Edge Caching via Karma Scores for Consumer Hardware
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Implementing network appliances and content delivery networks (CDNs) near end-user devices is hindered by the high cost and resource-intensive requirements of computationally powerful devices needed for complex caching algorithms, making it difficult to deploy these systems at extreme network edges.
Innovation Solution
A caching system at the network edge that uses a caching algorithm to determine dynamic cache priorities based on temporal, karma, and access frequency components, allowing for efficient culling of content to maintain cache capacity, and can be implemented using consumer-grade computers with minimal computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If network appliances and CDNs are implemented near end-user devices to reduce latency and network congestion, then network communication speed is improved, but the cost and resource requirements increase significantly due to the need for computationally powerful devices
Solution Approach 1:
The patent replaces expensive, high-complexity computing systems with consumer-grade computers that have minimal computational resources. Instead of deploying powerful network appliances at every edge location, the invention uses affordable devices with limited caching capabilities, accepting that individual caches may be smaller and less sophisticated.
Solution Approach 2:
The patent divides the caching function into multiple segments distributed across many consumer-grade devices rather than concentrating caching power in few powerful systems. Each device maintains its own local cache, and the system collectively provides caching coverage through aggregation of many small caches across the network.
2Ease of manufacture
If consumer-grade computers with minimal computational resources are used for caching, then deployment cost is reduced, but cache effectiveness may be compromised due to limited processing power and memory
Solution Approach 1:
The patent combines multiple small caches into a coordinated caching system where consumer-grade devices work together as a distributed cache network. By merging the caching capabilities of many devices and coordinating their behavior through the system architecture, the collective effectiveness matches or exceeds that of individual powerful caches.
Solution Approach 2:
The patent enables consumer-grade computers to perform caching functions they were not originally designed for. These devices serve multiple purposes: general computing, local caching, and participation in the distributed caching network, maximizing utility of affordable hardware.
3Productivity
If complex caching algorithms are implemented to optimize cache hit rates, then content delivery efficiency is improved, but the computational overhead and system complexity increase
Solution Approach 1:
The patent extracts the complex caching algorithm logic from the edge devices and relocates it to a centralized or semi-centralized control system. Consumer-grade devices execute simpler caching decisions based on guidance from the external system, which performs the computationally intensive algorithmic work remotely.
Solution Approach 2:
The patent introduces an intermediary caching coordination system that mediates between the content delivery network and consumer-grade devices. This intermediary handles complex caching decisions, priority management, and coordination, allowing simple devices to participate in sophisticated caching strategies without bearing the computational burden.
Data Source
AI summary
Several embodiments include a data cache system that implements a data cache and processes content requests for data items that may be in the data cache. The data cache system can receive a content request for at least one data item. The data cache system can update a karma score associated an originator entity of the data item. The originator entity can be a user account that uploaded the data item. When wiping the data cache for more storage space, the data cache system can determine whether to discard the data items based on a cache priority that is computed based, at least partially, on the karma score.