Cloud Resource Availability Tokens for Offline Service Continuity
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Solution Overview
Problem
Cloud computing networks face disruptions when resources go off-line, leading to reduced service capabilities and loss of resource information, which complicates service planning and resource allocation, resulting in wasted computational resources due to reallocation.
Innovation Solution
A system using tokenized representations of resource availability, maintained through a decentralized blockchain network, monitors network services at an aggregate level, determining synthetic availabilities to allocate resources conservatively based on worst-case scenarios, reducing reallocation waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time resource allocation strategies are used, then resource allocation efficiency is improved, but computational resources are wasted when resources go off-line
Solution Approach 1:
The system performs preliminary actions by continuously monitoring resource availability and pre-allocating resources based on predicted availability rather than reacting to real-time status changes. This allows the system to prepare resource allocation decisions in advance, reducing the need for computationally intensive real-time recalculations when resources go offline, thus maintaining allocation efficiency while minimizing computational waste.
Solution Approach 2:
The system dynamically adjusts resource allocation strategies based on resource availability status. When resources are online, the system uses real-time allocation; when resources go offline, the system transitions to a different allocation mode that doesn't require continuous computational updates, thereby adapting the allocation process to current conditions and reducing unnecessary computational resource consumption.
2Reliability
If services are rerouted to maintain continuity, then service reliability is improved, but the aggregate number of services is reduced
Solution Approach 1:
The system performs preliminary actions by monitoring resource availability in advance and pre-planning service routing decisions. When a resource is predicted to go offline, the system has already prepared alternative routing paths and resource allocations, allowing for smooth transitions that maintain service continuity without requiring aggressive rerouting that would reduce aggregate service capacity.
Solution Approach 2:
The system implements beforehand cushioning by maintaining a buffer of alternative resources and pre-established routing paths. This cushioning allows the system to absorb resource failures without immediate service disruption, enabling graceful degradation that preserves both reliability and aggregate service capacity by having pre-prepared fallback options rather than reacting with forceful rerouting.
3Device complexity
If monitoring is performed at aggregate level, then system overview is improved, but detailed resource information becomes inaccessible
Solution Approach 1:
The system segments monitoring into multiple levels: aggregate-level monitoring for high-level overview and detailed individual resource monitoring for specific needs. The aggregate monitoring provides system-wide visibility with simpler processing, while individual resource monitoring maintains detailed information access when needed. This segmentation allows the system to balance between simplified monitoring and detailed information preservation.
Solution Approach 2:
The monitoring system is designed with multi-functionality to serve both aggregate-level overview and detailed resource information needs. The same monitoring infrastructure can provide high-level metrics for system overview while also maintaining the capability to access detailed resource data, making the monitoring system universal rather than forcing a choice between simplicity and detail.
4Reliability
If redundancy systems are implemented, then service continuity is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system implements preliminary action by pre-identifying and pre-configuring redundant resources and routing paths before failures occur. This allows the redundancy system to be activated smoothly when needed without requiring complex real-time decision-making, reducing the operational complexity of redundancy management while maintaining service continuity through pre-prepared backup systems.
Data Source
AI summary
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.