Rate Controller for Heat Budget Management in Data Processing
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Solution Overview
Problem
Packet switching devices using binary and ternary content-addressable memories generate significant heat during lookup operations, exceeding heat budgets and requiring effective rate control to manage thermal limitations.
Innovation Solution
Implementing a rate controller that utilizes heat price tags and token buckets to manage and pace data processing operations within a heat budget, prioritizing latency-sensitive operations and adjusting processing rates based on current heat signatures and budgets across multiple components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parallel search operations are performed on content-addressable memory entries, then lookup speed is improved, but heat generation increases significantly
Solution Approach 1:
The patent implements dynamic rate control that adjusts the pace of parallel search operations based on real-time heat signatures. The system monitors thermal conditions and dynamically modifies the number of parallel operations executed, allowing maximum parallelism when thermal conditions permit and reducing parallelism when heat budgets are approached, thus resolving the contradiction between maintaining high lookup speed and controlling heat generation
Solution Approach 2:
The system changes operational parameters by introducing heat price tags that quantify the thermal cost of different lookup operations. Based on these parameters, the rate controller adjusts the execution rate of parallel searches, modifying the effective throughput parameter to stay within thermal constraints while maximizing performance when possible
2Temperature
If data processing operations are rate-controlled to stay within heat budgets, then heat management is improved, but processing throughput may be reduced
Solution Approach 1:
The patent implements a feedback mechanism where heat signatures from executed operations are continuously monitored and fed back to the rate controller. This feedback loop allows the system to learn from actual thermal impacts and adjust future operation rates accordingly, optimizing the balance between heat management and throughput by making informed decisions based on real thermal conditions rather than static constraints
Solution Approach 2:
The system performs preliminary actions by pre-calculating and associating heat price tags with different types of data processing operations before execution. This allows the rate controller to predict thermal impacts in advance and make proactive rate control decisions, preventing thermal overload before it occurs while minimizing unnecessary throttling of operations that would stay within budget
Data Source
AI summary
In one embodiment, individual or groups of heat generating data processing operations are rate-controlled such that a component, a set of components, a board or line card, and/or an entire apparatus or any portion thereof stays within a corresponding heat budget. One or more heat price tags are associated with these data processing operations which are used to determine whether or not a corresponding data processing operation can be currently performed within one or more corresponding heat budgets. If so, the data procession operation proceeds. If not, the data processing operation is delayed. Examples of such data processing operations include, but are not limited to, data retrieval from memory, data storage in memory, lookup operations in memory, lookup operations in a binary or ternary content-addressable memory, regular expression processing, cryptographic processing, or data manipulation.