Dynamic Network Resource Assignment to Reduce Waste in Pod Slices
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Solution Overview
Problem
In wireless communication systems, network resources are often wasted due to pods being allocated with more resources than needed, leading to inefficiencies and reduced processing speeds.
Innovation Solution
Systems and methods dynamically assign and redistribute network resources to pods, resource pools, and slices, utilizing machine learning algorithms to optimize resource allocation and reduce waste, enabling efficient access to specific services during maintenance windows and peak operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pods are allocated with fixed network resources, then resource availability is guaranteed, but resource waste increases when resources exceed needs
Solution Approach 1:
The patent implements dynamic resource allocation where network resources are not fixed to pods but are continuously adjusted based on real-time monitoring of resource usage patterns. The system monitors actual resource consumption and dynamically assigns or reclaims resources, transforming the static allocation model into a dynamic one that adapts to changing conditions, thereby eliminating waste while ensuring availability.
Solution Approach 2:
The system changes the allocation parameters of network resources by adjusting the amount of resources assigned to pods based on measured usage. Instead of maintaining constant resource levels, the system modifies resource parameters (such as bandwidth allocation, processing power) in response to actual demand, allowing optimal utilization without over-provisioning.
2Adaptability or versatility
If more network resources are allocated to pods, then service access capability is improved, but processing speed decreases due to resource waste
Solution Approach 1:
The patent enables pods to self-regulate their resource consumption through monitoring and feedback mechanisms. Each pod reports its actual resource usage to the allocation system, which then adjusts allocations accordingly. This self-service approach ensures that pods receive adequate resources for service access while preventing excessive allocation that would waste processing capacity and reduce overall system productivity.
3Productivity
If network resources are dynamically reassigned, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback-driven resource allocation system where resource usage is continuously monitored and fed back to the allocation mechanism. This feedback loop enables automatic adjustment of resource assignments based on actual needs, improving efficiency without requiring complex manual intervention. The systematic feedback approach manages complexity by using standardized monitoring and allocation protocols.
Solution Approach 2:
The resource allocation system is designed as a universal platform that can manage multiple types of network resources (bandwidth, processing power, storage) across multiple pods simultaneously. This multi-functional allocation mechanism handles diverse resource types through a unified approach, reducing the need for separate complex management systems for each resource type and thereby controlling overall system complexity.
4Stability of the object's composition
If resources are shuffled between pods during maintenance windows, then resource distribution symmetry is improved, but service access time increases
Solution Approach 1:
The patent performs resource reassignment and shuffling operations during pre-scheduled maintenance windows when service demand is minimized. By anticipating low-traffic periods and preparing resource reallocation in advance, the system achieves symmetric resource distribution without significantly impacting service access times during peak operations. This preliminary action during maintenance windows balances resource distribution while minimizing service disruption.
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. The memory may comprise information on one or more network resources. The processor may be configured to determine that the one or more network resources are available for allocation to first resource pools and second resource pools in response to determining that the one or more network resources are unassigned, assign first network resources to the first resource pools, and assign second network resources to the second resource pools. Further, the processor may be configured to generate a first pod in the one or more containerized clusters comprising the first resource pools, generate a second pod comprising the second resource pools, assign a first slice group ID of one or more slice group IDs to the first pod and assign a second slice group ID of the one or more slice group IDs to the second pod.