Dynamic Computing Job Placement Across Data Centers
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Solution Overview
Problem
Modern data centers face high operational costs due to energy expenses, and existing solutions to reduce these costs either increase financial costs, unexpectedly raise energy costs, or impede performance by migrating computing jobs to less efficient data centers.
Innovation Solution
Techniques for dynamically placing computing jobs across multiple data centers based on marginal electricity usage, availability of renewable energy, resource capacity constraints, and bandwidth costs to minimize overall financial and energy expenditures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If computing jobs are migrated to data centers with lower energy costs, then energy costs are reduced, but financial costs increase
Solution Approach 1:
The system dynamically places computing jobs across multiple data centers based on real-time conditions including energy costs, financial costs, resource capacity, and bandwidth availability. This dynamic approach allows the system to adapt to changing conditions and optimize the trade-off between energy costs and financial costs, rather than statically migrating jobs to locations with lower energy costs.
Solution Approach 2:
The system changes multiple parameters simultaneously when placing computing jobs, including energy cost parameters, financial cost parameters, resource capacity parameters, and bandwidth cost parameters. By considering and adjusting multiple parameters together, the system can find optimal placement decisions that balance energy cost reduction with financial cost management.
2Use of energy by stationary object
If computing jobs are migrated to reduce energy costs, then energy costs are reduced, but performance is impeded
Solution Approach 1:
The system considers resource capacity constraints as a key parameter when placing computing jobs. By evaluating resource capacity alongside energy costs, the system ensures that jobs are placed in data centers that can actually deliver the required performance and resource availability, preventing performance degradation while still achieving energy cost reduction.
Solution Approach 2:
The system uses feedback from resource capacity monitoring and performance metrics to make informed job placement decisions. By continuously monitoring resource capacity and performance outcomes, the system can adjust its placement strategy to maintain performance while optimizing energy costs.
3Use of energy by stationary object
If computing jobs are dynamically placed based on multiple factors, then overall costs are reduced, but system complexity increases
Solution Approach 1:
The system implements a universal job placement framework that handles multiple objectives simultaneously - energy cost optimization, financial cost management, performance maintenance, and resource capacity management. This multi-functional approach consolidates what would otherwise require separate systems into a single unified platform, managing complexity through integration rather than proliferation of separate components.
Solution Approach 2:
The system introduces an intermediary job placement mechanism that mediates between multiple conflicting objectives and data centers. This intermediary layer processes multiple factors (energy costs, financial costs, resource capacity, bandwidth costs) and translates them into optimal job placement decisions, simplifying the overall system architecture by centralizing the decision-making logic.
Data Source
AI summary
This document describes techniques for dynamically placing computing jobs. These techniques enable reduced financial and/or energy costs to perform computing jobs at data centers.