Quality Of Service Parameters Research Articles

Rough set-based quality of service optimisation guidelines with stack parameter in MANET

In mobile ad hoc network (MANET), providing a guaranteed service for packet delivery has to pass through several hurdles. Providing guaranteed service should address multiple quality of service (QoS) parameters under uncertain network conditions. The QoS parameters should be prioritised in consideration of MANET constraints and applications running on MANET. The QoS aspects of MANET are impacted by the multi-layer stack parameters. An analysis has been conducted to identify the trade-off point for configuring the stack parameters to achieve better QoS in packet delivery. The outcome of analysis is discussed in terms of packet error rate, energy efficiency, goodput and delay with the support of rough set theory (RST). The multi-objective analysis capabilities of RST make it suitable for taking dynamic decision to prioritise the QoS parameter. The rough set derived decision rules are filtered out based on probabilistic properties like strength, certainty and coverage of the decision rules.

Cost-enabled QoS aware task scheduling in the cloud management system

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.

Introducing test data-set for the QoS-aware web-services discovery and composition

The QoS-aware web services discovery and composition are considered as two important, open and hot issues in Service-Oriented Architecture (SOA). By definition, web-service discovery is about how to select the best web-service for a role in a workflow among available web-services whereas web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. The criteria, upon which the web service selection, position, and composition processes are based, may or may not cover the Quality of services (QoS) parameters. The latter case would turn the name of the job into QoS-aware web services discovery and composition. In this article, the QoS-WSC test data-set is introduced for the QoS-aware web-services discovery and composition with regard to specific potentials and needs of the web-services world. In this respect, at first, an interface has been designed to define QoS for the roles attended in a service-oriented architecture. Then a solution, which allows comparison among web-services through the calculation of similarity of the request to amounts of the QoS parameters of available web services, is proposed. The similarity is obtained using the internal multiplication of two vectors of weighted numerical parameters representing request parameters and QoS parameters of available web services. The weights are technically significant coefficients, which can be obtained from the QoS-WSC data-set, which is assembled out of a rule-based integration of two well-known prior data sets in the field of web services.

Various Methods of Link Design for Transferring Data in Wireless Sensor Networks for different Applications

ABSTRACTThis paper offers the different type transmission schemes used in wireless sensor networks. The wireless sensor network used in a number of applications such as Agriculture, Military, Medical, Multimedia etc. According to the applications there are different quality of service parameters and designing requirements for different layers in network design. While designing any ad hoc network or sensor network many designing principals and challenges are taken into consideration. For the designing of protocols layering in ad hoc sensor network basically a five layer model is taken into consideration. These layers are application, transport, network, Medium access control and physical layer. In this paper different types of designing constraints associated with physical and medium access layer has been introduced for different applications in sensor networks. In that manner this paper helps us to select the designing of a link for particular application.

Secure Cloud Data Storage System using Hybrid Paillier-Blowfish Algorithm

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically reconfigured in order to deliver optimum resource utilization by exploiting the pay-per-use model. However, concerns around security have been an impediment in the extensive adoption of the cloud computing model. In this regard, advancements in cryptography, accelerated by the wide usage of the internet worldwide, has emerged as a key area in addressing some of these security concerns. In this document, a hybrid cryptographic protocol deploying Blowfish and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA) techniques. Algorithms for secure data storage protocol in two phases have been presented. The proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and cipher-text size. Simulations have been carried out with Oracle Virtual Box and Fog server used on an Ubuntu 16.04 platform. This grouping of asymmetric and homomorphic procedures has demonstrated enhanced security. Compression usage has helped in decreasing the storage space and computation time. Performance analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks, throughput, and stream length for different modes of block cipher mode has been carried out. Security analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis 2.7.1. Similarly, for halting the aforementioned approaches and for regulating traffic, firewall protection has been generated in the chosen hybrid algorithms. Finally, enhancements in the performance of the Paillier and Blowfish hybrid scheme with and without compression compared to the existing schemes using RSA and AES procedures have been demonstrated.

