\section{Real-World Applications of GMPMW}\label{sec:A.A}

GMPMW has real-world applications in various areas. The following are a few examples.

\paragraph{Cloud Computing Task Processing} A cloud provider offers a machine learning (ML) inference service. It receives ML task requests, where different ML models produce different accuracies and require different amounts of energy. 
The cloud provider needs to decide which task to accept or reject, and if so, which model to use to process each task, in order to maximize the accumulated accuracy. In the experiment section of the paper (and Appendix~\ref{sec:A.D.2}), this scenario is used as a case study to evaluate our proposed algorithm. 


\paragraph{Labor Outsourcing} A labor outsourcing company receives job requests, and different workers can be assigned to the job. Different workers generate different profits and require different hourly wages, while the total wage is budgeted.
The company decides which job to accept or reject, and which worker to assign to which task, in order to maximize the total profit. 

\paragraph{Online Ad Placement} An online ad service provider receives ad placement requests, and different types of ads can be placed for each request. Different types of ads generate different interaction rates and consume different amounts of ad time, while the total amount of ad time is budgeted. 
The service provider decides which request to accept, and if so, which type of ad to serve, in order to maximize the overall interaction rate. In this scenario, the maximum total ad time is the limited resource, and the different types of ads are different workers.

