State-of-the-art load balancing algorithms for mist-fog-cloud assisted paradigm: a review and future directions
The rapid growth of IoT devices leads to increasing requests. These tremendous requests
cannot be processed by IoT devices due to the computational power of IoT devices and the …
cannot be processed by IoT devices due to the computational power of IoT devices and the …
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 …
Deep reinforcement learning-based task scheduling in iot edge computing
Edge computing (EC) has recently emerged as a promising paradigm that supports resource-
hungry Internet of Things (IoT) applications with low latency services at the network edge …
hungry Internet of Things (IoT) applications with low latency services at the network edge …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem
Abstract Distributed Job-shop Scheduling Problem (DJSP) is a hotspot in industrial and
academic fields due to its valuable application in the real-life productions. For DJSP, the …
academic fields due to its valuable application in the real-life productions. For DJSP, the …
Resource allocation with workload-time windows for cloud-based software services: a deep reinforcement learning approach
As the workloads and service requests in cloud computing environments change constantly,
cloud-based software services need to adaptively allocate resources for ensuring the Quality …
cloud-based software services need to adaptively allocate resources for ensuring the Quality …
Growable Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing
Abstract Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-
objective optimization problem) has attracted focus in the management of large-scale cloud …
objective optimization problem) has attracted focus in the management of large-scale cloud …
Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing
Cloud computing, as a large-scale distributed computing system dynamically providing
elastic services, is designed to meet the requirement of delivering computing services to …
elastic services, is designed to meet the requirement of delivering computing services to …
Anomaly detection in cloud computing using knowledge graph embedding and machine learning mechanisms
The orchestration of cloud computing infrastructures is challenging, considering the number,
heterogeneity and dynamicity of the involved resources, along with the highly distributed …
heterogeneity and dynamicity of the involved resources, along with the highly distributed …
Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical cloud computing
Cloud computing environment is becoming increasingly complex due to its large-scale
information growth and increasing heterogeneity of computing resources. Hierarchical …
information growth and increasing heterogeneity of computing resources. Hierarchical …