Hybrid Cloud Data Repatriation With Content-Aware Compression
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Solution Overview
Problem
Cloud service providers charge prohibitive egress fees for data download, and existing data compression methods are inefficient and generic, leading to high costs and computational burdens in data repatriation processes.
Innovation Solution
A framework utilizing a controller and mover modules to select the best compression algorithm based on data characteristics and SLA constraints, employing content and context-aware compression to minimize egress costs and computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed using generic algorithms before transmission, then data size is reduced, but compression efficiency is suboptimal and fails to meet SLA constraints
Solution Approach 1:
The patent applies different compression algorithms to different data sets based on their specific characteristics and SLA requirements. Each data set receives a tailored compression approach rather than a uniform generic algorithm, optimizing compression efficiency for each individual case while meeting specific service level agreements.
Solution Approach 2:
The system dynamically selects compression algorithms by evaluating data characteristics and SLA constraints as parameters. This parameter-based selection mechanism allows the system to adapt the compression approach to match the specific requirements of each data set, improving overall compression efficiency.
2Quantity of substance
If compression algorithms are run on cloud systems, then data compression is achieved, but additional computational costs are incurred
Solution Approach 1:
The patent enables on-premises systems to perform compression operations independently using locally available computational resources. This self-service approach eliminates the need to pay cloud egress fees for running compression algorithms, allowing organizations to compress data locally before transfer without incurring additional cloud computational costs.
3Reliability
If all data is repatriated to on-premises infrastructure, then data sovereignty and security are improved, but egress fees and computational overhead increase
Solution Approach 1:
The patent applies compression selectively to data sets that will be repatriated to on-premises infrastructure. By compressing only the necessary data before transfer, the system reduces egress fees and computational overhead while still achieving the goal of data sovereignty and security for repatriated data sets.
Data Source
AI summary
One example method includes selecting items to be repatriated from a cloud site to an on-premises site, and the items include a workload and a data set accessed by the workload, transmitting a repatriation request from the on-premises site to the cloud site, and the repatriation request identifies the selected items, receiving, by the on-premises site from the cloud site, a compressed data set which includes the data set in compressed form, receiving, by the on-premises site from the cloud site, a compressed workload which includes the workload in compressed form, and the compressed workload and the compressed data set have been compressed with a compression algorithm automatically selected based on content, and/or context, of data in the data set, decompressing, at the on-premises site, the compressed data set and the compressed workload, and deploying the decompressed data set and the decompressed workload locally at the on-premises site.