Resource Allocation TRIZ Cases

An adaptive approach predicts real-time computing resource requirements for virtual network functions using offline profiling and online monitoring.

Logical resource partitioning via realm isolation manages processor cores through dedicated security monitor realms.

A task mapping method co-locates mutually exclusive computational tasks to reduce state save overhead.

A host operating system stores write request data in shared memory and sends offload requests to virtual machines for parallel processing.

A resource allocation system groups existing test resources into combined pools to match specific task requirements.

Guest cluster virtual extensions separate infrastructure complexity from application development by deploying containers as managed virtual machine lifecycles.

Management apparatus coordinates multiple sensor nodes to combine distributed resources, satisfying complex user requests that exceed single node capabilities.

Control node segments data communication network to isolate computing clusters, resolving virtual machine performance overhead.

Asset plug-ins resolve typing and naming contradictions in script extensions by enabling conditional deployment of compatible components.

Runtime execution measurement replaces static prediction models to assign tasks accurately across heterogeneous multicore processors.

Runtime processor detects removal of unallocated arrays to maintain continuous user application execution.

Hardware acceleration devices maintain load data in memory to route requests, avoiding delays from stale software metrics.

A dynamic resource provisioning system allocates cloud computing resources across environments to balance real-time workloads efficiently.

Capability adaptors translate guest input output interfaces to match host system hardware specifications during virtual machine migration.

Host controllers transmit compact status signatures to detect changes, reducing unnecessary network resource consumption during routine monitoring.

A dynamic packet scheduler adjusts CPU core binding to work items based on real-time metrics.

Virtual vector processing distributes variable-length operations across multiple cores to resolve the contradiction between high productivity and system cost.

A network broker manages cloud resource access via a single intermediary link.

A dynamic scheduling system optimizes application response times using predicted task costs and reinforcement learning.

Standardizing control information resolves inaccurate image recognition and slow retrieval in cross-engine game testing.

Dynamic routing of composite service requests selects optimal third-party web services based on live cost and response time metrics.

Animated bounding boxes mediate between query points and primitives, reducing computational overhead while supporting arbitrary data types.

A reconfigurable arithmetic circuit dynamically adjusts hardware partitions to match problem scale and accuracy requirements.

Segmenting the control plane across multiple clusters resolves lifecycle complexity while maintaining centralized policy enforcement.

Runtime environments exchange resource units via peer-to-peer requests to eliminate centralized charging bottlenecks and reduce system complexity.

Machine learning framework predicts non-linear causal effects to determine optimal resource allocation amounts.

An allocator sends write probes to caches to detect memory address presence before directing data streams.

Telemetry data feeds machine learning models that predict workload phases, enabling dynamic hardware adaptation without complex heuristic rules.

A processor analyzes resource requirements and availability using a machine learning model to provision compute, network, and storage resources.

Allocation control apparatus assigns high-priority tasks to dedicated virtual cores.

Virtual machine monitor power manager adjusts processor core states based on wireless communication component activity levels.

A migration source proxy transitions to a queuing mode to buffer incoming data during container relocation.

Segmenting timestamped data for deterministic distribution across grid computing devices reduces computational expense and memory requirements.

A dynamic affinity policy engine monitors user density to migrate virtual machines between datacenters.

A resource manager allocates computational resources from a global pool to CPUs.

A resource pool database stores monitored consumption data to reclaim system resources, preventing overload and improving stability.

A monitoring system detects virtual resource outliers by comparing live metrics against peer cluster members.

Patsnap Eureka TRIZ analysis shows how workload modeling prevents infrastructure overload by simulating resource utilization before migration.

Auxiliary communication lines handle clock training signals to accelerate data transmission in display devices.

A baseboard management controller physically partitions host processors, memory ranges, and I/O devices into isolated resource groups.

A guest transaction queue facilitates secure asynchronous communication across cloud services, reducing bandwidth overload and protecting sensitive information.

A computing performance management system assigns dynamic budgets to anytime algorithms and redistributes remaining resources.

Segmenting the primary hypervisor into a simple version ensures critical guest OS continuity when resource dispatching fails.

A load balancer distributes tasks using a slotted hash function and maintains worker registries to ensure consistent task mapping.

A resource manager interface enables dynamic allocation and reallocation of computing resources among schedulers based on statistical usage data.

A parallel queue instance reroutes messages to healthy nodes when primary throughput drops below normal levels.

A cloud computing system uses a central controller to distribute packets across local data centers based on real-time monitoring.

Classifying host interfaces into emulation and direct connection pools reduces processor load while enabling high-end application support.