Sponge: Efficient Inference Serving with Dynamic SLO Guarantees Using In-Place Vertical Scaling

Core Concepts

Sponge maximizes resource efficiency while guaranteeing dynamic SLOs for deep learning inference serving by applying in-place vertical scaling, dynamic batching, and request reordering.

Abstract

Sponge is a novel deep learning inference serving system that addresses the challenge of dynamic Service Level Objectives (SLOs) at the request level. The key insights are:

Combining in-place vertical scaling, dynamic batching, and request reordering is a powerful approach to handle request-level dynamism caused by variable network conditions.

Sponge formulates the problem as an Integer Programming optimization to capture the relationship between latency, batch size, and resources, providing a mathematical model for efficient resource allocation.

Sponge's prototype implementation and preliminary experiments demonstrate its potential, reducing SLO violations by over 15x compared to a state-of-the-art horizontal autoscaler.

Sponge addresses the challenge of dynamic SLOs by:

Applying in-place vertical scaling to change the computing resources of DL models on-the-fly.
Using request reordering to prioritize requests with lower remaining time budgets.
Leveraging dynamic batching to increase system utilization and reduce resource requirements.
The paper provides a mathematical formulation of the problem and a solution algorithm to find the optimal CPU core allocation and batch size configuration that minimizes resources while guaranteeing all requests' SLOs.

Stats

The network bandwidth can fluctuate from 0.5 MB/s to 7 MB/s in a 10-minute range.
The maximum communication latency (cl_max) across all requests is 600 ms.

Quotes

"SLOs are comprehensively defined from end to end, with the variable network time required for transferring user requests and input data introducing dynamic time budgets for serving inference requests."
"Sponge relies on three adaptation strategies to capture per-request dynamic SLOs: 1) in-place vertical scaling to change the computing resources of DL models in spot, 2) request reordering to prioritize close-to-deadline requests, and 3) dynamic batching to increase the utilization of the DL models."

Key Insights Distilled From

Sponge

by Kamr... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00704.pdf

Deeper Inquiries

How can Sponge be extended to support model variant selection and switching to further improve accuracy, cost-efficiency, and latency trade-offs

To extend Sponge to support model variant selection and switching for improved accuracy, cost-efficiency, and latency trade-offs, several key enhancements can be implemented. Firstly, Sponge can incorporate a mechanism to evaluate the performance metrics of different model variants, such as accuracy, latency, and resource utilization. By dynamically assessing these metrics based on the current workload and SLO requirements, Sponge can intelligently select the most suitable model variant for each inference request.
Additionally, Sponge can implement a model switching strategy that allows for seamless transitions between different model variants based on real-time performance feedback. By monitoring the system's performance and adapting to changing conditions, Sponge can switch to a more optimal model variant to meet the desired SLOs while optimizing resource utilization.
Furthermore, Sponge can introduce a cost-efficiency component that considers the monetary cost associated with each model variant. By incorporating cost-aware decision-making algorithms, Sponge can select model variants that strike a balance between performance, cost, and resource efficiency.
By integrating these features, Sponge can enhance its capabilities to support model variant selection and switching, enabling it to achieve improved accuracy, cost-efficiency, and latency trade-offs in dynamic inference serving environments.

How can Sponge be generalized to handle pipelines of DL models with data dependencies, where scaling decisions for individual models need to be coordinated

Generalizing Sponge to handle pipelines of DL models with data dependencies requires a sophisticated approach to coordinate scaling decisions for individual models within the pipeline. One key challenge is managing the interdependencies between models, as scaling decisions for one model can impact the performance of downstream models. To address this, Sponge can implement a holistic optimization framework that considers the entire pipeline as a unified system.
Sponge can introduce a mechanism for modeling and analyzing the data flow and dependencies between DL models in the pipeline. By understanding the relationships between models and their resource requirements, Sponge can optimize scaling decisions to ensure smooth operation and efficient resource utilization across the pipeline.
Moreover, Sponge can implement dynamic coordination strategies that adjust scaling decisions based on the real-time performance of each model in the pipeline. By continuously monitoring the system and adapting to changing conditions, Sponge can dynamically allocate resources to different models to meet overall SLO requirements while optimizing the performance of the entire pipeline.
By addressing the challenges of data dependencies and coordination in pipeline scenarios, Sponge can be generalized to effectively handle complex inference serving environments with multiple interconnected DL models.

What are the potential challenges and opportunities in incorporating multi-dimensional scaling (both vertical and horizontal) to handle highly dynamic workloads that exceed the capacity of a single instance

Incorporating multi-dimensional scaling, including both vertical and horizontal scaling, to handle highly dynamic workloads that exceed the capacity of a single instance presents both challenges and opportunities for Sponge. One key challenge is managing the coordination between vertical and horizontal scaling decisions to ensure efficient resource utilization and performance across the system.
An opportunity lies in leveraging the strengths of vertical scaling for fine-grained resource adjustments within individual instances and horizontal scaling for scaling out to multiple instances to handle increased workloads. By combining these scaling approaches strategically, Sponge can dynamically adapt to fluctuating workloads and ensure optimal performance while minimizing resource waste.
Challenges may arise in synchronizing scaling decisions between vertical and horizontal scaling mechanisms, especially in scenarios with complex data dependencies and varying workload patterns. Ensuring seamless coordination and efficient resource allocation across multiple instances while maintaining performance and SLO requirements will be a key challenge for Sponge.
Overall, by effectively incorporating multi-dimensional scaling strategies, Sponge can enhance its scalability, flexibility, and performance in handling highly dynamic workloads that require a combination of vertical and horizontal scaling to meet the demands of modern inference serving systems.