Chuanyi Li

TL;DR: A multi-objective scheduling (MOS) algorithm for scientific workflow in multicloud environment is proposed, the aim of which is to minimize workflow makespan and cost simultaneously while satisfying the reliability constraint.

TL;DR: This paper proposes an effective classification methodology by employing both project-specific and non-project-specific keywords and machine learning algorithms that does well in achieving high classification accuracy by using keywords as features, reducing considerable manual efforts in building machine learning based classifiers, and having stable performance in finding minority classes no matter how few instances they have.

TL;DR: Three pre-training tasks are introduced that are specifically designed to enable SPT-Code to learn knowledge of source code, the corresponding code structure, as well as a natural language description of the code without relying on any bilingual corpus, and eventually exploit these three sources of information when it is applied to downstream tasks.

TL;DR: The numerical results show that the proposed online learning offloading method for mobile users can derive the optimal offloading scheme compared with the baseline algorithms.

TL;DR: A load-aware resource allocation and task scheduling (LA-RATS) strategy which adaptively allocates resource in MCC system for delay-tolerant and delay-sensitive mobile applications according to cloudlet’s load profile is designed and a tree generation based task backfilling algorithm is proposed to raise the utilization of the cloudlet.