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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.19363 |
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| _version_ | 1866914494874124288 |
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| author | Manamperi, Lakshani Pathirana, Disumi Pathirana, Thiwanka Premarathna, Nipun Gunasekara, Kutila |
| author_facet | Manamperi, Lakshani Pathirana, Disumi Pathirana, Thiwanka Premarathna, Nipun Gunasekara, Kutila |
| contents | Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented frameworks capable of transparently managing device heterogeneity, fault tolerance, and connectivity volatility. This paper introduces CROWDio, a centralized MCdC platform comprising three tightly integrated subsystems: (i) a declarative SDK that abstracts distributed execution to a single function annotation, eliminating the need for explicit parallelism management; (ii) a tiered checkpointing mechanism that enables fault-tolerant task resumption under the memory and execution constraints inherent to mobile runtimes; and (iii) a pluggable multi-criteria scheduling framework driven by continuous live device telemetry, supporting interchangeable decision strategies without modification to the dispatch core. Empirical evaluation across six heterogeneous Android devices spanning CPU-bound, AI/NLP inference, and data-parallel workloads demonstrates that capability-aware adaptive scheduling reduces total execution time by up to 56.9% relative to naive round-robin dispatch, while the checkpointing subsystem incurs a bounded overhead of only 2-3 s per task regardless of checkpoint frequency. A system-wide Jain's Fairness Index of 0.889 confirms equitable and stable workload distribution across heterogeneous worker devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19363 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CROWDio: A Practical Mobile Crowd Computing Framework with Developer-Oriented Design, Adaptive Scheduling, and Fault Resilience Manamperi, Lakshani Pathirana, Disumi Pathirana, Thiwanka Premarathna, Nipun Gunasekara, Kutila Distributed, Parallel, and Cluster Computing Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented frameworks capable of transparently managing device heterogeneity, fault tolerance, and connectivity volatility. This paper introduces CROWDio, a centralized MCdC platform comprising three tightly integrated subsystems: (i) a declarative SDK that abstracts distributed execution to a single function annotation, eliminating the need for explicit parallelism management; (ii) a tiered checkpointing mechanism that enables fault-tolerant task resumption under the memory and execution constraints inherent to mobile runtimes; and (iii) a pluggable multi-criteria scheduling framework driven by continuous live device telemetry, supporting interchangeable decision strategies without modification to the dispatch core. Empirical evaluation across six heterogeneous Android devices spanning CPU-bound, AI/NLP inference, and data-parallel workloads demonstrates that capability-aware adaptive scheduling reduces total execution time by up to 56.9% relative to naive round-robin dispatch, while the checkpointing subsystem incurs a bounded overhead of only 2-3 s per task regardless of checkpoint frequency. A system-wide Jain's Fairness Index of 0.889 confirms equitable and stable workload distribution across heterogeneous worker devices. |
| title | CROWDio: A Practical Mobile Crowd Computing Framework with Developer-Oriented Design, Adaptive Scheduling, and Fault Resilience |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.19363 |