arXiv subjects

Mehran Salmani

Publications and source records attributed to Mehran Salmani.

3 recordsLinked to original sources

arXiv2024

A Tale of Two Scales: Reconciling Horizontal and Vertical Scaling for Inference Serving Systems

Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. However, managing resources in these systems poses significant challenges, particularly in maintaining performance under varying and unpredictable workloads. Two primary scaling strategies, horizontal and vertical scaling, offer different advantages and limitations. Horizontal scaling adds more instances to handle increased loads but can suffer from cold start issues and increased management complexity. Vertical scaling boosts the capacity of existing instances, allowing for quicker responses but is limited by hardware and model parallelization capabilities. This paper introduces Themis, a system designed to leverage the benefits of both horizontal and vertical scaling in inference serving systems. Themis employs a two-stage autoscaling strategy: initially using in-place vertical scaling to handle workload surges and then switching to horizontal scaling to optimize resource efficiency once the workload stabilizes. The system profiles the processing latency of deep learning models, calculates queuing delays, and employs different dynamic programming algorithms to solve the joint horizontal and vertical scaling problem optimally based on the workload situation. Extensive evaluations with real-world workload traces demonstrate over $10\times$ SLO violation reduction compared to the state-of-the-art horizontal or vertical autoscaling approaches while maintaining resource efficiency when the workload is stable.

cs.DC↗

arXiv2023

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency

Efficiently optimizing multi-model inference pipelines for fast, accurate, and cost-effective inference is a crucial challenge in machine learning production systems, given their tight end-to-end latency requirements. To simplify the exploration of the vast and intricate trade-off space of latency, accuracy, and cost in inference pipelines, providers frequently opt to consider one of them. However, the challenge lies in reconciling latency, accuracy, and cost trade-offs. To address this challenge and propose a solution to efficiently manage model variants in inference pipelines, we present IPA, an online deep learning Inference Pipeline Adaptation system that efficiently leverages model variants for each deep learning task. Model variants are different versions of pre-trained models for the same deep learning task with variations in resource requirements, latency, and accuracy. IPA dynamically configures batch size, replication, and model variants to optimize accuracy, minimize costs, and meet user-defined latency Service Level Agreements (SLAs) using Integer Programming. It supports multi-objective settings for achieving different trade-offs between accuracy and cost objectives while remaining adaptable to varying workloads and dynamic traffic patterns. Navigating a wider variety of configurations allows \namex{} to achieve better trade-offs between cost and accuracy objectives compared to existing methods. Extensive experiments in a Kubernetes implementation with five real-world inference pipelines demonstrate that IPA improves end-to-end accuracy by up to 21% with a minimal cost increase. The code and data for replications are available at https://github.com/reconfigurable-ml-pipeline/ipa.

cs.DC↗

arXiv2023

Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems

The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses. Moreover, these services face dynamic workloads of requests, imposing changes in their computing resources. Failing to right-size computing resources results in either latency service level objectives (SLOs) violations or wasted computing resources. Adapting to dynamic workloads considering all the pillars of accuracy, latency, and resource cost is challenging. In response to these challenges, we propose InfAdapter, which proactively selects a set of ML model variants with their resource allocations to meet latency SLO while maximizing an objective function composed of accuracy and cost. InfAdapter decreases SLO violation and costs up to 65% and 33%, respectively, compared to a popular industry autoscaler (Kubernetes Vertical Pod Autoscaler).

cs.LG↗