Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Nowadays large-scale distributed machine learning systems have been deployed to support
various analytics and intelligence services in IT firms. To train a large dataset and derive the …

The continuous shift towards data-driven approaches to business, and a growing attention to
improving return on investments (ROI) for cluster infrastructures is generating new …

We introduce and study a general version of the fractional online knapsack problem with
multiple knapsacks, heterogeneous constraints on which items can be assigned to which …

This paper studies the cloud market for computing jobs with completion deadlines, and
designs efficient online auctions for cloud resource provisioning. A cloud user bids for future …

To enable machine learning at the edge of wireless networks (such as edge cloud), close to
mobile users, is critical for future wireless networks, but challenging since the lower layers in …

The rapid proliferation of electric vehicles has resulted in a drastic increase in the total
energy demand of EVs. Given the limited charging rate capacity of charging stations and …

The rapid advancement and extensive implementation of Large Language Models (LLMs)
are milestones in the realm of artificial intelligence. Although Parameter-Efficient Transfer …

We consider a market-based resource allocation model for batch jobs in cloud computing
clusters. In our model, we incorporate the importance of the due date of a job rather than the …

The cloud computing industry has grown rapidly over the last decade, and with this growth
there is a significant increase in demand for compute resources. Demand is manifested in …