Cloud Deployment Optimization via Daisy Chain Image Distribution
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Solution Overview
Problem
Cloud-based computing faces challenges in rapidly and reliably deploying applications and provisioning resources to meet increased demand, resulting in delayed response times and unreliable operational efficiency.
Innovation Solution
A cloud deployment system that uses a daisy chain copying technique to distribute optimized base images and application images across additional servers, generating memory snapshots, and performing optimizations such as garbage collection and memory compression to ensure efficient deployment and minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional deployment methods are used, then deployment process is simple, but deployment time is long and response to demand increase is delayed
Solution Approach 1:
The system performs preliminary actions by creating optimized base images and application images in advance, preparing deployment packages before they are needed. This allows the deployment process to start immediately when demand increases, rather than creating and optimizing images during the deployment process itself.
Solution Approach 2:
The deployment process is segmented into distinct phases: base image creation, application image creation, optimization, and distribution. Each phase can be executed independently and in parallel, significantly reducing total deployment time compared to sequential traditional methods.
2Speed
If resources are quickly provisioned to meet demand, then response time improves, but operational efficiency and reliability may be compromised
Solution Approach 1:
Optimization operations such as garbage collection and memory compression are performed in advance on the base images before distribution. This ensures that newly provisioned resources start in an optimized state, guaranteeing operational efficiency from the moment they begin serving requests, not after some settling period.
Solution Approach 2:
The system creates optimized copies of base images and application images that can be rapidly distributed and deployed. These pre-optimized copies ensure consistent, reliable performance across all new instances while enabling fast provisioning through efficient copying rather than building from scratch.
3Productivity
If application instances are increased to meet demand, then service capacity increases, but deployment complexity and resource distribution challenges increase
Solution Approach 1:
The system merges base image and application image creation into a unified deployment package. This consolidation simplifies the deployment process by reducing the number of separate operations and dependencies, making it easier to scale from a few to thousands of instances without proportionally increasing complexity.
Solution Approach 2:
The base image serves as an intermediary that contains all necessary optimizations and configurations. This intermediary layer simplifies distribution by allowing standardized, pre-configured images to be copied and deployed across multiple machines without repeating complex setup procedures for each instance.
Data Source
AI summary
Systems and methods of cloud deployment optimization are disclosed. In some example embodiments, a method comprises running original instances of an application concurrently on original servers to implement an online service, receiving, by the original instances of the application original requests for one or more functions of the online service, receiving a command to deploy a number of additional instances of the application, transmitting synthetic requests for the function(s) of the online service to one of the original servers according to a predetermined optimization criteria, deploying the number of additional instances of the application on additional servers using a copy of the original instance of the application, and running the deployed additional instances of the application on their corresponding additional servers concurrently with the original instances of the application being run on their corresponding original servers.