Skip Directly to [Instruction on How to Run the Video Processing Prototype]
A Developer Walks through Cloud
1. Little Phone, Big Cloud
A few months ago, a phrase caught my attention: “Instagram for Video”. It was an interesting idea for a mobile application. As a software designer, I dug into the idea, soon to realize one major implementation challenge.
It turns out that video is a collection of pictures–many, many pictures. Given the standard 24 frames-per-second rate, even an one-minute-long video would be comprised of 1440 pictures, which meant image-processing of 1440 pictures on a mobile phone. That is a lot of pictures for a small battery in your mobile phone to handle.
There is an alternative way to the scenario; let’s consider moving the image-processing task over to a remote machine that is bigger, stronger, and meaner. In this scenario, the mobile phone could upload the video to a server via the internet, process it remotely, and retrieve the processed video back in a seamless fashion.
However, there is one absolutely-crucial requirement in this scenario; we are going to need a big, big, big machine–big enough to handle millions of requests once this killer application goes viral (go big or go home). There is only one answer to this type of demand: “the Cloud.”
Luckily, there is an open-source cloud available; Eucalyptus is an open-source Infrastructure-as-a-Service cloud platform whose APIs are compatible with the ones with Amazon’s EC2. This makes Eucalyptus an ideal in-house cloud application development platform. It guarantees that once my killer application runs on Eucalyptus, it will also run on EC2 with no modifications required, thus creating a truly portable cloud application with the world-wide deployability.
2. IaaS Cloud
For those who are not familiar with the IaaS clouds, let me to take you to a quick walkthrough to the cloud.
Eucalyptus and Amazon’s EC2 offer “Infrastructure-as-a-Service” cloud platforms. It means that a cloud-user can request, “Hey cloud, I need 5 machines with full network connectivity and access to the storage,” then within minutes, the user will have the complete system ready for use.
Take this concept little further; instead of requesting machines for generic purposes, the cloud-user could have specified which machines to serve as what purposes at the creation. For instance, using the example in this article, the cloud-user could have asked, “Hey cloud, I want one machine to work as a collector while the rest as image-processors, and have them process my cat video immediately!” Then, the cloud would have brought up a network of machines with the specific tasks assigned to each machine, and they would have worked on processing of the cat video right away. Once the processing was complete, the machines would have been self-terminated, leaving only the processed cat video behind.
3. App on the Cloud
Let’s go back to the video-processing application on the cloud. Here I will cover some major design considerations when developing applications on the cloud.
3.1. Parallelism and Elasticity
Designing an application on a distributed system requires a process to be broken down into small tasks. Then, one must identify the tasks that can bring parallelism into the process. In this video-processing application, the process can be broken down into 3 major steps: decoding the video into images, processing the images, and encoding the processed images back to a video. Given these breakdowns, the natural approach is to distribute the image-processing task over multiple machines and assign a single machine to perform the encoding and decoding.
One important characteristic of the cloud that you must realize at the core of the design is the elasticity of the cloud. The elasticity is what differentiates the cloud applications from the traditional distributed applications. Traditionally, in a distributed computing environment, the number of nodes N in the system is a static value that is unchangeable during a job. However, in the cloud environment, there is no bound in the number N, theoretically the number N is limitless. This means that at any given point during the job, the system should expect the number N to grow, or even shrink in some cases. For instance, in our video-processing application, we could initially start with 5 machines assigned to be image-processing nodes, however in the middle of the processing, we should be able to add 5 more nodes to boost the productivity. Taking such advantage of the elasticity must be considered at the design level of the application.
Following is the overview of the prototype of the video-processing application in the cloud.
For more detailed instructions, please go to the page [Instruction on How to Run the Video Processing Prototype]
The goal of the prototype is to demonstrate a cloud application that performs image-processing tasks in a distributed fashion. The application takes input of a video file, performs image-processing in parallel, and when it terminates, the processed video file is stored in a known storage location provided.
For the simplicity of the prototype, let’s assume that there is a machine that works as a file server that has an apache web-service running in the open, which is accessible from the cloud. In other words, any virtual instances(nodes) spawned on the cloud will have access to the files on the file server via download(wget). Given this setup, for instance, when we trigger the collector node, it can download the input video file from the file server to start the process.
For the prototype, we need to construct two types of nodes: collector node and image-process node. However, before I go into further details, I must explain what takes places when the cloud-user requests an instance from the IasS cloud.
