From the Queue to the Quality of Service Policy: A Middleware Implementation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_61Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology. To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the communications and control components of communication. The paper introduces the Queue-based Quality of Service Cycle concept for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service Policies controls directly the message queues to obtain the user-defined parameters values.The middleware architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). From the Queue to the Quality of Service Policy: A Middleware Implementation. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer Verlag (Germany). 432-437. doi:10.1007/978-3-642-02481-8_61S432437Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (December 2005)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC®. Sensor Web Enablement: Overview And High Level Architecture, OpenGIS Consortium Inc (2006)Poza, J.L., Posadas, J.I., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multi-media and QoS: A Survey 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet, pp. 1–37, XP002219363 (August 1998)ITU-T Recommendation E.800 (0894). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability (1994)Stuck, B.W., Arthurs, E.: A Computer & Communications Network Performance Analysis Primer. Prentice Hall, Englewood Cliffs (1984)Jain, R.: The art of Computer Systems Performance Analysis. John Wiley & Sons Inc., New york (1991)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems. Concepts and Design, 3rd edn. Addison Wesley, Madrid (2001)Jung, J.-l.: Quality of Service in Telecommunications Part II: Translation of QoS Pa-rameters into ATM Performance Parameters in B-ISDN. IEEE Comm. Mag., pp. 112–117 (August 1996)Wohlstadter, E., Tai, S., Mikalsen, T., Rouvellou, I., Devanbu, P.: GlueQoS: Middleware to Sweeten Quality-of-Service Policy Interactions. In: ICSE, 26th International Conference on Software Engineering (ICSE 2004) (2004

A quality of service framework for dependability in large-scale distributed systems

As recognition grows within industry for the advantages that can be gained through the exploitation of large-scale dynamic systems, a need emerges for dependable performance. Future systems are being developed with a requirement to support mission critical and safety critical applications. These levels of criticality require predictable performance and as such have traditionally not been associated with adaptive systems. The software architecture proposed for such systems takes its properties from the service-oriented computing paradigm and the communication model follows a publish/subscribe approach. While adaptive, such architectures do not, however, typically support real-time levels of performance. There is scope, however, for dependability within such architectures through the use of Quality of Service (QoS) methods. QoS is used in systems where the distribution of resources cannot be decided at design time. In this paper a QoS based framework is proposed for providing adaptive and dependable behaviour for future large-scale dynamic systems through the flexible allocation of resources. Simulation results are presented to demonstrate the benefits of the QoS framework and the tradeoffs that occur between negotiation algorithms of varying complexities

Custom Windows Performance Counters Monitoring Mechanism for Measuring Quality of Service Attributes and Stability Coefficient Service-Oriented Architecture

Service-Oriented Architecture (SOA) has been widely used for different types of systems as their underlying architecture. The most popular technology that implements the SOA is web service. When several web services provide same functionalities, Quality of Service (QoS) of web services turn to be an important issue. In this study, monitoring is used in order to measure QoS attributes of web services in SOA. Several monitoring mechanisms have been proposed. Windows Performance Counters (WPC) is one of approaches for monitoring services at provider-side. However, WPC monitoring approach has a limitation and it can be employed just for WCF services. Moreover, predefined system counter values do not map to QoS values properly. In this research, a new provider-side monitoring mechanism which is based on Custom Windows Performance Counters (CWPC) is proposed in order to overcome current limitations. CWPC will be set to measure QoS attributes of web services such as response time, throughput and reliability properly. The results of CWPC monitoring are useful in taking decision in adjusting suitable monitoring interval for the system. Additionally, the result verifies that CWPC is an accurate monitoring approach for measuring QoS attributes. Besides that, this study also focuses on variability of QoS values which are obtained by monitoring of web services at different service invocation time. QoS values are variable and service consumers may experience various QoS values due to the fact that web services run in a distributed, dynamic, and unreliable environment which makes them exposed to faults and failures. In this research, a new Stability Coefficient is introduced to measure stability of a service based on historical QoS values that were obtained by monitoring the web service. Such a measure enables service consumers to find a stable and trustable service based on QoS attributes and it can increase consumer’s satisfaction. In this study, the Stability Coefficient is defined based on an average of different QoS attributes of service stability. The results confirm that the proposed Stability Coefficient is a proper criterion for determining stability of services in terms of their QoS attributes and a stable service with less QoS values variation has a high Stability Coefficient which may lead to more satisfaction to service consumer

