See the "Menu Bar" section and the "Monitor Window" section for information about the menu bar and monitor window functions. The RTMT menu bar comprises several menu items that you can use to access different monitoring components. See the following sections for information about RTMT menu bar items:. The System menu provides access to applicationwide functionality, such as Profile and logout.
The Monitor menu provides access capability to precanned preconfigured items in the menu system. It includes the following categories:. Tip To monitor the precanned set of monitoring objects in RTMT, you can use either the menu bar at the top of the monitor window or the left controlling panel of the monitor window.
For more information about the left controlling panel, see the "Monitor Window" section. This menu provides device-based and CTI-based search functionality. The Device search menu comprises the following menu items on which you can search:.
Within these menu items, you can search on the basis of any device in the Cisco CallManager cluster and choose the status of the devices, including registered, unregistered, rejected, any status, and devices that are only configured in the database. You can also search by any model, or a specific device model and set up criteria that include several different attributes.
You can monitor chosen devices on the basis of the criteria that you specify for that device. You can search by the status of the device, device name, and application pattern. See Figure through Figure for examples of these monitoring criteria windows, where the device being searched is a phone.
See Figure for a search results example. You can also choose any or all the CTI Managers on which you want to search. You can specify criteria for the CTI applications, devices, and lines that include CTI status, device name, application pattern, and attributes. Note You can monitor each search result or bring up Cisco CallManager Administration in a separate browser by right-clicking on each item. Figure shows a right-click pop-up menu example. Use this menu to specify context-sensitive edit actions that are also available in right-click popup menus.
Note The "Polling Rate" item does not apply for precanned monitoring objects. Available trace settings in this menu include:. The Perfmon menu provides access to Perfmon functionality and includes the following menu items:. The Alert menu provides all alert-related functionality including alert defining, setting, and viewing.
This menu comprises the following menu options:. Once an alert has been raised, its color will automatically change to red in RTMT and will stay that way until you manually clear the alert. Two kinds of alerts occur: preconfigured and user-defined. You can further configure both, but the difference is that you cannot delete preconfigured alerts whereas you can add and delete user-defined alerts. Figure through Figure show examples of different alert menu categories and their associated windows.
You can use the Window menu to close opened monitoring windows by using two menu items: Close and Close All Windows. You can use the Close menu item to close the current active window in the right content viewing panel. Use the Close All Windows menu item to close all opened monitoring windows.
Figure shows a Window menu example. Figure shows an Applications menu example. The Help menu provides information about product name, client and server software versions, and third-party libraries software version information, as well as RTMT help pages. To support the precanned set of monitoring objects, the RTMT main user interface comprises two parts: left controlling center pane and right content viewing panel.
The controlling center comprises the View tab and the Alert tab. The View tab comprises several different monitoring categories, and the Alert tab comprises only the Alert category. The View and Alert tabs differ in that anything related to the View tab gets saved as a profile that can be restored at any time that RTMT is invoked.
The Alert tab relates only to the systemwide alert functionality. The states cannot be saved. See the "View Tab" section and the "Alert Tab" section for more information about each tab. The polling rate in each precanned monitoring window remains fixed, and the default value specifies 30 seconds. If the collecting rate of RISDC real-time information system directory changes in Cisco CallManager Administration service parameters web pages, the polling rate in the precanned window also updates.
In addition, the local time of the RTMT client application and not the backend server time, provides the basis for the time stamp in each chart. See the following sections for more information on each category:. The Summary page provides monitoring of some important common information on a central page.
Monitored information includes. Figure shows a summary monitoring page example. See Figure through Figure for examples of each monitored object. The Critical Services monitoring category provides the name of the critical service, the status whether the service is up, down, or activated , and the elapsed time during which the services have been in a particular status for a particular Cisco CallManager node.
Call processing monitored items include. Gateway activity monitoring includes the number of active ports, the number of ports in service, and the number of calls that were completed for each gateway type for a particular Cisco CallManager node or the entire cluster.
Trunk activity monitoring includes the number of calls in progress and the number of calls that were completed for a particular trunk type. The SDL queue types comprise high, normal, low, and lowest queue. You can monitor the SDL queue for a particular node or the entire cluster. See Figure through Figure for examples of call-processing activity monitoring. The Service monitoring category monitors the activities of the Cisco TFTP, the directory server, and the heartbeat information.
See the following sections for more information on the directory server and heartbeat:. Cisco TFTP builds configuration files and serves embedded component executables, ringer files, and device configuration files. Directory servers comprise database repositories that store user and device information such as user name, password, and location. Each node in the cluster has its own copy of the directory.
The directory supports three types of directory: embedded, active, and Netscape. An embedded directory resides on the same node as its associated Cisco CallManager other types reside on other nodes in the cluster. A directory that resides on the publisher node provides write permission.
You can view the directory servers connection status. Directory server connection status and replication status get checked when an alert is pending. If the server is not connected, an alert occurs. The directory server connection status gets polled every 10 minutes; replication status gets polled once every hour. The heartbeat acts as an indicator of the life of whatever it is monitoring.
