QoS Requirements Of Applications Research Articles

MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.

Dynamic extended sigmoid-based scheduling with virtualized RB allocation for maximizing frequency numerology efficiency in 5G/B5G NR networks

New trends of extremely ultra-low delay, ultra-high reliability, long-distance high-moving, etc., are strongly required for achieving Emergency resecure, Active Safe Driving (ASD), IoV communications, LEO communications, etc. Thus, 3GPP 5 G/B5G specifies flexible numerology technology providing different frequency SCS modes with different slot-times for diverse types of flows requiring different 5QIs in terms of delay, data rate, jitter, loss probability, and reliability. Although based on the frequency numerology, 3GPP Release 17 specifies three types of RB allocations, i.e., Types 0, 1, and dynamic switching allocations for UL access, such static and consecutive RB allocations result in inefficient numerology and frequency spectrum efficiency. As a result, typical critical issues exhibit in B5G NR, including high mutual exclusion (MX) of numerology, RB state independency flow scheduling, and static RB allocations suffering from dynamic flowing traffic. Thus, this paper proposes the Extended Sigmoid-based Scheduling with a Virtualized RB Allocation (SSA) consisting of two phases. Phase 1 proposes an Extended Sigmoid-based Cost-based Flow Scheduling (SCFS) algorithm to dynamically differentiate flow priorities. Phase 2 proposes a dynamic Virtualized RB pre-Allocation (VRBA) mechanism to maximize radio RB utilization and minimize mutual exclusion in 5 G frequency numerology. Numerical results indicate that the proposed SSA outperforms the compared approaches in network delay, jitter, resource utilization, network reward, and net-profit. Consequently, this paper achieves several objectives and contributions: 1) analyzing the QoS requirements of different applications with limited frequency spectrum, 2) proposing vRB state-related dynamic flow scheduling algorithm to maximize network efficiency, 3) minimizing numerology MX for vRB allocation, and 4) proposing adaptive cost-reward-based scheduling.

A dual-mode MAC protocol with service differentiation for industrial IoT networks using wake-up radio

The Internet of Things (IoT) has successfully revolutionized our society and all aspects of our daily lives. Wake-up radio (WuR) technology on its part has contributed to the success of IoT by significantly reducing latency and power consumption. Moreover, emerging mission critical Industrial IoT (IIoT) applications are time sensitive and generate more stringent delay and reliability constraints. Therefore, WuR-based MAC protocols must be carefully designed to meet these requirements. In this paper, a Dual-Mode MAC protocol (D2MAC) is proposed to fulfill QoS requirements of both delay-tolerant and real-time IoT applications by using service differentiation in the context of WuR-based IoT networks. Our protocol adopts two operation modes: normal mode, used in the case of normal traffic conditions, and priority mode for time–critical data. A Markov chain model is used to evaluate the performance of D2MAC. A simulation model based on MiXiM/ OMNet++ framework is also proposed to validate this analytical model. We illustrate the benefits of using the priority mode (PCA-WuR) to fulfill the requirements of IIoT applications. We also demonstrate that the Performance of PCA-WuR strongly depends on the acceptable delay limit for packet transmission.

Multi-Radio Access Software-Defined Vehicular Network

With the promising advances in the telecommunication and automotive industries, there are increasing concerns on the reliability of Intelligent Transportation Systems (ITS) applications and standardization efforts. Nowadays, vehicles are empowered with innovative communications and sensing capabilities to accelerate next-generation Connected and Autonomous Vehicles (CAV) or Vehicle-to-Everything (V2X) communications. Furthermore, V2X is based on many radio access technologies including Dedicated Short-Range Communications (DSRC), Cellular-V2X, and millimeter-wave. It is expected that a single technology is incapable to support the diverse QoS and reliability requirements of vehicular safety and non-safety applications. Therefore, recent research works have investigated and developed different approaches to enable multi-radio access technologies (Multi-RAT) in V2X networks to support these multitudes of requirements. In addition, the heterogeneity of diverse radio access technologies and rigidity in their deployment increases the complexity of the network management. Complications may also arise from inefficient resource utilization due to the complexity of an optimum RAT selection or multi-link establishment through Multi-RATs. Therefore, a global monitoring paradigm such as Software-Defined Vehicular Network (SDVN) has been identified as a solution to reduce this bottleneck, by adding flexibility and agility to the vehicular network. This paper extensively reviews recent related works on Multi-RAT V2X networks focusing on the opportunistic selection mechanism of RAT, allocation of radio resources, and simultaneous establishment of multi-link over a heterogeneous network. Consequently, we have outlined the SDVN architecture, layers, features, and significance towards enhancing the performance of such Multi-RAT networks. Finally, several potential challenges with probable solutions and opportunities are discussed.