Design and Analysis of an Optimized Scheduling Approach using Decision Making over IoT (TOPSI) for Relay based Routing Protocols

This research work focuses on support towards QoS approaches over IoT using computational models based on scheduling schemes to enable service oriented systems. IoT system supports on application of day-to-day physical tasks with virtual objects which inter-connect to create opportunities for integration of world into computer-based systems. The QoS scheduling model TOPSI implements a top-down decision making process over top to bottom interconnected layers using service supportive optimization algorithms based on demandable QoS requirements and applications. TOPSI adopts Markov Decision Process (MDP) at the three layers from transport layer to application layer which identifies the QoS supportive metrics for IoT and maximizes the service quality at network layer. The connection cost over multiple sessions is stochastic in nature as service is supportive based on decision making algorithms. TOPSI uses QoS attributes adopted in traditional QoS mechanisms based on transmission of sensor data and decision making based on sensing ability. TOPSI model defines and measures the QoS metrics of IoT network using adaptive monitoring module at transport layer for the defined service in use. TOPSI shows optimized throughput for variable load in use, sessions and observed delay. TOPSI works on route identification, route binding, update and deletion process based on the validation of adaptive QoS metrics, before the optimal route selection process between source and destination. This research work discusses on the survey and analyzes the performance of TOPSI and RBL schemes. The simulation test beds and scenario mapping are carried out using Cooja network simulator

Collaborative Based Filtering Approach for Web Service Recommendations using GEO-Locations

Service computing is one of Internet-based computing, whereas the shared configurable resources (e.g., infrastructure, platform, and software) are provided to computers and other devices are as services. Strongly promoted by the leading industrial companies like, Amazon, Google, Microsoft, IBM, etc, In recent years, service computing are quickly becoming popular. Applications are deployed in real time environment are typically large scale and complex. The rising popularity of service computing, it is how to build high-quality service applications it becomes an urgently required research problem. In Similar, the traditional component-based systems, cloud applications are typically involves multiple cloud components communicating with each other over application programming interfaces, through web services. On-functional performance of cloud services are usually described by the quality-of-service (QoS). QoS is an important research topic in cloud computing. When the creation optimal cloud service selection from a set of functionally corresponding services, QoS values are of cloud services provided the valuable information to assist decision making. The component-based systems, software components are invoked locally in tradition, while in cloud applications, the cloud services are invoked remotely by Internet connections. To evade the slow and expensive real-world service invocation QoS ranking prediction framework is used. This framework requires no extra invocations of cloud services when making QoS ranking prediction can implement novel collaborative filtering approach to recommend the web services with improved performance. DOI: 10.17762/ijritcc2321-8169.15033

Analisis Qos (Quality of Service) Pada Jaringan Internet (Studi Kasus : Upt Loka Uji Teknik Penambangan Jampang Kulon – Lipi)

– Technical Implementation Unit for Mine's Technology Assessment Jampang Kulon - LIPI, organizationally under and responsible to the Head of Geotechnology Research Center, Deputy of Earth Sciences, Indonesian Institute of Sciences. To support research and development activities, administration and cooperation is in need of Internet-based information systems. Quality of Service (QoS) is defined as a measure of how well the network and is an attempt to define the characteristics and nature of the service. In an Internet Protocol (IP), IP QoS refers to the performance of the -Package IP packets passing through one or more networks. QoS is designed to help end users become more productive by ensuring that end users get reliable performance of network-based applications. QoS refers to the ability of a network to provide better service at a specific network traffic through different technologies. Computer network performance can vary due to several problems, such as problems of bandwidth, latency and jitter, which can make the effect is large enough for many applications. Features Quality of Service (QoS) can make the bandwidth, latency and jitter are predictable and matched to the needs of applications that are used in the existing network. Keywords – Quality of Service, Internet Protocol, Bandwith, latency, jitte