When the heartbeat is lost, a blinking icon appears in the lower, right corner of the RTMT window. To find when the heartbeat loss was detected, click the blinking icon. An e-mail can notify you of the heartbeat loss. The Device monitoring category provides a summary of devices and device search capability. The device summary monitors registered phone, gateway, and media resource devices on a particular Cisco CallManager and an entire cluster.
The device search category allows you to search for every device in a Cisco CallManager cluster on the basis of the search criteria that you set up. Note You can also access the device summary information in Device Summary in the Monitor menu in the menu bar.
See "Monitor Menu" section for more information. You can also search for devices by using the Search menu in the menu bar. See the "Search Menu" section for more information.
See the "Monitor Menu" section for more information. In the hybrid QoE assessment, subjective tests only need be performed occasionally for updating the parameters of QoS-QoE mapping functions.
Hence, the cost of a hybrid QoE assessment scheme reduces significantly. Additionally, since streaming services over wireless networks have been becoming popular, the QoE enhancement algorithms, protocols, and evaluation methods for wireless streaming services were proposed [ 9 — 12 ]. In [ 13 ], Chen et al. Since the QoE evaluation for streaming services becomes more and more important, several QoE evaluation or monitoring tools were developed [ 15 — 19 ].
A QoE monitoring tool for multimedia quality assessment in NS-3 network simulator was presented in [ 15 ]. The paper [ 16 ] developed a testbed, which allows obtaining conclusive results regarding QoE in video-mediated group communication, for controlled experiments. Although the monitoring service can improve the user experience, the presented QoE factors are still not good enough.
In [ 18 ], the proposed concept, Monitoring of Audio Visual Quality by Key Performance Indicators MOAVI , is able to isolate and focus investigation, set up algorithms, increase the monitoring period, and guarantee better prediction of perceptual quality. In [ 19 ], Ickin et al. To improve the playout quality of video streaming services, Adaptive Media Playout AMP schemes [ 21 , 22 ] have been used.
In this work, an AMP playout system is implemented into the VLC media player, which is a free and open source cross-platform multimedia player. However, almost all the developed QoE monitoring tools in the literature [ 15 — 19 ] did not take the effects of AMP on QoE into account. Additionally, the most important feature of our proposed QoE monitoring system is that it can derive an overall QoE for several QoS metrics using the product form presented in [ 5 ].
Our previous work [ 5 ] has shown that the product form performs much better than the conventional averaging techniques in deriving an overall QoE of multiple QoS metrics.
Using the developed QoE monitoring program, network providers can identify network troubles and fix them in real time to improve the system performance and qualities. The novelties and contributions of this paper are summarized as follows.
Second, the developed QoE monitoring system derives an overall QoE from several QoS metrics such as the initial playout delay, packet loss rate, underflow time ratio, and normalized playout rate. Finally, the Group of Pictures- GOP- based cumulative average jitter , which is used to determine the playback threshold of the proposed AMP, is proposed to eliminate the effect of diverse frame sizes on the estimation of network jitter.
The rest of this paper is organized as follows. Section 2 describes the implementation of the AMP playout system. Implementation of the QoE monitoring system is then introduced in Section 3. Section 4 conducts several experiments and evaluates the QoE performance of video streaming services using the developed programs. Finally, the concluding remarks are given in Section 5. This section first describes the principles of AMP in Section 2.
Then the implementation schemes, available programming tools, and some challenging issues on measurements or estimations of key parameters are addressed in Section 2.
The AMP is implemented according to our proposed scheme [ 22 ] whose principle is described briefly as follows. In addition to the conventional slowdown threshold , a dynamic playback threshold and a speedup threshold are designed.
If the buffer fullness is below , the playout rate is reduced to avoid buffer underflow. When the buffer fullness exceeds the speedup threshold , the playout rate is increased to reduce the buffer fullness for avoiding buffer overflow. The Dynamic Playback Threshold Algorithm DPTA in [ 22 ] is designed to dynamically adjust the playback threshold , which affects the initial playout delay, under various network conditions. Whenever the current buffer fullness surpasses or equals , the playback starts immediately with the proper playout rate which is determined by the following equation: where is a random process and is estimated by the Arrival Process Tracking Algorithm APTA presented in [ 22 ].
That is, depends on the frame arrival process at the client buffer and is limited between and , as shown in Figure 1. Anyone interested in the detailed derivation of and can refer to [ 22 ]. The quadratic playout rate functions and in [ 22 ] are defined as follows: where is the uppermost threshold correlated to the buffer size at the client.
Notably, the playout rate must be limited such that rate variations are unnoticeable or acceptable by users. The perception of a slowdown video is usually different from that of a speedup video for users.
Therefore, two different restricted deviation ratios, denoted by and for and , respectively, are set for playout rates. The corresponding playout rate function of the proposed AMP is given by Figure 1. Numerical results in [ 22 ] have shown that the proposed AMP with quadratic playout rates performs much better than several conventional playout systems with linear playout rates and can adapt to different network load conditions for reducing the initial playout delay.