Imitation Learning Based Heavy-Hitter Scheduling Scheme in Software-Defined Industrial Networks

To realize flexible networking and on-demand topology reconstructing, Software-Defined Industrial Networks(SDIN) are increasingly embracing the flat structure. Similar to SDN, SDIN suffers from low traffic scheduling efficiency caused by large and imbalanced flows, known as the heavy hitters problem. Due to such heavy hitters, industrial networks may fail to satisfy application's QoS requirements, which results in more severe damages. To improve flow scheduling efficiency under heavy hitters, this article introduces a novel Imitation Learning-based Flow Scheduling (ILFS) method. ILFS utilizes P4-based In-band Network Telemetry(INT) technology to collect fine-grained, real-time traffic data from SDIN's data plane. In the control plane, it integrates the Generative Adversarial Imitation Learning (GAIL) model with a Soft Actor Critic (SAC) to preserve the experiences of flow, thereby better scheduling large flows. Our experiments thoroughly compare ILFS's performance with several state-of-the-art traffic scheduling strategies. The results indicate that ILFS successfully controls the link bandwidth utilization between 10% and 80% and significantly improves the average network throughput and link utilization rate.

Experimental Performance Evaluation and Frame Aggregation Enhancement in IEEE 802.11n WLANs

The IEEE 802.11n standard promises to extend today’s most popular WLAN standard by significantly increasing reach, reliability, and throughput. Ratified on September 2009, this standard defines many new physical and medium access control (MAC) layer enhancements. These enhancements aim to provide a data transmission rate of up to 600 Mbps. Since June 2007, 802.11n products are available on the enterprise market based on the draft 2.0. In this paper we investigate the effect of most of the proposed 802.11n MAC and physical layer features on the adhoc networks performance. We have performed several experiments in real conditions. The experimental results demonstrated the effectiveness of 802.11n enhancement. We have also examined the interoperability and fairness of 802.11n. The frame aggregation mechanism of 802.11n MAC layer can improve the efficiency of channel utilization by reducing the protocol overheads. We focused on the effect of frame aggregation on the support of voice and video applications in wireless networks. We also propose a new frame aggregation scheduler that considers specific QoS requirements for multimedia applications. We dynamically adjust the aggregated frame size based on frame's access category defined in 802.11e standard.

A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery.
 HIGHLIGHTS
 
 The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones
 To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced
 The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one
 
 GRAPHICAL ABSTRACT

Fog Computing Service Provision Using Bargaining Solutions

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively.

Differences Between Major Depression Disorder and Controls in the Recognition of Facial Expression: Evidence From Event-Related α Oscillations

Traditional routing in the Internet is best-effort which makes it challenging for video streaming since no throughput, jitter, delay or loss rate is guaranteed. As different paths have different characteristics, path differentiation such as multipath routing is a promising technique to be used for meeting QoS requirements of media-intensive applications. Using overlay networks different paths are offered which enable more flexibility in QoS and congestion control while the reliability of the connections is enhanced.Software Defined Networking (SDN) is known to be a promising solution to the problems of routing as it provides fine-grained control over packet handling. Relying on SDN, we propose an adaptive multipath provisioning scheme ensuring maximal bandwidth and resiliency of media transfer in overlay networks. The scheme is a time slot-based approach which dynamically finds multipaths. It relies on both active probing and traffic prediction.The experimental results confirm that a more accurate prediction together with more frequent probing lead to fewer number of path re-calculation and also indicate that the proposed scheme enhances the reliability of connections while a more balanced load is achieved in the network compared to the shortest path-based scheme.

Efficient network selection using multi fuzzy criteria for confidential data transmission in wireless body sensor networks

