cal argument for flexible QoS choices with empir- ical evidence from the INternet Demand EXperi- ment (INDEX). INDEX is a real-world market trial for quality-differentiated network services. It pro- vides Internet access over 128 kbps ISDN lines to a group of users from the Berkeley campus commu- nity (students, faculty, staff). Users select network services from a menu of QoS-price offerings and pay for their usage. They control their usage of net- work resources by means of a Java application run- ning on the user’s computer (Figure 1). The sub- jects can choose a servicequality instantaneously by clicking on a button and change the Quality of Service even during an active session. The appli- cation also provides usage feedback by displaying a summary of charges accumulated over the ses- sion, the day and the month. A detailed overview of the technology, experimental setup and design of INDEX can be found in (Rupp et al. 1998).
These challenges needs to develop efficient routing procedures, mechanisms for reducing power consumption and covering the battery life, appliances for efficient use of partial bandwidth and communication capacity, some new procedures for data security, and manufacturing slighter yet further potential mobile devices. On the whole there is a room for improvement in the QoS MANETs. This Journal Explains the Common Concepts in MANET and Quality of Service Networking. This could be improvised in future by using wide range of Algorithm in Genetic Concepts.
Abstract Online learning tools are widely used in engineering education. This includes traditional face-to-face, but also distance education. Since these tools rely on Internet connections, the performance of those connections (speed, latency) can impact on how learning tools are experienced by students. Quality of Service (QoS) describes technical performance parameters that reflect the quality of an Internet connection. Quality of Experience (QoE) on the other hand has been widely used to describe how users experience a particular service. In the context of this work, users are students undertaking learning tasks. While technical literature addresses QoE and educational literature discusses online learning, a gap exists describing the relationship of QoS and the quality of the learning experience. This work uses a mixed methods approach to address the research question: What dimensions of QoE of online learning can be affected by QoS? To answers this question, two groups of students were exposed to changing QoS conditions while they were undertaking an online learning activity using remote access technology. Both technical performance parameters, as well as, the impressions where recorded. Subsequently, a focus group was
configurations on a voice port and also on a dial peer, the dial-peer configuration takes precedence. Jitter is more likely to occur on low-speed links because even a single packet in the queue on a low-speed link can dramatically affect the amount of time a voice packet needs to wait in the queue before being transmitted. Jitter can be an even bigger problem if you do not have priority queuing, such as low latency queuing (LLQ), enabled or configured correctly on your WAN connections. For more information about LLQ, see the Cisco IOS Quality of Service Solutions Configuration Guide. For information about troubleshooting VoIP over Point-to-Point Protocol (PPP) links with QoS for low latency queueing, refer to VoIP over PPP Links with Quality of Service (LLQ / IP RTP Priority, LFI, cRTP), document ID 7111. Low-speed links also require special considerations when data traffic is also present to ensure that a large data packet does not cause excessive jitter. Generally on WAN links that are 768 kbps or slower, you should use some form of fragmentation and interleaving to ensure that large data packets do not starve smaller voice packets. Even with LLQ enabled, the voice packet must wait if it arrives when a data packet is in the process of being transmitted.
There can be many problems in managing the performance and capacity of large-scale sys- tems consisting of thousands of nodes. However, there is a general agreement among IT profession- als that, given adequate tools and skilled people, problems in the measuring and reporting of per- formance metrics can be overcome. Unfortunately, this is not the case for the prediction of enterprise quality of service (QoS) and service levels. One of the reasons for this is that there are no established definitions, vocabulary, or standards in these fields. In their absence, it is usually assumed that: • QoS is often represented by the end-to-end
Policy-based management can guide the behavior of a network or distributed system through high-level declarative directives that are dynamically introduced, checked for consistency, refined, and evaluated, resulting typically in a series of low-level actions. We actually view policies as a means of extending the functionality of man- agement systems dynamically, in conjunction with preexisting hard-wired manage- ment logic. In this article we first discuss the policy management aspects of architecture for managing quality of service in IP DiffServ networks as presented in , and focus on the functionality of the dimensioning and resource management aspects. We then present an analysis of the policies that can influence the dimensioning behavior as well as the inconsistencies that may be caused by the introduction of such policies. Finally, we describe the design and implementation of the generic Poli- cy Consumer component and present the current implementation status.
Abstract NOVELTY - The method involves selecting a route satisfying several Quality of Service (QoS) conditions, out of several routes connecting a start point to an end point via at least one node. DETAILED DESCRIPTION - The method sets conditions to be satisfied for several QoSs, respectively and inputs an error range permissible for a cost of an unknown optimum route having a minimum cost among routes satisfying all QoS conditions. A cost searching range containing at least the minimum cost is set and a determination is made whether the cost searching range has been narrowed to a searching possible range, the range is a function of the error range. In response to judgement that the cost searching range has not been narrowed to the searching possible range, several QoSs of routes leading from the start point to respective nodes at each cost are derived, in order of cost, beginning with a lowest cost within a current cost searching range, on the basis of QoSs of nodes having QoSs already derived. The cost searching range is narrowed when a route leading from the start point to the end point and satisfying all of the QoS conditions is found, on the basis of it cost. The cost sea rching range narrowed to the searching possible range an optimum route is searched.
