An Auction-Based Mechanism for Optimal Task Allocation and Resource Aware Containerization
Ramakant kumar
TL;DR
The paper tackles efficient task offloading and resource management in IoT by introducing AUC-RAC, a novel auction-based mechanism. AUC-RAC couples Bayesian sealed-bid auctions for task distribution with resource-aware Docker containerization at execution, coordinating a Master Node and multiple Worker Nodes. It explicitly optimizes costs at both the task distribution and execution levels and demonstrates the benefits of Docker Swarm for locality, parallelism, and scalability. Experimental results indicate improved offloading efficiency, reduced delays, and enhanced profitability, supporting practical applicability in diverse IoT edge scenarios.
Abstract
Distributed computing has enabled cooperation between multiple computing devices for the simultaneous execution of resource-hungry tasks. Such execution also plays a pivotal role in the parallel execution of numerous tasks in the Internet of Things (IoT) environment. Leveraging the computing resources of multiple devices, the offloading and processing of computationintensive tasks can be carried out more efficiently. However, managing resources and optimizing costs remain challenging for successfully executing tasks in cloud-based containerization for IoT. This paper proposes AUC-RAC, an auction-based mechanism for efficient offloading of computation tasks among multiple local servers in the context of IoT devices. The approach leverages the concept of Docker swarm, which connects multiple local servers in the form of Manager Node (MN) and Worker Nodes (WNs). It uses Docker containerization to execute tasks simultaneously. In this system, IoT devices send tasks to the MN, which then sends the task details to all its WNs to participate in the auction-based bidding process. The auctionbased bidding process optimizes the allocation of computation tasks among multiple systems, considering their resource sufficiency. The experimental analysis establishes that the approach offers improved offloading and computation-intensive services for IoT devices by enabling cooperation between local servers.
