Resource Management in Distributed Systems [E-Book] / edited by Anwesha Mukherjee, Debashis De, Rajkumar Buyya.
This book focuses on resource management in distributed computing systems. The book presents a collection of original, unpublished, and high-quality research works, which report the latest research advances on resource discovery, allocation, scheduling, etc., in cloud, fog, and edge computing. The t...
Saved in:
Full text |
|
Personal Name(s): | Buyya, Rajkumar, editor |
De, Debashis, editor / Mukherjee, Anwesha, editor | |
Edition: |
1st edition 2024. |
Imprint: |
Singapore :
Springer,
2024
|
Physical Description: |
XII, 314 pages 88 illustrations, 75 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9789819726448 |
DOI: |
10.1007/978-981-97-2644-8 |
Series Title: |
/* Depending on the record driver, $field may either be an array with
"name" and "number" keys or a flat string containing only the series
name. We should account for both cases to maximize compatibility. */?>
Studies in Big Data ;
151 |
Subject (LOC): |
- Resource Management in Distributed Computing
- Cloud Computing Resource Management
- Resource Allocation and Placement in Multi-access Edge Computing
- Resource Scheduling in Integrated IoT and Fog Computing Environments: A Taxonomy, Survey and Future Directions
- Trusted task offloading and resource allocation strategy in MEC environment
- Resource Management in Edge Clouds: Latency-aware Approaches for Big Data Analysis
- FSRmSTS - An Optimized Task Scheduling with a Hybrid Approach: Integrating FCFS, SJF, and RR with Median Standard Time Slice
- Container Orchestration in Heterogeneous Edge Computing Environments
- Resource targeted cybersecurity attacks in cloud computing environments
- Load balancing using Swarm intelligence in cloud Environment
- Interoperability and Portability in Big Data Analysis based Cloud-Fog-Edge-Dew Computing
- Cyber attack victim separation: new dimensions to minimize attack effects by resource management
- eBPF and XDP Technologies as Enablers for Ultra-Fast and Programmable Next-Gen Network Infrastructures
- Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions.