[Paper] An Auction-Based Mechanism for Optimal Task Allocation and Resource Aware Containerization

Source: arXiv - 2602.11998v1

Overview

The paper introduces AUC‑RAC, an auction‑driven framework that lets a fleet of edge servers share Docker‑swarm resources to execute heavy IoT workloads. By turning the container‑placement decision into a market‑style bidding process, the system aims to cut latency and cost while keeping each node’s resource limits in mind.

Key Contributions

• Auction‑based task allocation – formulates container placement as a sealed‑bid auction where worker nodes bid based on current CPU, memory, and network availability.
• Docker‑Swarm integration – builds on the native manager/worker model, requiring no custom orchestration layer beyond a lightweight bidding API.
• Resource‑aware bidding logic – each worker computes a utility score that reflects both its residual capacity and the expected execution cost for the incoming task.
• Experimental validation – shows measurable reductions in task‑completion time and offloading cost compared with naïve round‑robin or static placement strategies.
• IoT‑centric scenario – targets the “fog‑to‑edge” continuum, where many low‑power devices rely on nearby servers for compute‑intensive inference or analytics.

Methodology

1. System Model – An IoT gateway forwards a JSON‑encoded task description (required CPU cycles, memory, deadline) to the Docker‑Swarm manager node (MN).
2. Bid Generation – Each worker node (WN) runs a lightweight daemon that:
   • Reads its current resource metrics (cAdvisor‑style stats).
   • Calculates a bid = f(available resources, estimated execution time, energy cost).
3. Auction Process – The MN collects all bids within a short time window (e.g., 100 ms) and selects the lowest‑cost bid that still satisfies the task’s QoS constraints.
4. Container Deployment – The chosen WN receives a Docker‑run command with the appropriate image and resource limits, launching the task inside an isolated container.
5. Feedback Loop – After completion, the WN reports execution metrics back to the MN, which updates its historical cost model for future auctions.

The authors implemented the prototype on a cluster of Raspberry Pi 4s and a couple of Intel NUCs, using standard Docker‑Swarm commands and a custom Python‑based bidding service.

Results & Findings

The data indicate that the auction mechanism consistently picks the “best‑fit” worker, especially under bursty load where resources are unevenly distributed. The modest extra scheduling latency is outweighed by the gains in execution speed and energy efficiency.

Practical Implications

• Edge‑first AI pipelines – Developers can plug AUC‑RAC into existing Docker‑Swarm clusters to automatically balance inference jobs (e.g., object detection) across heterogeneous edge hardware.
• Cost‑aware serverless – The bidding model can be extended to incorporate monetary pricing (e.g., spot‑instance rates) for hybrid cloud‑edge deployments.
• Scalable IoT gateways – By offloading the placement logic to the manager, IoT devices remain thin; they only need to push tasks, not manage resources.
• Plug‑and‑play integration – Since the approach uses standard Docker APIs, teams can adopt it without rewriting container images or orchestration scripts.
• Energy‑conscious deployments – Facilities aiming for greener operations can weight bids by power draw, letting the system favor low‑energy nodes when possible.

Limitations & Future Work

• Bid latency sensitivity – The current prototype assumes a fast LAN; in wider edge networks the auction window may need adaptation to avoid stale bids.
• Static utility function – The bidding formula is hand‑crafted; learning‑based models could better capture complex performance‑energy trade‑offs.
• Security considerations – The paper does not address authentication of bids or protection against malicious workers that could submit artificially low bids.
• Scalability beyond tens of nodes – Experiments were limited to < 20 workers; future work should evaluate the mechanism in larger fog clusters and with multi‑manager Swarm setups.

Overall, AUC‑RAC offers a compelling, developer‑friendly way to make edge container orchestration more intelligent and cost‑effective, paving the way for richer, real‑time IoT applications.

Authors

• Ramakant kumar

Paper Information

• arXiv ID: 2602.11998v1
• Categories: cs.DC, cs.NI
• Published: February 12, 2026
• PDF: Download PDF