Grid Managers Dynamic Resource Allocation
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Solution Overview
Problem
In data centers, server clusters often inefficiently manage unpredictable workloads, leading to underutilization of resources and unnecessary costs due to over-provisioning for peak demands.
Innovation Solution
A network system that includes a grid computing environment where grid managers dynamically allocate and deallocate computational resources by loading new instructions to modify service behavior without restarting, enabling efficient resource utilization and management across a network of computers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers are provisioned for peak workload demands, then network bottlenecks are avoided and connectivity is safeguarded, but servers operate well under capacity most of the time leading to underutilization and unnecessary costs
Solution Approach 1:
The patent implements dynamic resource allocation where grid managers continuously monitor workload demands and dynamically adjust resource allocation in real-time. Services are launched, scaled, or terminated based on actual demand patterns, transforming the static server provisioning model into a dynamic system that adapts to changing workloads, thereby eliminating over-provisioning while maintaining reliability.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed to variable based on workload characteristics. By monitoring parameters such as CPU usage, memory demand, and network traffic patterns, the system adjusts resource allocation parameters dynamically, allocating resources only when and where needed rather than maintaining constant high capacity across all servers.
2Ease of manufacture
If traditional server clusters manage unpredictable workloads, then infrastructure is maintained, but inefficient resource management leads to idle servers and constrained servers
Solution Approach 1:
The patent implements self-service through automated grid managers that independently monitor workload patterns, predict resource needs, and execute allocation decisions without human intervention. The system automatically launches services when workload thresholds are detected and terminates them when demand subsides, enabling the infrastructure to manage itself and eliminating the need for manual server provisioning and deprovisioning.
Solution Approach 2:
The system incorporates continuous feedback loops where grid managers monitor actual workload performance, compare it against target metrics, and automatically adjust resource allocation accordingly. This closed-loop control ensures that resources are allocated efficiently based on real-time performance data, preventing both over-provisioning and under-provisioning while maintaining optimal infrastructure utilization.
3Ease of operation
If services are modified by loading new instructions, then behavior modification is achieved without restarting, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-compiling and preparing instruction sets that can be dynamically loaded into services. These pre-prepared instruction modules are stored in a repository and can be quickly instantiated and injected into running services when behavior modification is needed, avoiding the complexity of compiling instructions at runtime while enabling flexible service adaptation without restarts.
Data Source
AI summary
A network of grid managers includes a first computer linked to a second computer, the first computer having a first grid manager and the second computer having a second grid manager, the first and second grid managers handling at least locating, reserving, allocating, monitoring, and deallocating one or more computational resources for an application, the grid manager, upon receipt of a command, loads new instructions to modify current instructions residing in the service that modifies a behavior of the service without restarting the service.