Workload Energy Profile Scheduling Optimization
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Solution Overview
Problem
Existing networked computing environments, such as cloud computing, often neglect energy efficiency in workload scheduling, leading to increased costs and potential energy shortages due to rising energy prices.
Innovation Solution
An energy profile is identified for each workload, detailing required computing resources, energy consumption attributes, and proposed duration, allowing for the determination of an optimized schedule that minimizes total processing costs and adheres to budget constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional workload scheduling is used without energy optimization, then workload processing can be completed, but energy consumption increases and costs rise
Solution Approach 1:
The system performs preliminary analysis by identifying energy profiles for workloads before scheduling them. Energy profiles contain energy consumption attributes and proposed durations that are determined in advance, allowing the scheduler to make informed decisions about when and where to execute workloads to minimize energy costs while maintaining processing efficiency
Solution Approach 2:
The scheduling system dynamically adjusts workload execution timing and resource allocation based on energy profiles and current energy conditions. The optimizer continuously evaluates different scheduling scenarios and modifies the schedule to achieve optimal energy consumption while ensuring workload completion within acceptable timeframes
2Use of energy by moving object
If energy optimization scheduling is implemented, then energy consumption is reduced, but system complexity increases
Solution Approach 1:
The system enables workloads to essentially schedule themselves by attaching energy profiles to each workload that contain all necessary energy consumption attributes and proposed durations. The optimization engine processes these profiles automatically to generate optimized schedules, reducing the need for complex manual scheduling configurations while achieving energy efficiency
Solution Approach 2:
The system introduces energy-related parameters (energy consumption attributes, proposed duration, cost constraints) into the workload description and uses these parameters to drive scheduling decisions. By changing the scheduling approach from purely time-based to energy-aware parameter optimization, the system achieves energy reduction without requiring fundamentally new system architecture
3Loss of energy
If workload schedules are optimized for energy consumption, then total processing costs are minimized, but scheduling flexibility is reduced
Solution Approach 1:
The system optimizes schedules to meet energy cost targets rather than minimizing every possible energy expenditure. The energy profile contains a proposed duration that serves as a constraint, allowing workloads to be scheduled with some flexibility around energy-optimal times while ensuring completion within acceptable time windows, thus balancing cost minimization with operational flexibility
Data Source
AI summary
Embodiments of the present invention provide an approach for optimizing energy consumption utilized for workload processing in a networked computing environment (e.g., a cloud computing environment). Specifically, when a workload is received, an energy profile (e.g., contained in a computerized data structure) associated with the workload is identified. Typically, the energy profile identifies a set of computing resources needed to process the workload (e.g., storage requirements, server requirements, processing requirements, network bandwidth requirements, etc.), energy consumption attributes of the set of computing resources, and a proposed duration of the workload. Based on the information contained in the energy profile (and resource availability) a schedule (e.g., time, location, etc.) for processing the workload will be determined so as to optimize energy consumption associated with the processing of the workload. In a typical embodiment, the schedule will be determined such that a total cost for processing the workload can be minimized and/or to any budgeted amount/costs can be met.