Program Initiation/Switching TRIZ Cases

This case distributes cloud jobs across nearby consumer devices to cut infrastructure costs, latency, and disruption risk.

A task manager uses queues and a dependency matrix to reduce command-processor traffic and support parallel neural task execution.

A lattice task interface displays running app previews together, supporting clearer switching, drag-and-drop, and split-screen control.

A round-robin subset schedule tracks multiple objects within a fixed compute budget while reducing processing overhead.

This case routes pipeline tasks to environments matching their properties, reducing data movement and conserving compute resources.

This case uses AI to predict future task demands, guiding node selection to improve cloud resource efficiency and execution reliability.

AIoF frames AI task requests across disaggregated resources, using transport-layer credits to reduce overhead and protect compute capacity.

Candidate cache locations are tested with mini-batches to select faster data access for deep learning jobs under limited storage.

Event notifications trigger virtual machine resource pool scaling, balancing available resources against memory waste and rejected calls.

Machine learning selects cloud service combinations to balance deadlines, costs, and resource use.

Group-private memory arenas isolate failures, letting affected processing groups recover while others continue running.

This case uses released-memory application lists to restart background processes and reduce repeated kills on Android terminals.

This case combines push scheduling for HPC with pull scheduling for big data to reduce master-node load and improve throughput.

This case divides large workloads into equal sub-workloads for faster dispatch and balanced execution in compute-near-memory processors.

Deterministic compilation predicts model resource needs for reliable processor scheduling.

A memory controller uses priority queues and adaptive mode switching to schedule mixed PIM and non-PIM workloads with lower latency.

A secondary process mediates wake-ups after secondary-server synchronization, reducing transaction latency and process load.

Preassigned hash-ring relationships let busy clusters borrow directly from idle clusters, reducing query delay and contention.

Multicore scheduling stores essential process context in non-volatile memory for rapid recovery.

An operating system assigns application tasks to typed queues and allocates threads by load, reducing bursts and system overhead.

The scheduler extracts parallel RPA sections, estimates execution times, and matches idle bots by priority to improve utilization.

A separate storage module serializes and encrypts application data for secure retention and restoration during secure element OS upgrades.

A context switch instruction moves only process-used contiguous registers, reducing save/restore overhead and application latency.

A super proxy selects tunnel devices by attributes such as IP geolocation to improve content fetching efficiency and routing.

Manager-worker nodes throttle distributed scanning for compliant, reliable processing.

This GPU shader approach delays varying attribute fetches until cull results arrive, reducing redundant computation and memory bandwidth.

This graphics rendering case simplifies shaders as GPU demand rises, helping maintain smooth application performance in complex scenes.

Shadow register files, TLBs, and L1 caches preload successor contexts to reduce switching overhead during L4 cache misses.

A rescheduling judgment unit monitors production conditions and regenerates work orders for urgent tasks and new orders.

A cloud framework generates operation-specific dictionaries and validates selected technical operations, improving job performance.

External protocol handling saves execution state, allowing deterministic code to resume after failed operations without redundant retrieval.

A conference terminal displays a clone interface so users can control large screens more easily without a separate control terminal.

Dedicated task control interfaces manage hardware across secure, non-secure, and realm worlds while limiting cross-state communication.

Tokenization, state-dependency removal, testing, functional separation, and security hardening convert legacy code for cloud deployment.

Shared interrupt vectors gain class and source data, reducing software complexity and latency during processor interrupt handling.

Linked task metadata structures encapsulate execution state for flexible scheduling and higher multiprocessor processing throughput.

One-to-one host exchange links accelerator clusters, reducing communication overhead while scaling distributed machine learning training.

A controller scales non-interactive job clusters from queue size, monitors startup, and replaces failures during task processing.

A pass-through app and uncaught exception handler restart or reboot AR wearables when foreground applications become unresponsive.

A host scheduler reserves GPU streaming multiprocessors by task execution time and period for predictable real-time processing.

GPU APIs deallocate nested resource structures to improve memory efficiency.

A two-phase virtual switch configuration keeps services connected while reducing network interruption during VM migration.

Estimated GPU task times are compared with deadlines to adjust scheduling and operating parameters for efficient execution.

A batch queue thread manager adapts active threads to CPU, memory, and workload conditions across distributed servers.

A component sharing layer enables discovery, access control, and connections across heterogeneous agents, improving reuse and robustness.

Stored restart conditions and job associations resume abnormal job flows from selected starting points, reducing unnecessary reprocessing.

Workload-aware agents dynamically tune network node parameters for better performance.

A cross-cluster nanoservice detects microservice restarts and informs the external job service to reset affected jobs.

A unified pipeline model resolves workflow-versus-pipeline confusion by coordinating shared contexts and independently running others.

The compiler tests parallelism and scheduling options, then uses learned parameters to reduce AI processor power consumption.