DETERMINATION OF THE ELECTRONIC ENERGY LEVELS OF A QUANTUM WELL HETEROSTRUTURE Zn(1-x) MgxO/ZnO/Zn1-xMgxO.

Nowadays, Cloud Computing is the preferred choice of IT organizations as it provides dynamic and pay-per-use based services to its clients. The main problem of the cloud computing is security and privacy of data which occurs because of its open and distributed structural design. Thus, it becomes essential to design an intrusion detection system (IDS) to provide security to the system. IDS help to protect cloud ecosystem from the malicious activities of the attacker. In this research work, IDS is designed to detect malicious node by using an optimization technique along with the concept of AI (Artificial Intelligence).The Cuckoo Search (CS) algorithm is used as an optimization technique and it is a meta-heuristic approach which is inspired by the behavior of birds and CS algorithm operates on the healthiness function. After the feature optimization, different types of feature are categorized based on the node’s nature into two types namely normal and attackers. On the basis of extracted features, train the proposed system using ANN (artificial neural network) as classifier to classify the attackers which affect the networks in cloud environment. ANN is a multiclass classifier which is used to solve multi-class problems and due to this reason ANN is used in proposed work with CS optimization algorithm. In this research work, ANN is used to distinguish between attacker nodes and genuine nodes based on their optimized feature sets. Thus, instead of passing data to the attacker node, the node passes the data to the genuine node and hence, the system is protected. By using the above motioned concept in cloud computing the possibility of results improvement become high because only genuine node involving in the data transmission process. To know the performance of the system, the QoS (Quality of service) parameters such as PDR (Packet delivery ratio), energy consumption rate and total delay with and without prevention algorithm are measured. The development of proposed ARS is done in the MATLAB 2016a software with the help of various toolboxes like data acquisition, artificial intelligence, optimization and curve fitting.

Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

In the Next Generation Radio Networks (NGRN), there will be extreme massive connectivity with the Heterogeneous Internet of Things (HetIoT) devices. The millimeter-Wave (mmWave) communications will become a potential core technology to increase the capacity of Radio Networks (RN) and enable Multiple-Input and Multiple-Output (MIMO) of Radio Remote Head (RRH) technology. However, the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently. The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests. To handle this issue effectively, Machine Learning (ML) algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity conditions. This paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm, namely K-Nearest Neighbor (KNN), to improve the communication Quality of Service (QoS) in real-time IoT networks. KNN supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher power. The experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters, including communication reliability, latency, and throughput.

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained.

Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering

With the proliferation of Internet-of-Things and continuous growth in the number of web services at the Internet-scale, the service recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service recommendation is the Quality-of-Service (QoS) parameter, which depicts the performance of a web service. In general, the service provider furnishes the value of the QoS parameters before service deployment. However, in reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task, and thus, the QoS prediction has gained significant research attention. Multiple approaches are available in the literature for predicting service QoS. However, these approaches are yet to reach the desired accuracy level. In this article, we study the QoS prediction problem across different users, and propose a novel solution by taking into account the contextual (more specifically, location) information of both services and users. Our proposal includes two key steps: (a) hybrid filtering, and (b) hierarchical prediction mechanism. On the one hand, the hybrid filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical prediction mechanism is to estimate the QoS value accurately by leveraging hierarchical neural-regression. We evaluated our framework on the publicly available WS-DREAM datasets. The experimental results show the outperformance of our framework over the major state-of-the-art approaches.

