Dynamic Proxy Scaling for Cloud Data Protection
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Solution Overview
Problem
Current data protection systems in virtualized computing environments face challenges such as high costs and processing overhead due to the need for maintaining idle virtual machines, and there are delays between data protection operations, leading to inefficient resource allocation.
Innovation Solution
The system dynamically adds and removes proxy data protection agents in a cloud data storage system to process data protection jobs on an on-demand basis, efficiently scaling virtual machines to minimize costs and resource usage by powering down decommissioned agents and machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system maintains idle virtual machines for data protection operations, then data protection capability is preserved, but computing resources and costs are wasted during idle periods
Solution Approach 1:
The system dynamically provisions and deprovisions virtual machines based on real-time workload demands. Virtual machines are activated when data protection operations are needed and deactivated when idle, transforming the static resource allocation into a dynamic system that adapts to changing requirements, thereby eliminating waste during idle periods while maintaining capability when needed
Solution Approach 2:
The system changes the operational state parameter of virtual machines between active and deactivated states based on workload conditions. This parameter change allows the system to maintain data protection capability when required while minimizing resource consumption during idle periods, directly addressing the contradiction between reliability and energy loss
2Reliability
If the system deploys numerous virtual machines for data protection, then data protection coverage is improved, but processing overhead and costs increase significantly
Solution Approach 1:
The system uses dynamic provisioning to deploy virtual machines only when data protection operations are required, rather than maintaining a large static fleet. This dynamic approach improves data protection coverage during operations while reducing processing overhead and costs by eliminating the need to maintain numerous idle virtual machines
Solution Approach 2:
The system creates virtual machines on-demand that can serve multiple data protection purposes, making each deployed virtual machine multi-functional. This universality allows the system to achieve comprehensive data protection coverage without deploying numerous specialized virtual machines, thereby reducing overall complexity and costs
3Loss of energy
If the system powers down virtual machines between operations, then costs and resource usage are reduced, but delays occur between data protection operations
Solution Approach 1:
The system performs preliminary provisioning actions by pre-configuring virtual machine templates and maintaining a ready-to-deploy infrastructure. When data protection operations are needed, pre-configured virtual machines can be rapidly instantiated, minimizing delays while still allowing power down between operations to reduce costs and resource usage
Data Source
AI summary
Systems described herein may dynamically add one or more proxy data protection agents to a cloud data storage system to process a data protection job. Upon completion of the job or at some other appropriate interval, the system can power down and decommission the proxy data protection agents and/or the virtual machines on which the data protection proxies reside according to a cleanup schedule (e.g., at hourly or minute intervals). In order to improve the allocation of computing resources, the system takes into account currently existing proxies or virtual machines when processing a backup request to determine the need for new proxies to service the backup request. In this manner the system can save costs and computing resources through efficient virtual machine deployment and retirement.