Cloud Workload Profiles for Resource Optimization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current cloud infrastructure faces challenges in efficiently managing diverse workloads due to overprovisioning of resources, leading to stranded resources, increased costs, and unnecessary power and space allocation.

Innovation Solution

The generation and use of workload profiles, which are probabilistic models of actual workloads, allow cloud providers to anticipate resource needs, optimize resource allocation, and improve forecasting capabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If cloud providers overprovision computing hardware to ensure maximum availability, then service availability is improved, but resource utilization efficiency deteriorates

Engineering Contradiction:

Improveservice availabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of workload telemetry data to generate probabilistic workload profiles before provisioning resources. These profiles predict future resource requirements, allowing providers to allocate resources in advance based on actual workload patterns rather than overprovisioning, thus maintaining availability while improving efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously collects telemetry data from workloads and uses machine learning models to update workload profiles in real-time. This feedback loop enables dynamic resource allocation that adapts to changing workload conditions, ensuring availability while optimizing resource utilization by allocating resources based on actual rather than predicted peak demands

Inventive Principle:

Principle #23Feedback

2Reliability

If cloud providers overprovision resources to meet diverse workload requirements, then service reliability is improved, but infrastructure cost increases

Engineering Contradiction:

Improveservice reliabilityVSAvoidinfrastructure cost

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system generates workload profiles in advance that capture the probabilistic nature of resource requirements. These profiles enable providers to provision resources based on statistically-derived requirements rather than conservative overprovisioning, reducing infrastructure costs while maintaining service reliability through accurate prediction of actual needs

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transforms raw telemetry data into probabilistic parameters that define workload profiles. By changing the representation of workload characteristics from static allocations to dynamic probabilistic models, the system optimizes resource allocation to match actual usage patterns, reducing unnecessary infrastructure expenditure while preserving reliability

Inventive Principle:

Principle #35Parameter changes

3Productivity

If cloud providers allocate more resources to ensure performance needs, then workload performance is improved, but resource waste increases

Engineering Contradiction:

Improveworkload performanceVSAvoidresource waste

Core Design Contradiction:

ProductivityVSLoss of substance

Solution Approach 1:

The system performs preliminary workload analysis to establish baseline performance requirements and resource needs. By understanding the probabilistic nature of workload demands in advance, providers can allocate resources that match actual performance needs rather than allocating excessive resources, thus maintaining productivity while reducing waste

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of allocating full excessive resources to all workloads, the system applies partial provisioning based on workload-specific probabilistic profiles. Each workload receives precisely the resources it needs according to its characterized behavior patterns, eliminating the waste associated with uniform overprovisioning while maintaining necessary performance levels

Inventive Principle:

Principle #16Partial or excessive action

4Adaptability or versatility

If cloud infrastructure is expanded to support diverse workloads, then workload versatility is improved, but management complexity increases

Engineering Contradiction:

Improveworkload versatilityVSAvoidmanagement complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The workload profile generation system serves multiple functions: characterizing workloads, predicting resource needs, optimizing allocation, and performance monitoring. This universal approach to workload management handles diverse workload types through a single framework, improving versatility while reducing the complexity that would arise from separate management systems for different workload categories

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12265925B2Cloud workload management using workload profiles

Publication Date: 2025.04.01 VERIZON PATENT & LICENSING INC

AI summary

Disclosed are embodiments for profiling active workloads in a cloud platform and downstream applications for improving cloud infrastructure based on the profiling. In one embodiment, a method comprises receiving telemetry vectors from a computing device in a cloud platform, each of the telemetry vectors including a plurality of measured values associated with a workload executing on the computing device; clustering the telemetry vectors using an unsupervised learning algorithm, the unsupervised learning algorithm outputting parameters associated with a plurality of clusters; and generating a workload profile from the plurality of clusters by selecting at least one cluster from the plurality of clusters and storing corresponding parameters of the at least one cluster as the workload profile.