Smart Contract-based Resource Allocation and Fairness in Edge Computing Environments Using Blockchain and AI Optimizers

Project Abstract

This study presents a comprehensive framework that integrates smart contracts, blockchain, and AI-driven optimization to tackle resource allocation and fairness in edge computing environments. The proposed approach leverages decentralized ledgers to enforce policy-compliant scheduling, resource accounting, and trustless collaboration among heterogeneous edge nodes, users, and service providers. A multi-layer architecture is introduced, featuring (i) an edge orchestration plane that models workloads as stochastic demand processes, (ii) a blockchain-based governance layer that records resource claims, payments, and QoS guarantees via immutable smart contracts, and (iii) an AI optimization engine that continuously learns from system state to allocate CPU, memory, storage, and network bandwidth while meeting latency, energy, and budget constraints. The abstract details the problem of heterogeneity, dynamicity, and scarcity in edge resources, where conventional centralized schedulers struggle to maintain fairness and QoS across diverse tenants. Our solution employs a market-inspired resource allocation mechanism in which smart contracts implement permissioned auctions, priority queues, and security constraints, ensuring transparent and auditable decisions. The AI optimizers combine reinforcement learning, multi-objective optimization, and federated learning to predict demand, adapt to network topology changes, and maximize a fairness-aware utility function. Fairness is operationalized through metrics such as envy-freeness, proportional fairness, and equitable SLA adherence, and is integrated into the reward structure of the learning process. The methodology includes (i) formalizing the resource model and QoS requirements, (ii) designing a modular smart contract suite that handles autonomic admission control, dynamic pricing, service-level commitments, and revocation, (iii) constructing a permissioned blockchain protocol with efficient consensus to minimize overhead, (iv) developing an AI optimizer that jointly handles placement, scaling, and migration decisions with information hiding and privacy-preserving techniques, (v) implementing a simulation environment and a real-world testbed using containerized workloads and edge devices, and (vi) evaluating performance under varying load patterns, topology changes, and adversarial scenarios. The evaluation framework compares the proposed framework against baseline schedulers and blockchain-augmented resource managers on metrics including throughput, latency, energy consumption, cost, fairness indices, contract execution latency, and resilience to faults or attacks. Preliminary results indicate that the combination of smart contracts with blockchain-backed provenance and AI-driven allocation can significantly improve fairness without sacrificing efficiency. The framework demonstrates faster convergence to balanced resource distribution, reduced SLA violations, and stronger traceability of decisions due to immutable contract logs. The study also discusses scalability considerations, such as shard-based or sidechain architectures to mitigate consensus bottlenecks, and privacy-preserving techniques to protect sensitive workload information. Potential limitations related to deployment overhead, governance complexity, and the need for standardized interfaces are identified, along with strategies to mitigate them. The research contributes a novel, integrative approach for equitable resource management in edge ecosystems and lays the groundwork for resilient, auditable, and autonomous edge services.

Project Overview

What This Project Is About

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A simple, beginner-friendly overview of how smart contracts can help manage computing tasks in networks of edge devices, with automated decision-making to allocate resources fairly.

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The Problem It Addresses

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Edge computing involves many devices with limited power and varying workloads. Without a reliable system, resources like CPU time and memory can be unfairly distributed, causing delays or wasted capacity. This project tackles fair, transparent resource allocation in such environments.

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Objectives of the Project

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1. Explain the basic concepts of edge computing, blockchain, and smart contracts in simple terms.
2. Describe how resource allocation can be automated and made fair using smart contracts.
3. Introduce a lightweight model or prototype that demonstrates resource sharing between edge devices.
4. Discuss potential benefits, limitations, and real-world use cases.

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What You Will Do Step by Step

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1) Learn the core ideas and define key terms in plain language. 2) Outline a basic architecture that links edge devices, a blockchain layer, and automated rules. 3) Create a simple smart contract example that allocates resources fairly. 4) Simulate scenarios (e.g., varying workloads) to show how the system behaves. 5) Analyze outcomes and identify strengths and weaknesses.

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Expected Outcome

A clear, easy-to-understand plan and a simple prototype or workflow that demonstrates fair resource sharing in edge networks, along with a discussion of potential improvements and future work.