Dynamic Resource Migration for Data Center Latency
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Solution Overview
Problem
Large-scale data centers often operate in sub-optimal states due to geographical disparities between users and their resources, leading to increased latency and operational costs, as users are frequently remote from their hosted services, which diminishes user experience and increases costs.
Innovation Solution
A system comprising a front-end component to receive requests, distributed computation and storage components to analyze log information for resource migration decisions, and a location service component to redirect requests to geographically optimal data centers, considering factors like latency, capacity, and network quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resources are hosted in geographically disparate data centers, then service capacity and availability are improved, but user access latency and operational costs increase
Solution Approach 1:
The patent implements dynamic resource migration between data centers based on real-time user access patterns. The system continuously monitors request logs and automatically migrates resources to geographically optimal locations, making the hosting environment adaptable rather than static. This resolves the contradiction by allowing resources to move between dispersed locations dynamically, maintaining both high capacity and low latency.
Solution Approach 2:
The system performs preliminary analysis of user access patterns using log information before making migration decisions. By proactively identifying resources that would benefit from relocation based on historical data, the system can pre-position resources in optimal locations before performance degradation occurs, thereby reducing latency while maintaining service capacity.
2Reliability
If resources are hosted in geographically disparate data centers, then service availability is improved, but operational costs increase
Solution Approach 1:
The system dynamically consolidates resource hosting to minimize operational costs while maintaining availability. By analyzing access patterns and migrating resources to serve user bases more efficiently, the system reduces redundant data center operations and energy consumption associated with serving geographically mismatched users, thereby lowering operational costs while preserving service availability.
Solution Approach 2:
The system changes the operational parameters of data center utilization by migrating resources based on geographic and access pattern analysis. This optimization of hosting locations alters the energy consumption profile and operational cost structure while maintaining the availability required for service continuity.
3Device complexity
If resources remain in current locations, then migration complexity is avoided, but user experience deteriorates
Solution Approach 1:
The system implements self-service automation for resource migration decisions and execution. The front-end component automatically collects log information, analyzes access patterns, determines optimal migration targets, and executes resource relocation without manual intervention. This automation eliminates the complexity burden from operators while continuously improving user experience through data-driven optimization.
Solution Approach 2:
The system establishes a feedback loop where user access patterns are continuously monitored through log collection, analyzed to identify optimization opportunities, and used to drive automated migration decisions. This closed-loop feedback mechanism improves user experience systematically while keeping migration complexity managed through automation rather than manual processes.
Data Source
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AI summary
An exemplary system includes a front-end component to receive requests for resources in a data center and configured to associate each request with identifying information, to locate one or more resources for each request and to store, in a log file, the identifying information and information about the location of the one or more resources; one or more distributed computation and storage components to acquire log file information and configured to analyze log information to decide if one or more resources associated with one or more requests should be migrated to a data center in a different geographical location; and a location service component to receive decisions made by the one or more distributed computation and storage components and configured to inform the front-end component when a decision causes one or more resources to be migrated to a data center in a different geographical location to thereby allow the front-end component to re-direct future requests for the one or more migrated resources to the data center in the different geographical location. Various other devices, systems and methods are also described.