The notion of Quality-of-Service (QoS) has been introduced to capture the qualitatively and/or quantitatively defined performance contract between user applications and the service provider. It was firstly defined by the International Telecommunication Union (ITU) as the set of requirements on all the aspects of service aiming at the degree of satisfaction of a user of the service . Since then, a lot of work has been done in the area of QoS in both wired and wireless networks. Special attention has been given to the QoS in wireless networks since they are more resource constrained than wired networks (e.g., in IEEE 802.11 networks , mobile ad hoc networks , ad hoc wireless networks , and mobile networks ). Recently, different communication networks converged into one large heterogeneous network that is used for communication in Machine-to-Machine (M2M) systems, opening new research challenges.
Usage Guidelines Use this command to specify the name of the class for which you want to create or modify class map match criteria. Use of the class-map command enables class-map configuration mode in which you can enter one of the match commands to configure the match criteria for this class. Packets arriving at either the input or output interface (determined by how the service-policy command is configured) are checked against the match criteria configured for a class map to determine if the packet belongs to that class. When configuring a class map, you can use one or more match commands to specify match criteria. For example, you can use the match access-group command, the match protocol command, or the match input-interface command. The match commands vary according to the Cisco IOS release. For more information about match criteria and match commands, refer to the “Modular Quality of Service Command-Line Interface (CLI)” chapter of the Cisco IOS Quality of Service Solutions Configuration Guide.
Before looking at the various protocols and mechanisms that may be used to provide quality of service to applications, we should try to understand what the needs of those applications are. To begin, we can divide applications into two types: real-time and non- real-time. The latter are sometimes called “traditional data” applications, since they have traditionally been the major applications found on data networks. They include most popular applications like Telnet, FTP, email, Web browsing, and so on. All of these applications can work without guarantees of timely delivery of data. Another term for this non-real-time class of applications is elastic, since they are able to stretch gracefully in the face of increased delay. Note that these applications can benefit from shorter-length delays, but they do not become unusable as delays increase. Also note that their delay requirements vary from the interactive applications like Telnet to more asynchronous ones like email, with interactive bulk transfers like FTP in the middle.
Modern problem solving environments (PSE) are envisioned as collections of cooperating programs, tools, clients, and intelligent agents [Gal94]. These components are integrated into an environment that facilitates user interaction (such as problem statement and solution engineering) and cooperative execution of the components charged with the solution tasks. An example is a system that would help an environmental scientist or a regulator to pose environmental engineering questions (problems), develop, execute and validate solutions, analyze results, and arrive at a decision (e.g., cost-effective emission control strategy). Such a PSE would consist of a management, analysis and computational framework that would be populated with a variety of models and data that describe the science behind the phenomena, the solutions of interest and the decision rules [e.g., Den96]. It is usually assumed that a modern PSE is distributed across a number of central processing units that may or may not reside in one physical computer. In fact, the advent of high-performance computing engines and networks, the potential of new technologies (such as the Asynchronous Transfer Mode) to "guarantee quality of service", and the ready access to network-based information through the World-Wide Web (WWW) is opening a fantastic opportunity for bringing serious numerical and problem-solving applications closer to a broad base of potential users.
As already discussed WSN are very different from adhoc networks so Diffserv and Interserv models cannot be used in WSN. To achieve QoS simple models should be used using cross layer approaches. Mobility is the main issue to be considered as most of the time it is assumed that sensor node and sink are stationary but there exist certain scenarios, for example, in military environment, the sensor nodes and the sink will be made mobile. So efficient techniques for QoS considering mobility should be developed. Also, the topology of the network may also keep on dynamically changing. Therefore, efficient routing protocols are required to address mobility and dynamicity of the wireless sensor network . In case of real time applications Reliability and timeliness is an important concern in providing QoS. Data redundancy may exploit reliability; if we use fusion techniques it will effect timeliness and introduces delay. So optimum techniques should be developed to overcome this issue. Multipath techniques should be developed to ensure delivery of data in timely and reliable fashion. Moreover different cross layer techniques should be developed and different QoS control mechanisms should be developed to achieve Quality of Service in WSN. Most importantly the major issue of WSN i.e. Energy consumption should be considered while developing new protocols and techniques.
iMAX the IEEE 802.16 standard for broadband wireless metropolitan area network (WMAN) is becoming popular mainly due to its open standard and support to quality of service (QoS) for different categories of services. Voice over IP, home entertainment video, triple play and the high evolution of Internet usage have created an excessive demand of broadband technologies such as E1/T1 and DSL. On the other hand, it is very expensive to create new infrastructures with either fiber optic or copper wires. IEEE 802.16 can offer a great advantage to SPs to provide low cost connections and extensive mobility.
