# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 5 Discussion  
A qualitative comparison between the related papers presented in Section 2 and our proposal is summarized in Table 6.  
There are some proposals that work with RAW videos and others work performing only transcoding tasks. Our proposal is based on the processing of RAW content, whose videos take up more disk space and the management of these files must be as efficient as possible to improve encoding times. Moreover, our proposal uses the latest stable codecs (H.265 and VP9), while most  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposal**} & \multicolumn{2}{c}{**Type of Encoding**} & \multirow{2}{*}{**Code Used**} & \multirow{2}{*}{**Chunk Size (Frames)**} & \multirow{2}{*}{**Fnalign**} & \multirow{2}{*}{**Video Resolution**} & \multirow{2}{*}{**Tested on.**} & \multirow{2}{*}{**Quality Analysis**} \\ \cline{1-1} \cline{6-7}  & & & & & & & & \\ \hline
[4] & RAW encoding & H.264 & 250 & Map-Reduce & 854 \(\times\) 480 (GL) &
\begin{tabular}{c} Public cloud \\ Amazon EC2 \\ \end{tabular} & No \\ \hline
[5] & RAW encoding & H.265 & \
\begin{tabular}{c} Variable, content-aware \\ video segmentation \\ \end{tabular} & Map-Reduce & 3840 \(\times\) 2160 (KL) &
\begin{tabular}{c} Private architecture \\ 1 master—20 workers \\ \end{tabular} & No \\ \hline
[4] & RAW encoding & VP8 & 24 and 96 & Serverless & 3840 \(\times\) 2160 (KL) &
\begin{tabular}{c} Public cloud \\ AWS Lambda \\ \end{tabular} & Yes \\ \hline
[7] & transcoding & H.264 & COP size & Map-Reduce & - & - & No \\ \hline
[8] & transcoding & H.264 & 32, 64, 128 & Map-Reduce & 320 \(\times\) 240 &
\begin{tabular}{c} Private architecture \\ 1 master—20 workers \\ \end{tabular} & No \\ \hline
[9] & transcoding & H.264 & 32, 64 and 128 MB & Map-Reduce & - & No \\ \hline
[20] & transcoding & H.264 & - & Map-Reduce & - & 4 nodes & No \\ \hline
[21] & transcoding & H.264 & - & Cluster of machines & - & Cluster of 2 & 2 to 20 machines & No \\ \hline Our proposal & RAW encoding & H.268 and VP9 & 25-50:100-200 & Message broker & 1902 \(\times\) 1000 (HEM) &
\begin{tabular}{c} Private cloud \\ 5 nodes—1 to 15 workers \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Qualitative comparison of the previous work with our proposal.  
works use the previous codecs such as H.264 and VP8. Regarding the paradigm used, most works use Map-Reduce or cluster of machines, while our proposal is based on the asynchronous message broker paradigm over a virtual infrastructure. Therefore, our proposal is well adapted to leverage the elasticity of computing resources offered by the cloud and has been tested on a private cloud infrastructure with several workers distributed in different nodes. We have performed a video quality analysis to verify that the encoded videos have a similar quality to the videos encoded using a sequential encoding system.  
Some approaches use hardware accelerators, as GPUs, to increase the performance of the encoder on a compute node. In these approaches, the GPU parallelizes specific time consuming parts of the video encoding process (mainly Motion Estimation and Intra-prediction) [9; 28; 29; 30]. However, the availability of nodes with GPU is not always possible, particularly on current heterogeneous cloud infrastructures, and the virtualization and remote use of GPUs on the cloud is another active area of research today [31]. Nevertheless, our solution could exploit the presence of GPUs in the VMs, and their combination could provide higher performance.