e2e connection. When there are multiple concurrent e2e connections, our solution improves the throughput by 27% compared to Classical Channel Hopping (which alternates channels at each hop) with only 11% lower Jain’s Fairness index.

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Method for Dynamic Service Orchestration in Fog Computing

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.