Research in traffic classification has searched for methods to determine the composition of traffic, without recourse to examining the packet payload. For the purpose of traffic control, it is not necessary to classify individual packets as to their specific application; this would require detailed knowledge of all application layer protocols. The usual goal is more modest. Traffic flows are classified into a number of QoSclasses, where flows within the same QoS class share similar QoS requirements. The fundamental idea of traffic classification is to differentiate flows based on flow and packet statistics that can be easily observed without examining payloads. Routers and switches with NetFlow or sFlow capabilities can collect traffic flow statistics, such as flow duration, number of packets and bytes, and packet length statistics. Our aim is to make use of the collected flow statistics to determine the QOS requirements for each flow.
Wireless networks are generally less efficient and unpredictable compared to wired networks, which make quality of service (QoS) provisioning a bigger challenge for wireless communications. The evergreen demand for fast delivery of large volumes of data is one of the challenging task for wireless communication technology. WiMAX (Worldwide Interoperability for Microwave Access) is a wireless broadband solution that offers a rich set of features with a lot of flexibility in terms of deployment options and potential service offerings. Its main objective is to provide quality with cost effectiveness. But Delivering QoS is more challenging for mobile broadband than for fixed. The time variability and unpredictability of the channel become more acute, and complication arises from the need to hand over sessions from one cell to another as the user moves across their coverage boundaries. The wireless medium has limited bandwidth, higher packet error rate, and higher packet overheads that altogether limit the capacity of the network to offer guaranteed QoS.in this paper, we provide an overview of the cyclic prefixes (Conventional/ Turbo) and service classes that are the key functions in the MAC common part sub layer.
We now show how Quorum effectively isolates the re- sponse times of each QoS class by selectively queuing and potentially dropping requests that cannot meet the guarantees. To illustrate this case, we show the evolution of response times for both A and B QoSclasses in Fig- ure 7. We also specify the measured compute time during the run (the shaded area) in order to better observe which portion of the displayed response time corresponds to the time the requests spend in the cluster versus portion that is spent queued inside Quorum. In the figure, we can see that while the cluster is underloaded, all requests are re- leased as fast as possible from the engine and promptly computed by the cluster (seconds from 0 to 50). Queu- ing or dropping is not necessary during this time interval. As soon as the cluster becomes overloaded at second 50 the engine starts queuing the excess of incoming requests that cannot be immediately computed by the cluster in an attempt to absorb transient peaks of traffic and avoid unnecessary “early” drops. Moreover, class B is the only class that sees an increase in queuing times – it is the only class that has incoming demands exceeding the guaran- tees. At the same time, class A is isolated from such effects given that its input demands are still below the guarantees. We should note that the increase in response times for class A from seconds 30 to 80, is due uniquely to the underlying increase of compute times triggered by the Load Control module. Before the guarantees are ex- ceeded, Quorum is gradually introducing work in an ef- fort to drive up utilization, as shown in Section 4.1. As soon as both QoSclasses have input demands that exeed
The UMTS architecture is employed in OPNET (14.5) simulator by using different nodes from object palette shown in Figure 2 and 3 respectively. IP cloud was used among servers and core network to show network involvement. IP links connect IP cloud to both servers and core network (GGSN) node. These nodes were placed in such a fashion that architecture can be clearly understood. All these nodes were connected using different connection links from the object Palette. After architecture implementation, required attributes are set for each node. Applications are defined in the application definition node and were assigned to respective QoS of UMTS. The profiles were created in the profile configuration node to be used by the users. Trajectory for user equipment was defined that is based on simulation time. In OPNET modeler, a trajectory is either segment or vector based. Here the trajectory is segment-based. In order to analyze different results, node statistics were selected. A single scenario is completed in all aspects after that duplicated and attributes were set for both scenarios. The two scenarios include hard and soft handover. The simulations are compiled for different seed values and results are examined. A number of different statistics are simulated for both soft and hard handover and resulting graphs of this simulation are analyzed on the basis of QoSclasses that which handover provides better services for these QoSclasses. The results
difficult, if we always want to have high cache hit-rates, and to treat QoSclasses unequally (i.e. use the servers with lower response time for the best customers) at the same time. The problem is so interesting that we still have intensive research going on to improve the mapping scheme that will try to sat- isfy these contradicting goals in a way that is close to the op- timal solution. Some considerable improvements have been obtained since , and the ”only QoS based” assignment works already quite well. This can clearly be seen in Fig- ures 1 and 2, which illustrate performance of a cluster of four servers. Figure 2 demonstrates how QoS-LB works, i.e. as- signs unequal load to servers according to QoSclasses. As a reference, Figure 1 shows similar performance curves for a round-robin scheme. In QoS-LB, the server number 4 is assigned as the preferred server for the most important cus- tomers, and as the QoS class gets worse, the number of pre- ferred server gets smaller. When looking at the figures, you should note that curves start from an empty system, and the load is not stable in the first half of the simulation run (confi- dence problems get bigger when using heavy-tailed distribu- tions).
