Hybrid Cache Architecture for Large AV Simulation Objects
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Solution Overview
Problem
Autonomous vehicle (AV) simulation systems face challenges with high network bandwidth demands and potential SLA violations due to heavy cloud access, necessitating an efficient caching solution to reduce backend access and optimize data storage.
Innovation Solution
A hybrid cache system combining a consistent hash cache and a hot cache, with the latter having a two-tier structure, is implemented to manage large data objects like drive bags, optimizing storage and access times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single-tier cache is used, then the system is simpler, but it cannot simultaneously optimize for high-speed access and large storage capacity
Solution Approach 1:
The cache is divided into two distinct tiers: a first tier with high-speed storage nodes for fast access to frequently used data, and a second tier with capacity-optimized storage nodes for storing less frequently accessed data. This segmentation allows the system to simultaneously achieve high access speeds for hot data while maintaining large overall storage capacity, resolving the contradiction between speed and complexity.
2Reliability
If data is replicated across multiple nodes, then access reliability improves, but storage efficiency decreases
Solution Approach 1:
Different replication strategies are applied to different tiers based on their specific requirements. The first tier uses replication across multiple high-speed nodes to ensure high access reliability and availability for frequently accessed data. The second tier uses more efficient storage methods with controlled replication to maintain data durability while maximizing storage capacity. This localized application of replication strategies optimizes both reliability and storage efficiency for each tier.
3Loss of time
If all data is stored in high-speed storage, then access time is reduced, but the system cannot handle diverse workload demands
Solution Approach 1:
The system dynamically adapts to different workload demands by intelligently distributing data between the two tiers based on access patterns and workload characteristics. The load balancer continuously monitors and adjusts data placement, moving frequently accessed data to the high-speed first tier and less frequently accessed data to the capacity-optimized second tier. This dynamic adaptation allows the system to efficiently handle diverse workload demands while optimizing access times for different data types.
Data Source
AI summary
A method of caching large data objects of greater than 1 GB, comprising: populating a sharded cache with large data objects backfilled from a data store; servicing large data object requests from a plurality of worker nodes via the sharded cache, comprising deterministically addressing objects within the sharded cache; and if a number of requests for an object within a time exceeds a threshold: after receiving a request from a worker node for the object, sending the worker node a redirect message directed to a hot cache, wherein the hot cache is to backfill from a hot cache backfill, and wherein the hot cache backfill is to backfill from the sharded cache.