Dynamic Resource Chain Assembly for Service-Level Compliance
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Solution Overview
Problem
Distributed computing systems face challenges in managing resource allocation and deployment due to varying computing requirements across different business groups within an enterprise, making it difficult to maintain sufficient capacity to meet service-level agreements.
Innovation Solution
A control node automatically manages resource chains by evaluating current capacity levels and dynamically assembling or disassembling resource chains to ensure compliance with service-level agreements, using asset declarations and capacity calculations to determine resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are preconfigured in fixed combinations to meet service-level agreements, then service reliability is improved, but system adaptability deteriorates
Solution Approach 1:
The patent implements dynamic resource chain assembly where resources are not fixed in preconfigured combinations but are dynamically selected and assembled based on current capacity levels and service requirements. The control node continuously evaluates resource capacities and adjusts resource chain compositions in real-time, transforming the static preconfiguration approach into a dynamic adaptive system that maintains reliability while improving flexibility.
2Adaptability or versatility
If resources are dynamically assembled based on capacity evaluation, then system adaptability is improved, but management complexity deteriorates
Solution Approach 1:
The control node implements self-service mechanisms by automatically evaluating resource capacity levels, determining appropriate resource chain assemblies, and executing assembly/disassembly operations without external intervention. The system monitors its own state, makes autonomous decisions about resource allocation, and adjusts configurations based on service-level agreement requirements, thereby managing complexity through automation rather than manual processes.
Solution Approach 2:
The patent incorporates feedback loops where the control node continuously monitors resource capacity levels and service performance, uses this information to evaluate whether current resource chains meet service-level agreements, and adjusts resource allocations accordingly. This closed-loop feedback system manages complexity by using real-time data to drive automated decision-making rather than requiring complex pre-planning for all scenarios.
3Device complexity
If manual management of resource allocation is used, then system complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical management processes with automated computational systems. The control node uses computer-implemented algorithms to evaluate resource capacities, determine optimal resource chain assemblies, and execute configuration changes automatically. This substitution of manual operations with automated computing processes maintains relative system simplicity while dramatically improving resource utilization efficiency and response time to changing conditions.
Data Source
AI summary
In general, this disclosure describes techniques of managing resources in a service-oriented distributed computing system. As described in this disclosure, a control node automatically manages the system such that the system has sufficient capacity to satisfy an agreement to provide a given level of a service. In order to automatically manage the system, the control node evaluates the current capacity levels of resources in the system. The current capacity levels are indicative the capacity of resources to perform certain acts. The capacity of the resources to perform these acts may be indicative of whether the system is able to provide the given level of the service. Because resources may be deployed in chains, the capacities of resources to perform the certain acts may be defined to be dependent on the capacities of other resources to perform other acts.