Microservice Prefetch Permutation Optimization for Latency SLOs
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Solution Overview
Problem
Microservice computing systems face challenges in optimizing prefetching to meet Service Level Objectives (SLO) for latency while minimizing resource usage and costs, particularly in serverless environments where cold-start latencies can significantly impact response times.
Innovation Solution
A method and system that determine optimal microservice prefetch permutations by calculating latency scores and eliminating permutations that do not meet SLO requirements, while selecting the most cost-effective permutations based on startup and compute times weighted by probability of occurrence, to balance latency and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If microservices are prefetched to reduce cold-start latency, then response time is improved, but resource consumption and cost increase
Solution Approach 1:
The system performs preliminary actions by calculating and storing optimal prefetch permutations in advance, rather than reacting to each request individually. The latency score calculations and permutation determinations are performed proactively so that when requests arrive, the optimal prefetch strategy is already ready to be executed immediately.
Solution Approach 2:
The system dynamically adjusts prefetch strategies based on real-time request characteristics and historical data. The latency scores are recalculated based on current service performance metrics, and the optimal permutations are determined dynamically for each request type, allowing the system to adapt to changing conditions while minimizing resource waste.
2Reliability
If multiple prefetch permutations are evaluated to meet SLO requirements, then latency reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex prefetch optimization problem into manageable components: calculating latency scores for individual permutations, evaluating SLO compliance for each permutation, and determining optimal permutations based on cost criteria. This segmentation allows complex reliability requirements to be met through systematic evaluation of discrete options rather than monolithic analysis.
Solution Approach 2:
The system incorporates feedback mechanisms where historical request data and service performance metrics are used to refine latency score calculations. The SLO compliance evaluation provides feedback that guides the selection of optimal permutations, creating a closed-loop system that continuously improves its prefetch strategy based on actual performance data.
3Measurement precision
If all possible microservice sequences are considered, then prefetch accuracy is improved, but computation time increases
Solution Approach 1:
The system applies partial action by evaluating only the necessary subset of permutations rather than exhaustively analyzing all possible microservice sequences. By calculating latency scores and identifying optimal permutations through targeted evaluation, the system achieves sufficient prefetch accuracy without the computational burden of complete enumeration.
Data Source
AI summary
Prefetch of microservices for incoming requests. The method determines for an incoming request a Service Level Objective (SLO) requirement for latency of a request type of the incoming request. The method generates a set of possible microservice sequences for the request including a probability of occurrence of each of the possible microservice sequences and determines a set of prefetch permutations for the set of possible microservice sequences. A latency score is calculated for each prefetch permutation and any prefetch permutations that do not meet the SLO requirement for latency of the request type are eliminated. An optimal prefetch permutation of the remaining prefetch permutations is selected by considering a total cost of the prefetch permutation based on a cost of running each microservice in the set of sequences.