Cloud EDA Resource Provisioning via Performance Metrics

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Solution Overview

Problem

Electronic design automation (EDA) tasks require significant computing resources, leading to inefficiencies due to under-provisioning or over-provisioning, resulting in increased costs and resource idle times, especially with fluctuating demand during IC design cycles.

Innovation Solution

Provisioning computing resources in public cloud infrastructure based on performance metrics, dividing EDA tasks for parallel execution, and dynamically assigning tasks to minimize costs by leveraging performance ratios and historical data to optimize resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If redundant computing resources are provisioned to ensure EDA tasks are finished within time, then the task completion reliability is improved, but the cost increases

Engineering Contradiction:

Improvetask completion reliabilityVSAvoidcomputing resources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically provisions computing resources based on real-time workload analysis and performance metrics. Instead of statically over-provisioning resources, the system adjusts resource allocation dynamically to match actual demand, ensuring task completion reliability while avoiding unnecessary resource consumption and costs.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms by monitoring performance metrics of computing resources and using this information to optimize resource provisioning. Historical performance data is analyzed to predict future resource needs, allowing the system to provision resources efficiently while maintaining reliable task completion within time constraints.

Inventive Principle:

Principle #23Feedback

2Quantity of substance

If computing resources are under-provisioned, then the cost is reduced, but the EDA task set may not be finished within time

Engineering Contradiction:

Improvecomputing resourcesVSAvoidtask completion time

Core Design Contradiction:

Quantity of substanceVSDuration of action of moving object

Solution Approach 1:

The system performs preliminary analysis of EDA task requirements and historical performance data before provisioning resources. By predicting resource needs in advance based on task characteristics and past performance metrics, the system can provision appropriate computing resources beforehand, ensuring tasks complete within time constraints without over-provisioning.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes provisioning parameters based on task-specific requirements and performance metrics. Different EDA tasks receive customized resource allocations based on their computational demands, allowing the system to optimize both resource utilization and task completion time for each specific workload.

Inventive Principle:

Principle #35Parameter changes

3Quantity of substance

If servers remain idle during non-design periods, then the infrastructure cost is reduced, but insufficient resources are available when design activity increases

Engineering Contradiction:

Improvecomputing resourcesVSAvoidresource adaptability

Core Design Contradiction:

Quantity of substanceVSAdaptability or versatility

Solution Approach 1:

The system creates a hybrid infrastructure where computing resources serve multiple purposes - private infrastructure handles routine design work, while public cloud resources provide supplemental capacity during peak demand periods. This multi-functional approach allows the same resource pool to adapt to varying workload requirements without permanent over-provisioning.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically transitions between private and public cloud resources based on workload demands. During low-activity periods, resources are consolidated to reduce costs; during high-demand periods, additional public cloud resources are provisioned automatically, providing adaptability without permanent infrastructure bloat.

Inventive Principle:

Principle #15Dynamics

4Reliability

If EDA tasks are executed on private infrastructure only, then security and control are improved, but resource utilization efficiency deteriorates due to idle times

Engineering Contradiction:

Improvetask execution controlVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments EDA task execution between private and public cloud infrastructures. Critical tasks requiring high security and control remain on private infrastructure, while less sensitive tasks can be executed on public cloud resources during idle periods, improving overall resource utilization efficiency without compromising essential security requirements.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20110246653A1Efficient provisioning of resources in public infrastructure for electronic design automation (EDA) tasks

Publication Date: 2011.10.06 SYNOPSYS INC

AI summary

Provisioning resources in public cloud infrastructure to perform at least part of electronic design automation (EDA) tasks on the public cloud infrastructure. Performance metrics of servers in the public cloud infrastructure and performance history of a user's past EDA tasks are maintained to estimate operation parameters such as runtime of a new EDA task. Based on the estimation, a user can provision appropriate types and amounts of resources in the public cloud infrastructure in a cost-efficient manner. Also, a plurality of EDA tasks are assigned to computing resources in a manner that minimizes the overall cost for performing the EDA tasks.