In the last decade, due to wireless technology’s enhancement, the people’s interest is highly increased in Wireless Body Sensor Networks (WBSNs). WBSNs consist of many tiny biosensor nodes that are continuously monitoring diverse physiological signals such as BP (systolic and diastolic), ECG, EMG, SpO2, and activity recognition and transmit these sensed patients’ sensitive information to the central node, which is straight communicate with the controller. To disseminate this sensitive patient information from the controller to remote Medical Server (MS) needs to be prolonged high-speed wireless technology, i.e., LTE, UMTS, WiMAX, WiFi, and satellite communication. It is a challenging task for the controller to choose the optimal network to disseminate various patient vital signs, i.e., emergency data, normal data, and delay-sensitive data. According to the nature of various biosensor nodes in WBSNs, monitor patient vital signs and provide complete intelligent treatment when any abnormality occurs in the human body, i.e., accurate insulin injection when patient sugar level increased. In this paper, first, we select the optimal network from accessible networks using four different fuzzy attribute-based decision-making techniques (Triangular Cubic Hesistent Fuzzy Weighted Averaging Operator, Neutrosophic Linguistic TOPSIS method, Trangualar Cubic Hesistent Fuzzy Hamacher Weighted Averaging Operator and Cubic Grey Relational Analysis) depending upon the quality of service requirement for various application of WBSNs to prolong the human life, enhanced the society’s medical treatment and indorse living qualities of people. Similarly, leakage and misuse of patient data can be a security threat to human life. Thus, confidential data transmission is of great importance. For this purpose, in our proposed scheme, we used HECC for secure key exchange and an AES algorithm to secure patient vital signs to protect patient information from illegal usage. Furthermore, MAC protocol is used for mutual authentication among sensor nodes and Base Stations (BS). Mathematical results show that our scheme is efficient for optimal network selection in such circumstances where conflict arises among diverse QoS requirements for different applications of WBSNs.

Prioritized Energy-Efficient MAC Protocol in Wireless Sensor Networks

In this paper, we propose a MAC protocol that provides energy efficiency and QoS for wireless sensor networks. Because it does not classify the data according to the characteristics, traditional MAC protocols for Wireless Sensor Networks cannot provide service differentiations for different traffic classes. For the purpose of QoS support, the input data to the node is divided into priority classes according to the transmission emergency. Depending on the urgency of the data, the protocol can provide time slots for nodes and take different contention window sizes for different traffic classes. Nodes use the reinforcement learning-based control mechanism to actively infer the status of other nodes to classify effectively under various kinds of traffic conditions. The proposed protocol can meet the QoS requirements of delay-sensitive applications and can effectively deal with the problem of a multi-channel hidden terminal with the use of multi-queue. For the next purpose, the proposed protocol is energy efficient because it is based on sleep mode. To improve the energy efficiency, each node has a short non-overlapping listening interval, saving energy in idle listening intervals. This is one of the ways to efficiently use the energy of a node to extend the life of the entire network. This achieves low power consumption by increasing the throughput of data processed across the network and enabling efficient transmission. The classified data can be further improved by rearranging the data according to the nature of the transmitted data in the queue.

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

A cloud-fog computing system for classification and scheduling the information-centric IoT applications

Cloud users submit their applications for processing on the provider's infrastructure expecting a diverse set of computational, storage, or communicational resources. Classifying the tasks of these applications based on their needs and scheduling them onto the most suitable resources is a big obstacle. To manage with such issues, a system that supports classification and scheduling them on to the most efficient resource for execution is essential. An information-centric (IC) internet of things (IoT) system for cloud-fog computing environment, which supports these two key functionalities through ensured QoS was proposed in this article. The experimental simulations and performance analysis of the proposed approach was prepared by considering a global cloud with three servers, four fogs, and four types of IoT applications with a sum of 3,000 tasks collectively. Experimental outcomes state that the anticipated cloud-fog computing architecture reduces the average makespan time and service cost up to a maximum of 11.8%, and 12.63% respectively when compared with other existing approaches. Also, the cost-makespan trade-off (CMT) gains with the proposed CSIc_IoTA is up to 28.5% and can guarantee better QoS requirements of real-time IoT applications.

Reliable and efficient data dissemination schemein VANET: a review

Vehicular ad-hoc network (VANET), identified as a mobile ad hoc network MANETs with several added constraints. Basically, in VANETs, the network is established on the fly based on the availability of vehicles on roads and supporting infrastructures along the roads, such as base stations. Vehicles and road-side infrastructures are required to provide communication facilities, particularly when enough vehicles are not available on the roads for effective communication. VANETs are crucial for providing a wide range of safety and non-safety applications to road users. However, the specific fundamental problem in VANET is the challenge of creating effective communication between two fast-moving vehicles. Therefore, message routing is an issue for many safety and non-safety of VANETs applications. The challenge in designing a robust but reliable message dissemination technique is primarily due to the stringent QoS requirements of the VANETs safety applications. This paper investigated various methods and conducted literature on an idea to develop a model for efficient and reliable message dissemination routing techniques in VANET.

