Precomputing Data Center Workloads to Reduce Peak Latency
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Solution Overview
Problem
Data centers face inefficiencies due to varying workload demands, leading to underutilization of capacity during low-demand periods and potential latency during high-demand periods, resulting in high operational costs and stranded capacity.
Innovation Solution
Implementing pre-computation of predicted tasks during low-demand periods to smooth workload, utilizing optimal operational times and energy costs, and storing pre-computed data for rapid retrieval during high-demand times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data centers are designed with extra capacity to accommodate peak computing volume, then peak performance is improved, but capital costs and operational costs increase due to underutilization during low-demand periods
Solution Approach 1:
The patent applies preliminary action by pre-computing tasks during low-demand periods before they are actually needed. The system identifies candidate tasks that are likely to be requested during peak periods and performs their computation in advance when resources are underutilized. This shifts workload from peak periods to off-peak periods, allowing the data center to operate at more consistent utilization levels and reduce the need for excessive peak capacity.
2Loss of energy
If data centers operate at mean workload level to reduce costs, then operational costs are reduced, but data latency increases and user experience deteriorates during peak periods
Solution Approach 1:
The system performs preliminary computation of candidate tasks during low-demand periods, storing results for rapid retrieval during peak periods. This pre-computation approach ensures that when peak demand occurs, pre-computed results can be returned quickly without requiring real-time computation, thus maintaining low latency while operating at mean workload levels during off-peak times.
3Use of energy by stationary object
If CPU throughput is throttled to limit power consumption during low-demand periods, then energy costs are reduced, but computational efficiency and responsiveness worsen when demand increases
Solution Approach 1:
The patent utilizes low-demand periods to perform preliminary computation of candidate tasks, effectively using available computational capacity during these periods to prepare results for future peak demand. This approach allows the system to maintain lower power consumption during off-peak hours while still ensuring high productivity during peak periods through rapid retrieval of pre-computed results.
Data Source
AI summary
Pre-computing a portion of forecasted workloads may enable load-balancing of data center workload, which may ultimately reduce capital and operational costs associated with data centers. Computing tasks performed by the data centers may be analyzed to identify computing tasks that are eligible for pre-computing, and may be performed prior to an actual data request from a user or entity. In some aspects, the pre-computing tasks may be performed during a low-volume workload period prior to a high-volume workload period to reduce peaks that typically occur in data center workloads that do not utilize pre-computation. Statistical modeling methods can be used to make predictions about the tasks that can be expected to maximally contribute to bottlenecks at data centers and to guide the speculative computing.