FDMOABC: Fuzzy Discrete Multi-Objective Artificial Bee Colony approach for solving the non-deterministic QoS-driven web service composition problem

The multi-objective quality of service (QoS)-driven web service composition problem (MOQWSCP) aims to find the best combinations of atomic web services (i.e. composite service) to answer high quality of the optimized QoS criteria in a way that maximize benefit QoS parameters such as availability and reliability and minimize the negative ones like price and response time, where the users’ requirements should be satisfied. Due to the dynamic environments in which the elementary services are invoked, some services’ QoS parameters are often ambiguous and uncertain, so, it is inappropriate to express them by fixed values. Hence, The QoS parameters are represented by trapezoidal fuzzy numbers. Thus, we formulate MOQWSCP as a fuzzy multi-objective optimization problem (FMOQWSCP). A fuzzy discrete multi-objective artificial bee colony (FDMOABC) approach is provided to solve the formulated FMOQWSCP, for which we have integrated a new fuzzy ranking method to cope with solutions sorting and a new fuzzy distance measure that is used to control and keep the diversity of FDMOABC’s solutions. Furthermore, a fuzzy multi-criteria decision-making method (FMCDMM) is provided to determine the best composite service among the Pareto-optimal solutions generated by FDMOABC. Finally, two kinds of comparisons are performed to validate the performance and the effectiveness of FDMOABC and FMCDMM methods. In the former, the combined FDMOABC and FMCDMM methods is compared against the fuzzy single objective optimization approaches TGA and EFPA, whereas in the later, a multi-objective optimization comparison is performed among FDMOABC and the fuzzy-extended versions of NSGA-II and SPEA2 algorithms.

WITHDRAWN: An optimal SLA based task scheduling aid of hybrid fuzzy TOPSIS-PSO algorithm in cloud environment

Cloud computing is an important technology for bulk data storage, resource mobilization and online access to computer services. In the cloud computing platform there are plenty of resources, but the foremost challenge is to allocate tasks. The vital issue in scheduling is how the entire task could be allocated with maximized resource utilization to a resultant virtual machine, existing scheduling algorithms mainly focused on minimization of the task execution time while ignoring the SLA (Service Level Agreement) and QoS (Quality of Service) assurance. This paper thrives to accomplish the total task with eminent resource utilization, minimum migration cost, and minimum utilization of energy. For attaining these objectives, the proposed method utilizes the hybrid algorithm such as Fuzzy-TOPSIS and particle swarm optimization (PSO) approach. Initially the available task and the no. of VMs (virtual machines) are optimized by PSO algorithm. In the cloud, the multi-objective SLA based task scheduling problem is solved by the Fuzzy TOPSIS which uses the weighted sum of energy, cost and execution time as an objective function. Fuzzy TOPSIS method is work positively for many applications and impacts liberally on real world decision making concerns. The proposed approach is investigated based on the distinct evaluation metrics. Our proposed algorithm outperforms the existing approaches and achieves better results in terms of QoS parameters. The implementation had done using JAVA with CloudSim simulator.

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.

Implementasi dan Analisis Quality of Service (QoS) pada OpenMeetings dengan Virtual Private Network (VPN)

Abstract — Video Conference is a communication service that can be used to bring together two users (client) or more. Video conferencing can be used for a variety of activities that require communication in real-time without having to come face to face directly. One open-source that can be utilized as a means of communicating is OpenMeetings. OpenMeetings uses IP and in the same network as a means of conducting video conferencing between clients. But if a client is not in the same network, it can utilize the Virtual Private Network (VPN) technology. The VPN can be remote by using a MikroTik router. The Video conferencing service requires fairly high and stable connectivity. Quality of Service (QoS) can be used whether the network is eligible for video conferencing. The QoS parameters used are throughput and packet loss. The QoS test can be done using Wireshark.

STAR: SLA-aware Autonomic Management of Cloud Resources

Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing dedicated cloud services that ensure user's dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.

