A Collaborative Artificial Intelligence as a Service Composition Dataset

2026-09-26

AI Summary

This study addresses the lack of comprehensive datasets encompassing service descriptions, requests, and compositions in collaborative AI-as-a-Service (AIaaS) research, as well as the limitations of traditional sequential workflow approaches. To this end, we construct the first full-element dataset tailored for collaborative AIaaS, comprising 25,900 services and 10,000 requests. Furthermore, we propose a multi-armed bandit (MAB)-based service composition algorithm that incorporates a pre-evaluation mechanism to quantify service composability. Experimental results validate the effectiveness of the proposed dataset across service recommendation, selection, and quality-of-service (QoS) prediction tasks, while demonstrating that the MAB-based approach successfully overcomes the constraints of conventional composition paradigms. The associated code has been made publicly available.

Abstract

Artificial Intelligence as a Service (AIaaS) composition is an emerging research area that enables clients to combine multiple AI services to meet complex requirements. A recent extension of this paradigm is collaborative AIaaS composition, where multiple AI services are combined to create a unified solution. Research in this field requires datasets with service descriptions, composition requests, and corresponding composition solutions. However, no dataset contains all this information specifically for collaborative AIaaS composition. Existing datasets target traditional web services or sequential AI workflows and lack the AI-specific attributes required for realistic collaborative composition. To address these challenges, we present a collaborative AIaaS composition dataset containing 25,900 AIaaS services from multiple providers across 12 AI task families, along with 10,000 collaborative service requests. We further develop a Multi-Armed Bandit (MAB)-based collaborative composition algorithm that determines the composability of candidate service combinations prior to composition. Experimental results demonstrate that the dataset supports realistic collaborative AIaaS composition evaluation and related research in service recommendation, selection and QoS prediction. The dataset and implementation are publicly available at: https://github.com/deepakkanneganti9/CAIaaS

Problem

• Artificial Intelligence as a Service
• Collaborative AIaaS Composition
• Dataset
• Service Composition

Innovation

• Artificial Intelligence as a Service
• Collaborative Composition
• Dataset
• Multi-Armed Bandit
• Service Composability