In Proceedings of Quality in Research Conference (QIR) 2007, Sari R. F., Gde D I, Mukhayaroh N, Laksmiati D , made a performance evaluation of Weighted Round Robin which showed that the WRR based scheduler Implementation in WiMAX has supported WiMAX QoS by suppressing packet loss and providing each QoSclasses throughput value as they should be.
This protocol finds least-cost, delay-constrained path for real time data based on node’s energy reserve, transmission energy, error rate and other communication parameters. Moreover, the throughput of non real-time traffic is maximized. This protocol ensures guaranteed bandwidth through the duration of connection while providing the use of most energy efficient path.The protocol consists of two steps. The first step consists of calculating candidate paths in ascending order of least costs using an extended version of Dijkstra’s algorithm without considering end-to-end delay . In second step, it is checked which path fulfills the end-to-end QoS constraints and the one that provides maximum throughput is selected. Simulation results have shown that the proposed protocol consistently performs well with respect to QoS and energy metrics.
Ardagna and Pernici  proposed a modeling approach to service selection problem, but trust is just one of those considered quality criteria. Although this approach is effective to select a service for a consumer, it did not focus on the detection of malicious consumers. Then if malicious consumers exist, it may not select appropriate services for consumers. In the best Service Selection proposed Exact and approximated algorithms for best service selection based on service requests made by the users or services which matches user QOS requirement. It leads to computational complexity problem. The Best service selection process can be solved by applying greedy approach. This algorithm seems to be much faster when compare to other process.
The modifiers public and private, which determine what other classes can access MyClass, are discussed later in this lesson. The lesson on interfaces and inheritance will explain how and why you would use the extends and implements keywords in a class declaration. For the moment you do not need to worry about these extra complications.
It is extremely important that these networks should be able to provide efficient quality of service (QoS) that can meet the vendor requirements. The time delay is the main concern for QoS of routing protocols demanding that real time data be transmitted within a definite time interval. QoS support is essential for supporting time critical traffic sessions. Here we have comparison of proactive and reactive and hybrid routing protocols based on significant QoS parameter like throughput, bandwidth, time
Mobile Ad hoc Network (MANET) is a self-governing group of mobile nodes forming a self- motivated network and communicating over wireless links. Owing to its individuality such as easy deployment and self-organizing capability, it has great potential in many civil, military, real-time and a multimedia application is growing as well. These requests have Quality of Service (QoS) requirements and Security like bandwidth, end-to-end delay, jitter, energy, availability, authentication, integrity, and confidentiality. Consequently, it becomes very necessary for MANETs to have an efficient routing and QoS mechanism to support these applications. The emphasis of this paper is on exploring existing correlations for security and QoS issues in MANEs, the current issues and future challenges that are involved in this exciting area of research are also included.
