Incentive-Based Coded Distributed Computing for IoT Latency
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Solution Overview
Problem
Distributed computing in IoT services faces challenges in reducing latency due to stragglers and motivating self-interested computing nodes, particularly in heterogeneous environments where resource limitations and network conditions vary, and existing solutions do not effectively balance latency reduction with additional workload incurred by coding schemes.
Innovation Solution
A coded distributed computing system that employs game-theoretic and coding theoretic approaches to manage latency by using a parameter server to determine optimal stragglers and load allocations, and incentivizes end devices with time-discounting values and workload-based incentives to balance computing speed and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If coding schemes are introduced to mitigate the straggling effect, then latency of distributed computing is reduced, but additional data processing overhead and system complexity are incurred
Solution Approach 1:
The system segments computing tasks into multiple subtasks and distributes them to different computing nodes. Coding schemes are applied to create redundant encodings of these segmented tasks, allowing the system to recover results from a subset of completed nodes rather than waiting for all nodes, thereby reducing latency while managing complexity through structured task division
Solution Approach 2:
A parameter server acts as an intermediary that introduces coding schemes to manage the straggling effect. The parameter server encodes task assignments and aggregates results using coding theoretic approaches, serving as a mediator that reduces overall system latency while centralizing the complexity management function
2Reliability
If self-interested computing nodes are motivated to participate, then system reliability is improved, but additional incentive mechanisms and coordination overhead are required
Solution Approach 1:
The system implements feedback mechanisms where computing nodes receive incentives based on their participation and performance. The parameter server monitors node contributions and distributes rewards accordingly, creating a feedback loop that motivates self-interested nodes to participate reliably while the centralized coordination manages the complexity of incentive distribution
Solution Approach 2:
The incentive mechanism is designed to be dynamic, adapting to the behavior and performance of computing nodes. Nodes can change their participation strategies based on observed incentives and system state, allowing the system to maintain reliability through adaptive coordination rather than rigid protocols
3Productivity
If heterogeneous computing nodes are utilized to increase resource availability, then productivity is improved, but latency variation and management difficulty increase
Solution Approach 1:
The system applies local quality by assigning different coding schemes and task configurations to different computing nodes based on their specific capabilities and conditions. Each heterogeneous node receives customized task encodings and resource allocations that match its local characteristics, allowing high resource utilization while managing latency variation through node-specific optimization
Solution Approach 2:
The parameter server dynamically changes task parameters and coding configurations based on the observed state of heterogeneous computing nodes. By adjusting task assignments, encoding rates, and aggregation strategies according to node performance and availability, the system maintains high productivity while reducing latency variation across the heterogeneous infrastructure
Data Source
AI summary
A coding and Incentive-based distributed computing management system includes: a parameter server that publishes a gradient descent computation task to update parameters of distributed computing, sends the published task to end devices, and groups end devices into clusters by receiving related information from the plurality of end devices, determines the number of stragglers in each cluster and sends the number of stragglers to the end devices, along with an encoding scheme for gradient descent computation, and distributes incentive to the end devices based on encoded results and the end devices that receive the published task from the parameter server, sends an intention to participate in the published task and related information to the parameter server, determines CPU-cycle frequencies by receiving information on the grouping of the end devices and related information from the parameter server, and performs encoding for gradient descent computation and send a computed gradient to the parameter server.