Container Manager Resource Allocation via Supply Chain Economics
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Solution Overview
Problem
Conventional virtualization systems face challenges in efficiently managing resources, including excessive allocation, inefficient scaling, and poor prediction of future resource needs, leading to overprovisioning or underprovisioning, especially in container systems where dynamic changes are frequent.
Innovation Solution
The introduction of supply chain economics and virtual currency units to optimize resource management in container systems, allowing for dynamic allocation and reallocation of resources based on demand, with a Container Manager determining the necessary resource bundles and automatically selecting the most cost-effective servers, and allocating resources to meet service level agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional virtualization systems allocate resources to handle peak demands, then system reliability is improved, but resource utilization efficiency deteriorates due to excessive allocation
Solution Approach 1:
The patent implements dynamic resource allocation in container systems where resources are not statically assigned but continuously adjusted based on real-time demand monitoring. The system automatically scales resource allocation up or down to match actual workload requirements, eliminating the need for excessive static allocation while maintaining system reliability during peak demands.
Solution Approach 2:
The container system autonomously manages its own resource allocation without requiring manual intervention or over-provisioning. Through self-monitoring and automatic scaling mechanisms, the system serves itself by dynamically acquiring and releasing resources based on actual needs, improving both reliability and efficiency simultaneously.
2Stability of the object's composition
If dedicated servers are used for each application, then system stability is improved, but scalability deteriorates due to difficulty in scaling and managing multiple servers
Solution Approach 1:
The patent implements a universal container platform that can host multiple applications and workloads on shared infrastructure. Instead of requiring dedicated servers for each application, the system provides a multi-functional environment where containers can be dynamically created, deployed, and managed, enabling both stability and scalability through resource virtualization and orchestration.
Solution Approach 2:
The system segments applications into isolated containers that can independently utilize shared resources. This segmentation allows each application to maintain its own stable environment while the overall system achieves scalability through efficient resource sharing and container orchestration, eliminating the need for dedicated physical servers.
3Loss of energy
If virtual machines are used for resource sharing, then resource utilization efficiency is improved, but automation capability deteriorates due to lack of automated resource management
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor container resource consumption, workload demands, and system state. This feedback drives automatic decision-making for resource allocation, scaling, and orchestration, enabling the system to efficiently manage resources while maintaining high automation capability through closed-loop control.
Solution Approach 2:
The container system autonomously performs resource management tasks including allocation, scaling, and orchestration without manual intervention. Through self-service automation, the system efficiently utilizes shared resources while simultaneously achieving high automation capability, as containers automatically negotiate and acquire resources based on their needs.
4Reliability
If excessive resources are allocated to handle peak demands, then service level agreement compliance is improved, but cost efficiency deteriorates due to overprovisioning
Solution Approach 1:
The system dynamically adjusts resource allocation to match actual demand patterns rather than relying on static over-provisioning. Through continuous monitoring and automatic scaling, the system maintains service level agreement compliance during peak demands while releasing excess resources during low-utilization periods, thereby eliminating wasteful spending on permanently allocated but underutilized resources.
Solution Approach 2:
The patent implements parameter changes in resource allocation based on real-time workload conditions. The system adjusts resource parameters such as CPU allocation, memory allocation, and storage capacity dynamically, allowing it to meet service level agreements when needed while optimizing cost efficiency by reducing resource allocation during periods of lower demand.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, are disclosed for managing resources in container systems, including multi-cloud systems. The use of supply chain economics alone and in combination with other techniques offers a unified platform to integrate, optimize or improve, and automate resource management in a container system. These techniques may be used to auto-scale or place container or pod entities. They may also be used to monitor and control contention of computing resources in a container system, and to place, clone, resize, suspend or terminate computing resources.