Dynamic Workload Orchestration for Heterogeneous Software Processes
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Solution Overview
Problem
In data centers, software processes often do not fully utilize hardware resources, leading to inefficiencies and increased costs due to static hardware configurations and inefficient workload distribution in cloud-computing environments, where different software processes have varying resource requirements.
Innovation Solution
A method for optimizing the distribution of heterogeneous software workloads by predicting resource needs, analyzing workload profiles, and dynamically scheduling software processes on computing hosts to maximize hardware utilization and minimize idle resources, using a resource coordinate system and machine-learning prediction models to balance workload distribution and orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware resources are adjusted to suit specific needs of software processes in traditional on-premise deployments, then software processes can fully utilize hardware resources, but hardware configuration becomes static and inflexible in cloud-computing environments
Solution Approach 1:
The patent implements dynamic workload distribution and orchestration that continuously monitors hardware resource usage and automatically adjusts software process placement across computing hosts. This dynamic approach replaces static hardware configurations with adaptive resource allocation, allowing the system to respond to changing workload demands while maximizing hardware utilization.
Solution Approach 2:
The patent creates a universal orchestration layer that manages heterogeneous software processes across diverse hardware resources. This layer provides a unified interface for resource allocation that works across different computing hosts and hardware configurations, enabling both specialized resource utilization and flexible reconfiguration without requiring dedicated hardware for each software process type.
2Adaptability or versatility
If cloud-computing architectures permit elastic reactions to workload, then hardware resource flexibility improves, but not all software processes are configured to leverage this functionality leading to inefficiencies
Solution Approach 1:
The patent implements a feedback-driven orchestration system that continuously monitors hardware resource usage, workload characteristics, and software process performance. This feedback loop enables the system to automatically adjust workload distribution and trigger elastic scaling decisions, ensuring that software processes leverage cloud-computing elasticity while maintaining optimal efficiency through continuous optimization.
Solution Approach 2:
The patent enables software processes to automatically leverage elastic resource allocation through the orchestration layer. The system self-adjusts resource allocation based on monitored workload characteristics, eliminating the need for manual configuration while allowing software processes to benefit from elastic scaling and dynamic resource availability.
3Productivity
If workload distribution is optimized to maximize hardware utilization, then hardware resource efficiency improves, but ensuring no hardware capacity limits are reached requires complex orchestration
Solution Approach 1:
The patent introduces an orchestration layer as an intermediary between workload distribution and hardware resource management. This intermediary automatically handles the complexity of monitoring hardware capacity limits, adjusting workload placement, and coordinating resource allocation across computing hosts. By centralizing these control functions, the system achieves optimized hardware utilization without requiring complex distributed coordination throughout the entire system.
Data Source
AI summary
A request is received to schedule a new software process. Description data associated with the new software process is retrieved. A workload resource prediction is requested and received for the new software process. A landscape directory is analyzed to determine a computing host in a managed landscape on which to load the new software process. The new software process is executed on the computing host.