Magnifi is the flagship enterprise solution offered by VideoVerse. Magnifi endeavours to connect fans with real-time, relevant, customized video content and make content discoverability easier. To this end our proprietary AI models extract highlights and key moments from enterprise video content to auto-produce social ready clips. The technology renders itself seamlessly to sports, news and entertainment videos. MultiStream Technologies pvt. Ltd. is currently hosting three web portals for its Hospital Management Software on the AWS Cloud but wants to upgrade and redesign the architecture following AWS best practices and is looking for a certified and competent AWS MSP for the same.
The MultiStream Technologies was looking to host and migrate their live streaming solution from Azure to AWS. The Customer problems and pain points they had where:
• MultiStream Technologies Pvt. Ltd. encountered obstacles with their live streaming solution that was hosted on Azure cloud. The infrastructure couldn’t handle the rising demand, leading to low uptime and availability of GPU-based instances, negatively impacting the company’s growth. To overcome this issue, the customer was searching for a cloud platform that is more durable and scalable, capable of supporting their expanding customer base and facilitating their desired business growth. They desired a cloud platform that could utilize features such as auto-scaling with custom metrics, auto-healing, and load balancing to ensure that the infrastructure could manage the increased demand without any performance issues.
• Several users contribute their stream or camera input to generate video footage, which can be captured as is or transformed into a media live channel. This footage is then subjected to motion and sports data analytics, based on particular criteria, using a separate server. The resulting clips are then processed using Machine Learning/Reinforcement Learning. These clips are sourced from online media platforms such as Sony and ESPN. However, the Multistream team faced challenges in providing these services in a scalable manner due to limited integration capabilities of media services with their current vendor.
• MultiStream Technologies is seeking a managed services provider who specializes in the management of applications and infrastructure, allowing their developer and product teams to concentrate on product development and innovation.
• Dividing incoming backend application traffic evenly among separate servers per process and appropriately scaling the instances was one of the most difficult technical requirements.
• Workmates planned to provide the client with comprehensive assistance so that they could achieve both their commercial and technical goals. The Workmates Team engaged in several conversations with the technical and management teams of MultiStream to learn more about their current application infrastructures and the problems they are experiencing. Workmates wanted to provide feedback so that it would be clear what they expected from the Workmates Team and the Amazon Cloud infrastructure.
• To better understand the business and the current capability gaps, a migration readiness assessment was performed. It was performed to understand businesses, people, technology, IT process are ready to adopt the cloud journey. The AWS MRA Tool was used on the gathered inputs to generate relevant reports like the Heatmap and Radar which helped in understanding the existing strengths and weaknesses of the existing infrastructure for the forward journey to the AWS Cloud. The MRA Report helped in designing a solution plan for a smooth journey to the AWS Cloud by building upon the current weaknesses.
• The dependencies for the existing application infrastructure were determined and the applications were migrated to the AWS Cloud in various phases using the Cloud Endure tool following AWS best practices.
• CloudWorkmates took an initial approach to deploy the Landing Zone setup in Mumbai region.
• Coming to the problem statement, the in-house developers and application architects will deploy their python ML model to extract specific clips from the live sports stream and scaling and balancing incoming traffic across the distinct application server the following approach was applied:
• The request coming to application running on server was stored in Google Firebase collections, which triggers functions.
• The application code was modified to send instance id serving the request along with request id to the firebase collection. Firebase trigger has been set on the collection.
• The firebase trigger is responsible for making a Firebase function call which calculates average load per EC2 server. If threshold has been reached, then trigger a Lambda function with Node execution runtime.
• The lambda function makes use of AWS SDK to make an API call to scale up the desired capacity of ASG. Same applies for scaling down.
• CloudWorkmates ensured valid uptimes for all the required systems by deploying relevant managed services such as Auto Scaling.
• The application migration and deployment flow were observed by a separate migration approach.
1. Using IAM CloudWorkmates restricted users and group to access specific AWS resources only as per the requirement.
2. AWS Multi-Factor Authentication for AWS accounts, including options for hardware/Software based authenticators was enabled.
3. Quarterly Patch Management and Patch Automations is carried out using AWS SSM. During patch all the security patches, OS critical patches will be applied.
4. Deep visibility into API calls through AWS Cloud Trail, including who, what, and from where calls are made. All user related activities are tracked and logged.
5. All the SSH ports will be bound with OpenVPN server, also default ports are changed to the custom port.
6. DB is accessible only through the Application containers and through the VPN. All servers are hosted on the private subnet.
7. For Configuration Management and Policy as a Code, AWS Config was used, which helped us detect any configurations drifting within the AWS Account.
8. All the Data on Rest are encrypted using AWS KMS. EBS volumes of EC2, S3 Buckets and RDS are encrypted.
9. Trusted Advisor Checks will be carried out every week and ensure the all the security checks are used.
10. AWS Guard Duty for threat detection and identifying malicious activities in the account is enabled.
11. AWS Secrets Manager are being used to store the DB credentials encrypted using KMS.
12. AWS WAF has been implemented to help protect web applications and APIs against common web exploits and bots that may affect availability, compromise security, or consume excessive resources.
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