3.2 The TLS Framework
3.2.4 Traffic Prediction Methodology
As mentioned in section 3.2.1, the data to be transmitted for each flow is estimated within the Traffic Prediction Module (TPM)at the super-frame level. This mod- ule estimates the data for the differentVBRflows by exploiting their slowly decay- ing auto-correlation properties and thus circumvents bandwidth over-provisioning problems effected by conservative allocation [72]. Although, a plethora of different VBR traffic prediction algorithms [73, 74] are available, a light weight algorithm must be used here as TLS employs the estimation mechanism on-line at each super-frame boundary. The implementation of exact TPM will vary depending on the nature of flows being predicted. In addition, any such strategy must grad- ually learn and tune specific parameters over time to allow accurate prediction for a given type of flow. Thus, within the traffic prediction module, a separate instance if the traffic estimator used, must run for each flow, as shown in Fig- ure 3.1. This mechanism allows a given estimator instance to customize itself with respect to its corresponding flow and thereby deliver better prediction accuracy over time.
This work uses Adaptive Trend and Seasonality Adjusted Exponential Smooth-
3.2 The TLS Framework
ing (ATSAES) [75], a simple and reasonably accurate time-series forecasting tech- nique with prompt responsiveness to process changes and only a constant time computational overhead. Although, the fundamental methodology remains same, ATSAES must be adapted according to the type of the traffic flow being esti- mated. Trace based H.264-encoded video traffic has been used as a representation of RT VBR flows during the experimental analysis of TLS. At each super-frame boundary, the Traffic Prediction Module (TPM) estimates the total amount of data to be transmitted by the flows. In this section, we present an overview of the steps followd by TPM for H.264-encoded videos.
In video coding, a group of pictures (or GOP structure), specifies the order in which intra (I)- and inter (P and B)-frames are encoded. I, P, and B-frames of H.264-encoded video sequences are encoded with different degrees of compres- sion and possess varying statistical characteristics which result in short-term bit rate variations. Figure 3.4 depicts the three levels of distinct video frame sizes for I-frames (approx 7000 to 8000 bytes), P-frames (approx 1000 to 2000 bytes) and B-frames (approx 0 to 100 bytes) which rehashes itself in a cycle with a fixed frequency. For example, the video frame sequence represented in Figure 3.4 follows a GOP(16,2) structure, and hence I-frames with similar frame sizes are observed every 16 frames with alternating P and B-frames in between. To effec- tively estimate in the prediction process, the data contained in this three level GOP structure, TPM uses an independent seasonality index SI, SP and SB for the I, P, and B-frames respectively, which normalizes the three distinct levels video traffic to the same level (deseasonalized level). The deseasonalized video frames (all of whose I, P and B-frames are at the same level) is then used to evaluate the current trend of the data content in the video traffic. The predic- tion module then predicts the amount of data in future video frames by using the obtained trend and the deseasonalized data content in previous video frames.
Moreover,TPM attempts to detect structural changes in the underlying sequence (e.g., scene change) and updates the components of the prediction module (level, trend, seasonality index) accordingly in each super-frame. The ATSAES based
prediction mechanism progresses in three steps:
• Initialization: At this step, initial estimates of level (L0), trend (T0), and seasonality factors (SI, SP, and SB) for I, P and B-frames are computed.
The prediction module develops initial components, namely, level, trend and seasonality index for the video traffic with the help of the firstK GOPs of the video flow. This is carried out only once when the flow starts.
• Forecasting: This step is executed at the boundary of each super-frameSFs, to estimate the amount of data to transmit (Ds0i) for each RTflowfi in the next super-frame duration. For this, first the total number of predicted video frames (p) within a super-frame is computed as:
p= |SFs|
SPi (3.6)
where,|SFs|is the duration of thesth super-frame andSPi is the sampling period for theith RTflow. For example, the value ofpis 3 for a super-frame duration of 120 ms and sampling period is 40 ms (video traffic transmitted at 25 frames/secs). For an arbitrary video frame (say, the tth video frame index), the size of the (t+q)th (where,q= 1,2, ..p) video frame is predicted using the following equation:
Lt+q =Lt+q×Tt, (3.7a)
Ft+q =Lt+q×(SI or SP or SB)t+q (3.7b) where,Lt+q is the deseasonalized level andFt+qis the predicted video frame size for the (t+q)th frame index.
• Estimate Updation: After observing the actual video frame size for the (t + 1)th frame index (Dt+1) during the sth super-frame, the third step modifies the estimation of levelLt+1, trendTt+1, and seasonality factorsSI,
3.2 The TLS Framework
0 0.05 0.1 0.15 0.2 0.25 0.3
10 20 30 40 50 60 70 80 90 100
PLR
RT Flows
TLS: Traffic prediction module using Avg bit rate (without ATSAES) TLS: Traffic prediction module using ATSAES
Figure 3.5: RT VBR Packet Loss Rate Vs # RT flows (With and without ATSAES based prediction)
SP, andSB at the start of the (s+ 1)th super-frame as given below:
Lt+1 =a×
Dt+1 St+1
+ (1−a)×(Lt+Tt) (3.8a) Tt+1 =b× Lt+1−Lt
+ (1−b)×Tt (3.8b) St+i+1 =c×
Dt+1
Lt+1
+ (1−c)×St+1 (3.8c) Where, a, b,andcare the smoothing constants for level, trend and season- ality factors respectively, and Dt+1 is the actual data for the (t+ 1)th video frame index.
Figure 3.4 presents a comparison between the obtainedATSAES estimated video frame size values with respect to their actual values for the nbc H.264 AVC trace (QP =28) [1]. It may be observed from this figure that our generated estimated values are reasonably accurate in almost all cases. In Figure 3.5, we show plots to compare the packet loss rates of TLS with and without ATSAES based estimation. As expected, the results are always better withATSAES based prediction as it reduces bandwidth over provisioning problems.