Relying on a distributed hash table overlay (DHT), Harmony offers multi-faceted reputation evaluation across multiple resources by indexing the resource information and the reputation of each type of resource to the same directory node. In this way, it enables nodes to simultaneously access the information and reputation of available individual resources. Zol is a top online trading platform in China similar to Amazon and eBay. Zol is chosen for market data analysis because neither Amazon nor eBay provides the historical rating record of each transaction. Zol provides the historical reputation record of each transaction, which enables to calculate the reputation for each type of a seller’s merchandise for the multi-faceted reputation study. We collected trace data including 1,562,548 transaction records from Zol covering the period from 9/20/2006 to 6/26/2010. In addition to the overall reputation values of sellers, Zol provides the ratings within [0,100] for five QoS attributes for each transaction: 1) price, 2) distance, 3) quality, 4) service, and 5) efficiency. Thus, if the resource a node possesses is limited, the highest-reputed nodes can easily become overloaded. Therefore, a seller’s individual reputation cannot reflect its QoS for
This section includes scenarios and results of the Interactive Best Effort traffic test. The interactive traffic is high sensitive to throughput and it is given the third highest priority in this project traffic scheduling scheme. The main two performance features is tested and analyzed for this traffic class are the throughput and delay, where the throughput results is the most important to this type of traffic. The two performance indicators are tested in the presence of the traffic scheduling mechanism described in this project. The test has been made at different load amount carried by network’s links. The loading traffics are from the different four classes, once at a time. A comparison is provided between the results once traffic scheduling is enabled as well as disabled. The comparison is made to proof benefits and/or limitations provided by the QoS scheduling mechanism to the interactive traffic as well as to study the effect of other traffic’s classes on class2 traffic.
Now a day’s user needs guaranteed quality of service . User’s demand and evaluation performance are not being satisfied with the traditional type of grid scheduler. Lot of research was carried out in this field and the result is QoS based task scheduling. It is a new issue in grid scheduling algorithm. It is a quite complicated QoS based grid scheduling which considers the QoS parameter of both the tasks and the resources before scheduling. CPU speed, memory, bandwidth are some of the QoS parameter in QoS based task scheduling. QoS aware Grid scheduler will allocate a task to the resource only if both task and resource QoS requirements are satisfied . QoS aware scheduler must consider the QoS requirements of the resources, QoS requirements of tasks, and minimize the makespan simultaneously.
Web services technology increasingly has been used to develop the new software systems’ era, by moving from module implementation to unit composition which is the base of the Service Oriented Architecture (SOA). Web service technology can reduce the time to market, as well as the Quality of Service (QoS) according to the Service Agreement Level (SAL) must be provided by the service providers that can gain the clients’ reputation and increase the market share. The new technologies era increased the functionality and the complexity of the software and systems in organizations, and as a result, a high system management costs and increased systems, sub system or component(s) failures. Accordingly, there is a growing interest in Self-Healing software as a solution to solve the problems of fault tolerance, reliability, security and availability of the systems. [1, 2, 3, 4]. Naccache, Gannod and Gary  stated that: “Self-healing systems must be able to recover from the failure of underlying components and services. The system must be able to detect and isolate the failed component, fix or replace the component, and finally reintroduce the repaired or replaced component without any apparent application disruption”. Keromytis  claimed that Self-healing software systems have emerged as a research era in the recent years, motivated by the capabilities of monitoring, diagnosis, and repair anomalies as an exciting and potential solution to the existing problems of inability of traditional technologies to guarantee the software availability, robustness, and reliability.
A component operation happens when a middleware or control component method is called; for example, when a control component starts the control action or when a middleware element is disconnected from a communications channel. Alarms are events associated with the compliance of the quality parameters. In our proposal we include the QoS and Quality of Control (QoC) parameters. QoC pa- rameters  are associated to the efficiency of the control action and are directly related with the QoS parameters . Thus, both parameters are considered. An ex- ample of QoS alarm is when a message arrives after the deadline. QoC alarm is re- lated to the content of the message, for example when the reference value exceeds the reference value. Finally, message filters events are triggered when the content of the message is (or not) identical compared to a content pattern; for example, when the message field “source” (user defined) corresponds to a particular control node.