Deadline-Aware Resource Allocation and Scheduling of Serverless Workloads on Heterogeneous Clusters

Serverless computing has become widely adopted as a cloud deployment model due to its ease of use and finegrained pay-as-you-go pricing. By hiding infrastructure complexity, it simplifies access to cloud resources and lets developers focus on application code. However, most serverless platforms oper...

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Bibliographic Details
Published inProceedings / IEEE International Conference on Cluster Computing pp. 1 - 11
Main Authors Fritz, Matthias, Benkner, Siegfried, Bajrovic, Enes
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.09.2025
Subjects
Dynamic Resource Tuning
Dynamic scheduling
Heterogeneous Clusters
Manuals
Measurement
Optimization
Pricing
Processor scheduling
Real-time systems
Reliability
Resource Allocation
Scheduling
Serverless
Serverless computing
Tuning
Online AccessGet full text
ISSN2168-9253
DOI10.1109/CLUSTER59342.2025.11186486

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Summary:Serverless computing has become widely adopted as a cloud deployment model due to its ease of use and finegrained pay-as-you-go pricing. By hiding infrastructure complexity, it simplifies access to cloud resources and lets developers focus on application code. However, most serverless platforms operate on a best-effort basis and provide minimal control over performance tuning. Combined with limited visibility into underlying hardware, this makes it difficult to reliably meet Service Level Objectives (SLOs). To address this, we introduce DHRT, a deadline- and heterogeneity-aware scheduling and resource allocation framework for performance-critical serverless workloads. DHRT applies heuristic-driven online optimisation to iteratively refine resource estimates by leveraging real-time metrics and historical data from live executions. To fulfil SLOs, it accounts for both workload characteristics and node heterogeneity. We evaluate DHRT on synthetic workloads by comparing it against baseline scheduling and resource allocation policies commonly used in FaaS platforms. Results show that DHRT accurately estimates resource demands within a few live executions, eliminating the need for manual resource tuning. By exploiting node heterogeneity and dynamically scaling vCPU allocations as workloads near their deadlines, DHRT improves resource efficiency and significantly reduces deadline violations.
ISSN:2168-9253
DOI:10.1109/CLUSTER59342.2025.11186486