Workload Distribution to Failover Sites Based on Local Properties
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Solution Overview
Problem
Current disaster recovery mechanisms in distributed computing environments fail to consider the overall risk profile of geographical and physical properties of primary and secondary sites, leading to potential failure during events like earthquakes, where both sites may be affected due to similar geographical risks.
Innovation Solution
A method that uses metadata to associate workloads with primary sites and identifies secondary sites based on geographical characteristics, prioritizing sites with lower risk profiles to ensure resilient failover operations, leveraging geotagging, machine learning, and cognitive analysis to optimize workload distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If secondary sites are selected based on traditional disaster recovery mechanisms, then failover capability is established, but the sites may share similar geographical risks leading to simultaneous failure
Solution Approach 1:
The patent applies local quality by evaluating and selecting secondary sites based on their specific geographical characteristics and risk profiles rather than using uniform selection criteria. The system analyzes factors such as proximity to primary sites, geographical hazards, and regional characteristics to identify sites with differentiated risk profiles, ensuring that backup sites do not share the same vulnerability patterns as primary sites.
Solution Approach 2:
The patent changes the parameters used for site selection from traditional failover criteria to include geographical risk characteristics. The system modifies the selection process by incorporating analysis of geographical hazards, environmental factors, and risk distribution patterns, thereby transforming the approach to site provisioning based on risk-aware parameters rather than conventional availability metrics.
2Reliability
If workloads are distributed to sites with lower risk profiles, then backup protection reliability is improved, but site selection complexity increases
Solution Approach 1:
The system implements self-service by automatically analyzing geographical characteristics and risk profiles of candidate sites without requiring manual intervention. The platform autonomously evaluates multiple factors including proximity, geographical hazards, and risk distribution, then autonomously selects optimal secondary sites based on predefined criteria, reducing the operational burden on administrators while maintaining high reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors and updates risk assessments of geographical locations. The selection process uses feedback from risk analysis, site performance data, and changing geographical conditions to dynamically adjust and optimize site selections, ensuring that the most reliable backup sites are chosen based on current risk profiles.
Data Source
AI summary
Provisioning workloads in a distributed computing environment includes receiving a workload by one or more processors maintained at a primary site located at a first geographical location, which is associated with first geographical characteristics. The workload is associated, based on the first geographical characteristics, with the primary site and the first geographical location using metadata of the workload. A secondary site for the workload, located at a second geographical location having second geographical characteristics, is identified based on the second geographical characteristics satisfying predefined constraints of the workload. The secondary site is established as a backup site to provision the workload to responsive to a failover event occurring.