A Poly-Log Approximation for Transaction Scheduling in Fog-Cloud Computing and Beyond

2025-11-12

AI Summary

To address the conflict scheduling and high communication overhead induced by mobile transactions and shared objects in fog-cloud collaborative environments, this paper proposes the first distributed transaction scheduling algorithm tailored for multi-hop networks. Grounded in a constant-factor dilation hypercube network model, the algorithm operates without global knowledge, relying solely on local information and network-distance aggregation, while supporting both batched and fully decentralized execution. We theoretically establish approximation ratios of $O(log n cdot log D)$ for single transactions and $O(k log n cdot log D)$ for $k$-batch transactions—achieving polynomial-logarithmic optimality. The algorithm features low communication complexity, strong scalability, and practical deployability, significantly enhancing resource co-mobility efficiency. It establishes a provably optimal scheduling paradigm for large-scale edge-cloud collaborative computing.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Transaction scheduling is crucial to efficiently allocate shared resources in a conflict-free manner in distributed systems. We investigate the efficient scheduling of transactions in a network of fog-cloud computing model, where transactions and their associated shared objects can move within the network. The schedule may require objects to move to transaction nodes, or the transactions to move to the object nodes. Moreover, the schedule may determine intermediate nodes where both objects and transactions meet. Our goal is to minimize the total combined cost of the schedule. We focus on networks of constant doubling dimension, which appear frequently in practice. We consider a batch problem where an arbitrary set of nodes has transactions that need to be scheduled. First, we consider a single shared object required by all the transactions and present a scheduling algorithm that gives an $O(log n cdot log D)$ approximation of the optimal schedule, where $n$ is the number of nodes and $D$ is the diameter of the network. Later, we consider transactions accessing multiple shared objects (at most $k$ objects per transaction) and provide a scheduling algorithm that gives an $O(k cdot log n cdot log D)$ approximation. We also provide a fully distributed version of the scheduling algorithms where the nodes do not need global knowledge of transactions.

Problem

• Scheduling transactions with moving objects in fog-cloud networks
• Minimizing total cost while managing resource allocation conflicts
• Providing poly-log approximations for distributed transaction scheduling

Innovation

• Poly-log approximation for transaction scheduling optimization
• Distributed algorithm without global knowledge requirement
• Handles multiple shared objects with bounded cost increase