Topology Manager for Energy-Aware Task Path Selection
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Solution Overview
Problem
Current energy management techniques in computing systems are inadequate in dynamically optimizing energy usage in cloud computing environments, leading to inefficiencies and increased carbon emissions due to idle devices and peak usage patterns.
Innovation Solution
A computer-implemented method that creates a topology for network computing systems by determining tasks for computing devices, connecting nodes based on execution order, and selecting paths with lower energy consumption to perform workloads, while considering alternative paths and energy metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If devices are kept active to handle peak usage, then system performance is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the computing topology by selecting different execution paths based on workload conditions. The topology manager continuously monitors and reconfigures the execution path to optimize energy consumption while maintaining performance, transitioning between different device states and configurations as needed
Solution Approach 2:
The system changes operational parameters by selecting different execution paths with varying energy consumption characteristics. The topology manager evaluates multiple paths and selects the optimal one based on current workload and energy constraints, effectively changing the system's operational state to balance performance and energy usage
2Use of energy by moving object
If devices are placed in sleep mode to save energy, then energy consumption is reduced, but system response time worsens
Solution Approach 1:
The system performs preliminary actions by pre-warming or pre-activating devices along the selected execution path before workload arrives. The topology manager can proactively prepare computing resources to ensure immediate response when tasks are assigned, reducing the latency associated with waking from sleep mode
Solution Approach 2:
The system maintains continuity of useful action by keeping devices in a ready state or lightly active along the execution path. Rather than completely shutting down devices, the topology manager maintains them in states that allow quick resumption of full operation, ensuring continuous capability without complete idle shutdown
3Productivity
If more devices are activated to handle workload, then processing capacity is improved, but energy waste increases
Solution Approach 1:
The system segments the workload execution into distinct paths through the computing topology. The topology manager divides the overall task execution into multiple possible routes, each utilizing different subsets of devices, allowing selective activation of only the necessary segments required for current workload processing
Solution Approach 2:
The system applies local quality by optimizing energy consumption at each node and path segment independently. The topology manager evaluates energy characteristics of individual paths and selects the optimal route through the topology, ensuring that only the necessary local resources are activated rather than globally activating all devices
4Use of energy by moving object
If topology is optimized for energy savings, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary component - the topology manager - that handles the complexity of path selection and energy optimization. This intermediary layer manages the sophisticated topology decisions while presenting a simplified interface to the rest of the system, encapsulating the complexity in a dedicated management component
Solution Approach 2:
The system implements feedback mechanisms where the topology manager continuously monitors energy consumption and workload conditions, then adjusts the execution path accordingly. This closed-loop feedback allows the system to automatically optimize energy efficiency without requiring complex manual configuration, using real-time data to drive topology decisions
Data Source
AI summary
A computer implemented method manages a topology for a network computing system. A number of processor units determines tasks for a workload to be performed by computing devices in the network computing system. The number of processor units creates the topology for performing the tasks in the workload. The topology comprises nodes representing the tasks assigned to the computing devices. The nodes are connected to each other based on an execution order for the tasks, and the topology has alternative paths for performing the tasks for the workload. The number of processor units determines energy consumption for the alternative paths in the topology. The number of processor units selects a particular path from the alternative paths for performing the tasks for the workload based on the energy consumption for the alternative paths.