MOGR: Multi-task Offloading via Graph Representation in Heterogeneous Computing Network

In the rapidly evolving field of heterogeneous computing networks, efficient task offloading plays a pivotal role in optimizing system throughput and resource utilization. However, existing task offloading methods often fall short of adequately modeling the dependency topology relationships between of-floaded tasks, which limits their effectiveness in capturing the complex interdependencies of task features. To address this limitation, we propose a framework named MOGR: Multi-task Offloading via Graph Representation. Our modeling approach takes into account factors such as task characteristics, network conditions, and available resources at the edge, and embeds these captured features into the graph structure. By utilizing Graph Convolutional Networks (GCN), our mechanism can capture and analyze the intricate relationships between task features, enabling a more comprehensive understanding of the underlying dependency topology. Through extensive evaluations in heteroge-neous networks, our proposed algorithm improves 15.1%-30.5% over greedy and approximate algorithms in optimizing system throughput and resource utilization. Our experiments showcase the advantage of considering the intricate interplay of task features using GCN-based modeling.