When the cloud-user asks the cloud, “Hey cloud, I need one machine,” the user is required to specify the image of the machine. In other words, the cloud-user must request, “Hey cloud, I need one machine with the RHEL 6.1 image that I have prepared for this video-processing prototype.” Then, the cloud will bring up a virtual instance that is flashed with the specified RHEL 6.1 image. Since users can prepare and upload images of their choices to the cloud, the possibilities are limitless on what you want the instances to do or to become.
For this particular prototype, I prepared a single image that would be used by all nodes. I took a generic Ubuntu Karmic image as the base image and modified its ‘rc.local’ script, which is the default script that gets executed automatically when the image boots up. The modified ‘rc.local’ script is set to read a line from the ‘user-data’ field, which get passed to the instance from the cloud-user at the creation. This small modification allows me to control the rolls of the instances with having only one image. For example, I can request, “Hey cloud, I want one instance with my special Ubuntu image and have it run the script ‘collector.pl'”, then later, I can ask, “Hey cloud, I want another instance with the same image, but this one will run the script ‘processor.pl’.”
The requests in the example above would look like the below. Notice using the same image ID ’emi-9BD01749′, but different ‘user-data’ values (-d).
First request to bring up a collector:
euca-run-instances emi-9BD01749 -k mykey0 -n 1 -g group0 -t c1.medium -d “collector.pl”
Second request to bring up a processor:
euca-run-instances emi-9BD01749 -k mykey0 -n 1 -g group0 -t c1.medium -d “processor.pl”
In the prototype, the actual requests contain more information than just a script name. The first request looks like,
euca-run-instances emi-9BD01749 -k mykey0 -n 1 -g group0 -t c1.medium -d “collector.pl 192.168.7.77 [lovemycat.avi]”
This command translates to, after the instance boots up, it downloads the specified script ‘collector.pl’ from the file server at ‘192.168.7.77’ via wget and execute the script. The purpose of the script ‘collector.pl’ is to turn the instance into the collector node for the video-processing application. First, the script installs all the necessary softwares via apt-get commands in Ubuntu; it uses various open-source softwares for the encoding and decoding tasks. It also installs the NFS server to create a shared directory where the processing nodes can access. Second, it downloads the target video file ‘lovemycat.avi’ from the file server at ‘192.168.7.77’ (for the convenience of the prototype, the file server is designed to provide all the external file resources to the instances). Then, the collector node decodes the avi file into a collection of JPEG images. These image files are stored in the shared directory opened up by the NFS server. Now, the collector node waits for the image files to be processed by the processing nodes. The collector node’s job is to periodically scan the shared directory for the progress.
After the collector node enters the stage where it idles and scans, the next step is to start a group of the processing nodes by requesting,
euca-run-instances emi-9BD01749 -k mykey0 -n 3 -g group0 -t c1.medium -d “processor.pl 192.168.7.77 [10.219.1.2 neon.scm]”
As result, 3 instances will boot up, download the specified script ‘processor.pl’ from the file server at ‘192.168.7.77’, and convert themselves into the image-processing nodes. It installs the opens-source image-processing software GIMP and the NFS client. It performs NFS-mount to the shared directory of the collector node, whose IP is at ‘10.219.1.2’. Then, these processing nodes will start picking up image files from the shared directory and perform image-processing using GIMP according to the script ‘neon.scm’.
The syntax of the user-data for this image is:
-d “<script> <file_server_IP> [ <arguments_for_script> ]”.
Now, here is one crucial design decision that compliments the elasticity of the cloud. The work-unit for the image-processing is set to be 20 images at a time. This means that each node is only allowed to grab a chunk of 20 images at a time to perform image-processing. Under this policy, the processing nodes must frequently inquire the collector node for a small amount of work, instead of pre-determining the complete workload for each processing node prior to the beginning of the processing. This approach allows more processing nodes to be added to the system at any moment, thus taking full advantage of the elasticity.
When the processing nodes discover that there are no more images to be processed, they will be self-terminated, freeing up the computing resources for the cloud. When the collector node learns that all the images have been processed, it wakes up and encodes the images to a new video file. The final AVI file will be uploaded to the storage location belongs to the cloud-user. Eucalyptus and EC2 offer S3 storage units that allow such operation, however I will skip the details for later.
This prototype demonstrates how a complex operation, such as distributed video-processing, can be automated using the cloud. However, the automation is just a tip of the iceberg for the cloud. The raw power of the cloud comes from the ability to instantly replicate the application in a massive scale across the world. Such capability of the cloud contributes to the recent booming development in Software-as-a-Service (SaaS) solutions.
Extra. Links to Processed Videos
Using Invert Filter –
Using Edge Filter –
Using Motion Blur Filter –
Related. Links to Project Home Page –