Leveraging Deep Learning to Achieve Efficient Resource Allocation with Traffic Evaluation in Datacenter Optical Networks

2018 ◽  
Author(s):  
Ao Yu ◽  
Hui Yang ◽  
Wei Bai ◽  
Linkuan He ◽  
Hongyun Xiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Optical Networks ◽  
Efficient Resource

Efficient resource allocation with service guarantees in passive optical networks

2007 ◽  
Vol 6 (7) ◽  
pp. 884 ◽  
Author(s):  
T. Orphanoudakis ◽  
H.-C. Leligou ◽  
E. Kosmatos ◽  
J. D. Angelopoulos ◽  
K. Kanonakis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Optical Networks ◽  
Passive Optical Networks ◽  
Service Guarantees ◽  
Efficient Resource

Efficient resource allocation of heterogeneous services in transparent optical networks

2007 ◽  
Vol 6 (12) ◽  
pp. 1349 ◽  
Author(s):  
Nadiatulhuda Zulkifli ◽  
Raul C. Almeida ◽  
Kenneth M. Guild
Keyword(s):  
Resource Allocation ◽  
Optical Networks ◽  
Transparent Optical Networks ◽  
Heterogeneous Services ◽  
Efficient Resource

scholarly journals A Deep Learning Approach for Mobility-Aware and Energy-Efficient Resource Allocation in MEC

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 179530-179546
Author(s):  
Zaiwar Ali ◽  
Sadia Khaf ◽  
Ziaul Haq Abbas ◽  
Ghulam Abbas ◽  
Fazal Muhammad ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Energy Efficient ◽  
Learning Approach ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation

Efficient Resource Allocation for All-Optical Multicasting Over Spectrum-Sliced Elastic Optical Networks

2013 ◽  
Vol 5 (8) ◽  
pp. 836 ◽  
Author(s):  
Long Gong ◽  
Xiang Zhou ◽  
Xiahe Liu ◽  
Wenwen Zhao ◽  
Wei Lu ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Optical Networks ◽  
All Optical ◽  
Efficient Resource

scholarly journals Efficient Resource Management for Deep Learning Applications with Virtual Containers

2020 ◽  
Author(s):  
Wenjia Zheng
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Completion Time ◽  
Resource Optimization ◽  
Single Node ◽  
Resource Configuration ◽  
Learning Tasks ◽  
Efficient Resource ◽  
Slow Growing ◽  
System Time

The explosion of data has transformed the world since much more information is available for collection and analysis than ever before. To extract valuable information from the data in different dimensions, various deep learning models have been developed in the past years. Although these models have demonstrated their strong capability on improving products and services in various applications, training them is still a time-consuming and resource-intensive process. Presently, cloud, one of the most powerful computing infrastructures, has been used for the training. However, how to manage cloud computing resources and to perform the training efficiently is still challenging current techniques. For example, general resource scheduling approaches, such as spread priority and balanced resource schedulers, actually do not work well with deep learning workloads. Besides, the resource allocation problem on a cluster can be divide into two subproblems: (1) local resource optimization: improve resource configuration for a single machine; (2) global resource optimization: improve the cluster-wide resource allocation. In this thesis, we propose two novel container schedulers, FlowCon and SpeCon, that are designed to address these two subproblems respectively and specifically to optimize performance of short-lived deep learning applications in the cloud. FlowCon focuses on resource configuration of single-node in a cluster, as show that it efficiently improves deep learning tasks completion time and resource utilization, and reduces the completion time of a specific job by up to 42.06\% without sacrificing the overall system time. SpeCon targets on cluster-wide resource configuration that speculatively migrate slow-growing models to release resources for fast-growing ones. Based on our experiments, SpeCon improves makespan for up to 24.7\%, compared to current approaches.

Efficient Resource Allocation for Multiclass Services in Multiuser OFDM Systems

2009 ◽  
Vol E92-B (2) ◽  
pp. 533-543 ◽  
Author(s):  
Jae Soong LEE ◽  
Jae Young LEE ◽  
Soobin LEE ◽  
Hwang Soo LEE
Keyword(s):  
Resource Allocation ◽  
Ofdm Systems ◽  
Efficient Resource ◽  
Multiuser Ofdm
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