Latency-Aware Serverless Scheduling for SLO-Sensitive Requests
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Solution Overview
Problem
Serverless platforms struggle to manage latency-sensitive requests effectively, leading to violations of Service-Level Objectives (SLO) due to inadequate consideration of SLO objectives and underlying hardware resource utilization, resulting in excessive queuing latencies and inefficient resource allocation.
Innovation Solution
A latency-aware-based serverless request scheduling apparatus and system that includes a first scheduling module and a second scheduling module to optimize pod and node selection based on SLO conditions, historical processing times, and resource utilization, ensuring efficient allocation and reducing SLO violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional serverless scheduling is used, then resource allocation is simple, but SLO violation rate increases due to inadequate consideration of latency-sensitive requests
Solution Approach 1:
The scheduling system is divided into two independent modules: a first scheduling module for pod-level scheduling and a second scheduling module for node-level scheduling. Each module operates with its own scheduling policies and criteria, allowing complex SLO-aware scheduling to be broken down into manageable segments that can be implemented and maintained independently.
Solution Approach 2:
The system performs preliminary SLO assessment when user requests are received, evaluating whether the request can meet SLO requirements before actual execution. This advance evaluation allows the system to identify latency-sensitive requests early and apply appropriate scheduling strategies proactively, rather than reactively after SLO violations occur.
2Speed
If pod creation is performed without node selection optimization, then resource allocation is fast, but request processing latency increases
Solution Approach 1:
The second scheduling module performs preliminary node selection based on processing capability indicators before pod creation. By pre-identifying suitable nodes with strong processing capabilities, the system minimizes the time pods spend in queuing states and reduces overall request processing latency.
Solution Approach 2:
The system continuously monitors node performance metrics and processing capabilities, using this feedback to dynamically adjust node selection decisions. This feedback mechanism ensures that pods are consistently allocated to nodes with the best current performance characteristics, optimizing request processing speed while adapting to changing system conditions.
3Productivity
If nodes are selected without considering processing capability, then resource allocation is uniform, but overall system performance decreases
Solution Approach 1:
The system applies differentiated node selection strategies based on local node characteristics and request requirements. Different nodes can be selected for different types of requests based on their specific processing capabilities, hardware resources, and current load conditions, optimizing overall system productivity through localized quality matching.
Solution Approach 2:
The system dynamically changes scheduling parameters such as node selection criteria and pod allocation strategies based on real-time system conditions and request characteristics. This parameter adaptation allows the system to optimize productivity for different workloads and system states without requiring a completely different scheduling approach for each scenario.
Data Source
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AI summary
This application relates to a latency-aware-based serverless request scheduling apparatus and system. The apparatus includes: a first scheduling module that determines a determined user request at a queue head of a current request queue as a target user request, and sends a pod creation request to a second scheduling module when determining that a target pod that meets an execution condition and that is to execute the target user request is absent; and the second scheduling module that, when determining, based on the pod creation request, that a new pod meets a node creation condition, selects a target node from a plurality of nodes, creates the new pod in the target node, and sends information about the new pod to the first scheduling module. The first scheduling module manages the new pod based on the received information about the new pod, determines the new pod as a target pod, and sends the target user request to the target pod for execution. In this way, node resources can be used more efficiently, and an SLO violation rate can be reduced.