Continuous Reasoning for Managing Next-Gen Distributed Applications

Continuous reasoning has proven effective in incrementally analysing changes in application codebases within Continuous Integration/Continuous Deployment (CI/CD) software release pipelines. In this article, we present a novel declarative continuous reasoning approach to support the management of multi-service applications over the Cloud-IoT continuum, in particular when infrastructure variations impede meeting application's hardware, software, IoT or network QoS requirements. We show how such an approach brings considerable speed-ups compared to non-incremental reasoning.

A Distributed Network Q-Mac with Quality of Service Provisioning

A new distributed Cognitive Radio network Q-MAC has been proposed in the paper. The Q-MAC Protocol provides an efficient approach to address quality of service provisioning of delay sensitive applications. This MAC Protocol guarantees the QoS of network for such applications by giving priority to applications with higher time delay requirements in the stage of channel reservation. The performance of the Q-MAC Protocol with those of two existing protocols are compared. The results show that the Q-mac provides better throughput than those existing protocols. The analysis model and simulation results show that the designed Q-MAC Protocol is simple and effective. This Protocol can recognize and meet the QoS requirements of delay-sensitive applications and can effectively deal with the problem of multi-channel hidden terminal.

Severity-Based Prioritized Processing of Packets with Application in VANETs

To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

Fast Inter-Base Station Ring (FIBR): A New Millimeter Wave Cellular Network Architecture

Fifth Generation (5G) Millimeter Wave (mmWave) cellular networks are expected to serve a large set of throughput-intensive, ultra-reliable, and ultra-low latency applications. To meet these stringent requirements, while minimizing the network cost, the 3rd Generation Partnership Project has proposed a new transport architecture, where certain functional blocks can be placed closer to the network edge. In this architecture, however, blockages and shadowing in 5G mmWave cellular networks may lead to frequent handovers (HOs) causing significant performance degradation. To meet the ultra-reliable and low-latency requirements of applications and services in an environment with frequent HOs, we propose the Fast Inter-Base Station Ring (FIBR) architecture, where Base Stations (BSs) that are in close proximity are grouped together, interconnected by a bi-directional counter-rotating buffer insertion ring network. FIBR enables high-speed control signaling and fast-switching among BSs during HOs, while allowing the user equipment to maintain a high degree of connectivity. We demonstrate that the FIBR architecture efficiently handles frequent HO events in mmWave cellular systems, and thus more effectively satisfies the QoS requirements of 5G applications.

Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

The process of analyzing big data and other valuable information is a significant process in the cloud. Since big data processing utilizes a large number of resources for completing certain tasks. Therefore, the incoming tasks are allocated with better utilization of resources to minimize the workload across the server in the cloud. The conventional load balancing technique failed to balance the load effectively among data centers and dynamic QoS requirements of big data application. In order to improve the load balancing with maximum throughput and minimum makespan, Support Vector Regression based MapReduce Throttled Load Balancing (SVR-MTLB) technique is introduced. Initially, a large number of cloud user requests (data/file) are sent to the cloud server from different locations. After collecting the cloud user request, the SVR-MTLB technique balances the workload of the virtual machine with the help of support vector regression. The load balancer uses the index table for maintaining the virtual machines. Then, map function performs the regression analysis using optimal hyperplane and provides three resource status of the virtual machine namely overloaded, less loaded and balanced load. After finding the less loaded VM, the load balancer sends the ID of the virtual machine to the data center controller. The controller performs migration of the task from an overloaded VM to a less loaded VM at run time. This in turn assists to minimize the response time. Experimental evaluation is carried out on the factors such as throughput, makespan, migration time and response time with respect to a number of tasks. The experimental results reported that the proposed SVR-MTLB technique obtains high throughput with minimum response time, makespan as well as migration time than the state -of -the -art methods.

An efficient virtual CPU scheduling in cloud computing

In cloud computing, fine-grained virtual CPU scheduling techniques are essential in hiding physical resources from running applications and mitigating the decrease in performance upon virtualization. However, evaluating and predicting the behaviors of virtual processors is getting harder because of the diverse QoS requirements of cloud applications. In this paper, we propose a novel virtual CPU scheduling scheme to provide a high I/O performance for cloud applications. We present an evaluation function that evaluates the task characteristics of virtual machines by observing the amount of resource consumption of each virtual processor. Based on the evaluation function, the proposed scheduling scheme controls the priorities of virtual machines adaptively for fair distribution in handling I/O requests. Because our scheme evaluates both CPU-intensiveness and I/O-intensiveness of virtual machines, it provides high performance in terms of responsiveness even for various types of tasks. We implemented and experimented the proposed scheme on a virtual machine monitor. The experimental results showed that the proposed scheme increases the responsiveness and I/O bandwidth of virtual machines.