A survey on scheduling techniques in computing and network convergence
S Tang, Y Yu, H Wang, G Wang, W Chen… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …
computing power. This trend results in the urgent need for higher-level computing resource …
Serving heterogeneous machine learning models on {Multi-GPU} servers with {Spatio-Temporal} sharing
As machine learning (ML) techniques are applied to a widening range of applications, high
throughput ML inference serving has become critical for online services. Such ML inference …
throughput ML inference serving has become critical for online services. Such ML inference …
Multi-dimensional resource allocation in distributed data centers using deep reinforcement learning
W Wei, H Gu, K Wang, J Li, X Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the development of edge-cloud computing technologies, distributed data centers (DCs)
have been extensively deployed across the global Internet. Since different …
have been extensively deployed across the global Internet. Since different …
Deep reinforcement learning enhanced greedy optimization for online scheduling of batched tasks in cloud HPC systems
Y Yang, H Shen - IEEE Transactions on Parallel and …, 2021 - ieeexplore.ieee.org
In a large cloud data center HPC system, a critical problem is how to allocate the submitted
tasks to heterogeneous servers that will achieve the goal of maximizing the system's gain …
tasks to heterogeneous servers that will achieve the goal of maximizing the system's gain …
Conditional generative model based predicate-aware query approximation
Abstract The goal of Approximate Query Processing (AQP) is to provide very fast but"
accurate enough" results for costly aggregate queries thereby improving user experience in …
accurate enough" results for costly aggregate queries thereby improving user experience in …
Accelerating serverless computing by harvesting idle resources
Serverless computing automates fine-grained resource scaling and simplifies the
development and deployment of online services with stateless functions. However, it is still …
development and deployment of online services with stateless functions. However, it is still …
A parallel deep reinforcement learning framework for controlling industrial assembly lines
Decision-making in a complex, dynamic, interconnected, and data-intensive industrial
environment can be improved with the assistance of machine-learning techniques. In this …
environment can be improved with the assistance of machine-learning techniques. In this …
Freyr:Harvesting Idle Resources in Serverless Computing Via Deep Reinforcement Learning
Serverless computing has revolutionized online service development and deployment with
ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go …
ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go …
Joint Resource Overbooking and Container Scheduling in Edge Computing
Containers have gained popularity in Edge Computing (EC) networks due to their
lightweight and flexible deployment advantage. In resource-constrained EC environments …
lightweight and flexible deployment advantage. In resource-constrained EC environments …
Batch jobs load balancing scheduling in cloud computing using distributional reinforcement learning
In cloud computing, how to reasonably allocate computing resources for batch jobs to
ensure the load balance of dynamic clusters and meet user requests is an important and …
ensure the load balance of dynamic clusters and meet user requests is an important and …