ANALISIS QOS VIDEO STREAMING JARINGAN WIRELESS (STUDI KASUS: TAMAN INTERNET ENGKU PUTRI BATAM)

The increasing number of internet users in Indonesia has an impact on increasing the number of users of streaming video. The increase in the number of users is based on technological developments with the emergence of mobile devices such as notebooks, tablets and smartphones equipped with internet access via wireless networks. Unlike networks using cables, the characteristics of using video streaming over wireless networks are limited when compared to those using cables. To access various applications on the internet, including video streaming, a stable internet service is needed. For this reason, a Quality of Service (QoS) measurement research is needed which is carried out at one of the wireless network provider points in the city of Batam, namely Taman Internet Engku Putri. QoS can be used to measure and analyze the stability and quality of a wireless network. The parameters used in the measurement and analysis of QoS are Throughput, Delay, Jitter and Pakect Loss which will be tested on video streaming services. The test will be carried out at certain times such as peak hours and low hours. Low hours usually range from 08.00 WIB - 15.00 WIB and rush hour usually ranges from 15.00 WIB - 21.00 WIB. The results of measurement and analysis using four QoS parameters show that the video streaming service using wireless networks in Taman Internet Engku Putri is in the medium category.

Max-gain relay selection scheme for wireless networks

Next generation wireless systems are supposed to handle high amount of data with broader coverage and high quality of service (QoS). When a signal travels from a source to destination, the signal quality may suffer from the fading, which makes it difficult to receive correct messages. To handle the impact of fading, various diversity techniques are performed with Multiple Input Multiple Output (MIMO). Considering cooperative wireless networks, virtual MIMOs are being used, which also called cooperative diversity. In this paper, we propose a max-gain relay selection scheme (MGRS) for buffer-aided wireless cooperative networks. This scheme determines the best link using the maximum gain based on quality of link and available buffer size. The time slot is divided into two parts, one is used to choose the best link from the source to relay transmission (odd slot) and another time slot (even) is used based on the selection of the best link from the relay to destination. Markov chain model is use to measure buffer status and QoS parameters to evaluate the performance. The proposed scheme provides better QoS (12%) compared to the existing relay selection schemes with respect to throughput, end-to-end delay and outage probability.

A Systematic Review of Quality of Services (QoS) in Software Defined Networking (SDN)

In modern life the internet has become backbone of digital society which is the packet switched distributed network and connected with almost every digital devices, available everywhere in the world. The traditional network carries few challenges like, dependency on vendors, difficulty to manage large network, dynamically changing of forwarding policies and more. To overcome such challenges, today the idea of Software Defined Networking (SDN) came into existence. The basic idea behind SDN is to implement programmable network by separately managing the control plane and data plane to improve the efficiency of network performance. The main problem with SDN is the Controller Placement Problem (CPP), which gives the overview of whole network. Today the main focus of the researchers is to solve the CPP. CPP is a NP-hard problem because the network should consist of minimum controllers and controllers should be placed on appropriate locations. For the large size network, controller deployment is difficult to manage. But the challenge in this area is Quality of Services (QoS) in respect of controller management. This paper investigates the systematic review of QoS based on controller’s problems, analyzed the current research and summarized the findings of the different controller’s performance based on QoS parameters e.g. reliability, scalability, consistency and load balancing. Finally, this paper also highlights the research challenges to improve the QoS in SDN.

A regression tree predictive model for virtual machine startup time in IaaS clouds

Cloud computing provides effective ways to rapidly provision computing resources over the Internet. For a better management of resource provisioning, the system requires to predict service-level agreements (SLAs) such as virtual machine (VM) startup times under various conditions of computing resources. The VM startup time is an important SLA parameter, which can impact other SLA parameters such as service initiation time and VM scale out times. By predicting VM startup times, Cloud providers can improve Cloud users’ expectations. Various quality of service (QoS) parameters have been considered in different resource allocation frameworks. Also, there are several efforts addressing QoS prediction in cloud environments. However, little research has considered VM startup time as a QoS parameter. In this paper, we propose a regression tree model for predicting average, minimum, and maximum VM startup times. To test the efficiency of our model, we implemented the model in an OpenStack test environment. The test results show that our model predicts VM startup times with an average accuracy of 91.81%.

Efficient task scheduling on virtual machine in cloud computing environment

PurposeCloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.Design/methodology/approachCloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.FindingsTo evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.Originality/valueThe simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.