Deduplication Data Store Provisioning via Retention Multipliers
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Solution Overview
Problem
Existing data deduplication systems face inefficiencies in resource allocation, leading to either insufficient compute resources for effective data processing or excessive resource wastage, resulting in suboptimal performance and increased costs.
Innovation Solution
A storage management system that determines and provisions appropriate compute resources for deduplication data stores based on inputs such as daily data amount, maximum local and remote retention periods, using stored multipliers and constants to calculate necessary resources like memory, processors, and I/O performance, ensuring efficient resource allocation without excess.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute resources are increased for deduplication data stores, then data processing performance is improved, but resource cost increases
Solution Approach 1:
The system dynamically adjusts compute resource allocation parameters based on workload characteristics, data growth rates, and performance requirements. By changing allocation parameters rather than fixed resource quantities, the system optimizes the balance between processing performance and resource costs for deduplication operations
Solution Approach 2:
The resource allocation system transitions from static to dynamic provisioning, continuously monitoring deduplication data store performance and adjusting compute resource allocation in real-time. This dynamic approach allows the system to scale resources up during high-demand periods and scale down during low-utilization periods, optimizing both performance and cost
2Reliability
If compute resources are provisioned generously, then performance requirements are met, but resource wastage occurs
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor deduplication data store performance metrics and compare them against required performance thresholds. Based on this feedback, the system adjusts compute resource allocation to maintain performance requirements while eliminating excess resource provisioning that would lead to wastage
Solution Approach 2:
Rather than provisioning full excess resources to guarantee performance, the system uses partial action by allocating just enough compute resources to meet performance requirements under normal conditions, with the capability to temporarily scale up when performance thresholds are approached or exceeded
Data Source
AI summary
Example implementations relate to provisioning a deduplication data store. An example includes receiving inputs indicating an expected data amount and at least one retention period for the deduplication data store; performing a comparison of the at least one retention period to a stored data structure; determining at least one storage multiplier based on the comparison of the at least one retention period to the stored data structure; and determining an amount of storage to provision for the deduplication data store based on the expected data amount and the at least one storage multiplier.