CROWDio: A Practical Mobile Crowd Computing Framework with Developer-Oriented Design, Adaptive Scheduling, and Fault Resilience

TL;DR

CROWDio is a comprehensive mobile crowd computing framework that simplifies development, ensures fault tolerance, and optimizes scheduling across heterogeneous devices, demonstrated by significant performance improvements.

Contribution

It introduces a developer-oriented, adaptive, and fault-resilient MCdC platform with a declarative SDK, tiered checkpointing, and flexible scheduling driven by device telemetry.

Findings

Adaptive scheduling reduces execution time by up to 56.9%.

Checkpointing overhead remains low at 2-3 seconds per task.

Workload distribution remains fair with a Jain's Index of 0.889.

Abstract

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.