The input property collects all related parameters of the received video stream from the Network Interface Card NIC Access , the network demultiplexer Demux , and the video decoder Decoder , as shown in Figure 2. Several useful statistical parameters in input.
Based on these collected parameters from the input property, one can set and control the playout rate of the VLC media player to realize the AMP function. According to the descriptions in Section 2. Hence, the buffer fullness can be computed by the following equation: After deriving the buffer fullness , the playout rate of the AMP media player can be dynamically adjusted according to 1.
The command to dynamically set the playout rate of the VLC media player is defined by the function var. Although the currently developed program is designed for the MPEG-2 TS format, there is no difficulty to include other advanced video encoding technologies such as the H.
The dynamic playback threshold [ 22 ] is determined by where and are design parameters and is the average frame interarrival time.
However, video frames are categorized into I, B, and P frames and different types of frames usually have very different frame sizes.
For example, the frame size of an I frame is usually much larger than that of a B or P frame. Hence, the frame interarrival time between two successive I and B or P frames may become relatively large because of the long transmission time of an I frame, compared with that between two successive B and P frames.
Such variance of frame interarrival times maybe is not due to the network jitter but the diversity of frame sizes. To reduce the impact of frame size variation on the estimation of network jitter and estimate more accurately, in this work the average frame interarrival time within a GOP is used. Hence, the variable in 5 is replaced by , where , is the number of frames in a GOP, and is the interarrival time between the I frames of the -st and the th GOPs.
The procedures to implement the QoE monitoring system are described as follows. First, we propose an architecture for the QoE monitoring system. Next, several network-based QoS metrics are defined and their measurement methods are presented. Finally, the overall QoE evaluation method for the QoE monitoring system is introduced.
The architecture of our proposed QoE monitoring system is plotted in Figure 4. It includes the QoS measurement engine, QoE evaluation function, and performance reporting window. These obtained QoS metrics are used by the QoE evaluation function for producing the overall QoE of video streaming services.
In the proposed QoE monitoring system, the considered QoS metrics include underflow time ratio , packet loss rate , initial playout delay , and normalized playout rate. The measurements of these parameters are described as follows. At the same time, the number of underflow events is increased by 1. The underflow time ratio equals the ratio of accumulated underflow time to the total playback time duration.
The accumulated underflow time can be computed according to the value of Status provided by VLC Lua extension program, as shown in Figure 5. When the value of Status changes into 2, the underflow timer turns ON until the value of Status becomes 3.
Whenever the underflow timer turns OFF, the accumulated underflow time is updated. However, within each minute only the maximum and minimum normalized playout rates are used in our design [ 5 ].
That is, the corresponding QoE of during every minute equals the mean of these two individual QoEs resulting from the maximum and minimum normalized playout rates. Based on the results of [ 5 ], they are given as follows:. The scheme used in [ 5 ] consists of three steps to be done beforehand. First, several Key Performance Indicators KPIs or QoS metrics, such as the initial playout delay, packet loss rate, underflow time ratio, and playout rate, were selected.
Then several distorted videos were generated based on these selected QoS metrics. Second, subjective tests were conducted using human observers to rate the distorted videos in terms of Mean Opinion Score MOS [ 25 ]. The ACR subjective test method was adopted in [ 5 ].
Each distorted video clip was viewed and rated by no less than 30 viewers. Finally, the product form of individual QoS-QoE mapping functions [ 5 , 6 ] is used to derive the overall QoE of a video streaming service.
Notably, although only four QoS metrics are considered in 6 , the number of QoS metrics can be arbitrarily extended as needed. This is known as queuing. Once the data is tagged, and the policy is created, the main job of routers or switching devices is to automatically move these packets to the front of the queue and transmit them immediately without any delay. QoS transmits data from one node to another in a store and forward manner also known as hop-by-hop configuration.
RTP specializes in carrying and handling real-time transmissions of audiovisual data. Admins need to remember QoS can only function properly if the connected devices between the sender and receiver are configured to understand the priority of packets. The devices should be programmed to understand the priority of a VIP packet and know when to push them in the priority lane.
It collects, analyzes, and provides real-time visibility into your network bandwidth performance. With the NetFlow traffic analyzer, you can gauge traffic levels easily and evaluate if your QoS policies have the desired effect.
It offers an intuitive dashboard allowing admins to view vital information related to the status of the network by reviewing the network performance metrics. You can also create custom reports with a granular view of bandwidth consumption and detailed traffic analysis within a few clicks. ManageEngine NetFlow Analyzer offers a detailed traffic analysis using flow-based traffic analysis methods.
While the tool can be used for detailed network forensics, application monitoring, and even capacity planning, it also offers in-depth bandwidth and QoS monitoring capabilities. You can get quick insights into your bandwidth with usage tracking reports updated every minute.
With its enterprise edition, you can monitor distributed networks over a single console. Moreover, it also offers iOS and Android apps to monitor your enterprise traffic on the go.
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