Today end users just need an IP access connection, e.g. via a Wireless Local Area Network (WLAN) hotspot, a Digital Subscriber Line (DSL) connection or a GPRS / UMTS network to have access to these services. The success of Skype and other VoIP / Multimedia over IP providers in face of dropping IP connectivity prices is providing evidence for this view. With the progress of this IP-fication of networks, the competition on IP-based (telecommunication) services is growing, and we can witness a changing value chain in which connectivity charging decreases in favor for applications and content charging. This heterogeneous all IP telecommunications trends demands outstanding provision of Quality of Service (Figure 1), and motivates the provision of service experience to users, in order to fulfill their expectations motivating them to use it more and recommend it to friends.
Multilink protocols such as Multilink PPP (MLPPP) and Multilink Frame Relay (MLFR) increase the total bandwidth of a connection. Although the bundle of carrier lines acts as a single logical connection, each carrier line is physically separate, and you should remember this as you allocate the interface’s band- width. Carrier lines may go down and alter the bandwidth actually available. For example, an MLPPP connection with two T1 lines provides 3.0 Mbps of bandwidth. You can allocate up to 75 percent of this bandwidth, or 2.25 Mbps, to the interface’s classes. You could allocate 300 Kbps (10 percent) to Class 1, 600 Kbps (20 percent) to Class 2, 600 Kbps to Class 3, and 750 Kbps (25 percent) to Class 4. However, if one of the lines fails, the connection will only have 1.5 Mbps of bandwidth to provide the 2.25 guaranteed. If Class 3 and Class 4 are already consuming their full minimum bandwidth (1.35 Mbps), traffic from Class 2 will not be able to receive its guaranteed level of service.
While the vast majority of countries are regulating retail fixed and mobile QoS (residential and businesses), this is much less rare for wholesale QoS (see Table 5 and Table 6). This can be explained by the fact that reference wholesale offers generally include Service Levels Agreements which does not require an additional level of regulation. However, monitoring KPIs related to wholesale services can be relevant, especially to make sure the non- discrimination obligation, when imposed, is well applied. The initiative of Bahrain with respect to International Internet connectivity is also interesting to better understand this segment of the broadband markets.
complaint rate An account is a statement of money owed or paid that is read or otherwise accessed by a Subscriber; the services provided to the Subscriber may be prepaid or postpaid. An account complaint is a complaint that an account is inaccurate. This occurs when, for instance, incorrect call data are used, calls are charged at an incorrect rate, services are billed incorrectly, call discounts, credits or debits are handled incorrectly, or the total charge including tax is calculated incorrectly. An account complaint should not be confused with a request for information about accounts or tariffs, or with a service fault report. An account complaint may be submitted by phone, by personal contact at a customer service centre or in written form.
As an efficacious and efficient way to provide computing resources and accommodations to customers on demand, cloud computing has become more and more popular. From cloud accommodation providers’ perspective, profit is one of the most paramount considerations, and it is mainly determined by the configuration of a cloud accommodation platform under given market demand. However, a single long-term renting scheme is conventionally adopted to configure a cloud platform, which cannot guarantee the accommodation quality but leads to solemn resource waste. A double resource renting scheme is designed firstly in which short-term renting and long-term renting are coalesced aiming at the subsisting issues. This double renting scheme can efficaciously guarantee the quality of accommodation of all requests and reduce the resource waste greatly. Secondly, an accommodation system is considered as an M/M/m+D queuing model and the performance designators that affect the profit of our double renting scheme are analyzed, e.g., the average charge, the ratio of requests that need transitory servers, and so forth. Thirdly, a profit maximization quandary is formulated for the double renting scheme and the optimized configuration of a cloud platform is obtained by solving the profit maximization quandary.  Conclusively, a series of calculations are conducted to compare the profit of our proposed scheme with that of the single renting scheme. The results show that our scheme can not only guarantee the accommodation quality of all requests, but withal obtain more profit than the latter.
IBM (2010) has developed their IBM Cloud Adoption Framework to advise the best approaches and recommendations while developing services in different types of Clouds at the time of publication. They use diagrams to illustrate their concepts. However, there is a lack of real-life case studies to support their vision and points of views. This explains why a collaboration with independent researchers is helpful for Cloud Computing research. Chang and Li (2012) et al have started the first collaboration to demonstrate the first prototype of Financial Software as a Service (FSaaS) and illustrate FSaaS can be ported to different types of Clouds with its performance benchmark tested. More research outputs have been updated from Year 2012 onwards. Chang et al (2013 a) and Chang (2015) propose their Cloud Computing Business Model (CCBF) which has four major components and compiles a summary of successful deliveries and