SLA-Awareness for AI-assisted coding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909552145858560 |
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| author | Thangarajah, Kishanthan Leung, Arthur Chen, Boyuan Hassan, Ahmed E. |
| author_facet | Thangarajah, Kishanthan Leung, Arthur Chen, Boyuan Hassan, Ahmed E. |
| contents | The integration of AI-assisted coding tools within development environments drastically reduces development time, and allows developers to focus more on creative and critical aspects of software engineering through the use of Code Large Language Models (CodeLLMs). These coding assistants automate repetitive and time-consuming coding tasks such as code generation, code completion, code summarization, and code translation. Responsiveness is a crucial requirement of these coding assistants to maintain real-time interactivity, such that their use does not impede the developers' workflows. Different coding tasks have unique characteristics and latency requirements: Time-To-First-Token (TTFT) latency is essential for code completion tasks, while End-To-End (E2E) latency is crucial for code translation tasks. Managing these varying requirements simultaneously while optimizing resource usage poses significant challenges. Existing work adopts the Model-as-a-Service paradigm for serving individual CodeLLMs, but cannot effectively manage latency requirements of concurrent coding tasks and sequences of CodeLLM inference calls, due to a lack of end-to-end latency awareness. Another challenge is keeping resource utilization high, when the serving system is deployed on a shared cluster environment. To address these challenges, we propose Coding Assistant Task Orchestrator (CATO), a runtime system designed to serve a diverse assortment of coding tasks while meeting latency requirements and maximizing resource utilization. Our experiments demonstrate that when all types of coding tasks were served simultaneously, for TTFT-critical tasks, CATO improves overall Goodput rate and resource utilization by up to 10% and 41.1%, respectively. P95 E2E latency was also reduced by 18% for code summarization tasks, and P95 TTFT for code generation tasks were reduced by 14% compared against state-of-the-art systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SLA-Awareness for AI-assisted coding Thangarajah, Kishanthan Leung, Arthur Chen, Boyuan Hassan, Ahmed E. Software Engineering The integration of AI-assisted coding tools within development environments drastically reduces development time, and allows developers to focus more on creative and critical aspects of software engineering through the use of Code Large Language Models (CodeLLMs). These coding assistants automate repetitive and time-consuming coding tasks such as code generation, code completion, code summarization, and code translation. Responsiveness is a crucial requirement of these coding assistants to maintain real-time interactivity, such that their use does not impede the developers' workflows. Different coding tasks have unique characteristics and latency requirements: Time-To-First-Token (TTFT) latency is essential for code completion tasks, while End-To-End (E2E) latency is crucial for code translation tasks. Managing these varying requirements simultaneously while optimizing resource usage poses significant challenges. Existing work adopts the Model-as-a-Service paradigm for serving individual CodeLLMs, but cannot effectively manage latency requirements of concurrent coding tasks and sequences of CodeLLM inference calls, due to a lack of end-to-end latency awareness. Another challenge is keeping resource utilization high, when the serving system is deployed on a shared cluster environment. To address these challenges, we propose Coding Assistant Task Orchestrator (CATO), a runtime system designed to serve a diverse assortment of coding tasks while meeting latency requirements and maximizing resource utilization. Our experiments demonstrate that when all types of coding tasks were served simultaneously, for TTFT-critical tasks, CATO improves overall Goodput rate and resource utilization by up to 10% and 41.1%, respectively. P95 E2E latency was also reduced by 18% for code summarization tasks, and P95 TTFT for code generation tasks were reduced by 14% compared against state-of-the-art systems. |
| title | SLA-Awareness for AI-assisted coding |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.19876 |