Competitive Strategies for Online Cloud Resource Allocation with Discounts: The 2-Dimensional Parking Permit Problem

2015 ◽  
Author(s):  
Xinhui Hu ◽  
Arne Ludwig ◽  
Andrea Richa ◽  
Stefan Schmid
Keyword(s):  
Resource Allocation ◽  
Competitive Strategies ◽  
Cloud Resource

scholarly journals Algorithms for network-aware application component placement for cloud resource allocation

2017 ◽  
Vol 19 (5) ◽  
pp. 493-508 ◽  
Author(s):  
Maryam Barshan ◽  
Hendrik Moens ◽  
Steven Latre ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Resource Allocation ◽  
Component Placement ◽  
Cloud Resource ◽  
Application Component

scholarly journals Learning Resource Allocation and Pricing for Cloud Profit Maximization

2019 ◽  
Vol 33 ◽  
pp. 7570-7577 ◽  
Author(s):  
Bingqian Du ◽  
Chuan Wu ◽  
Zhiyi Huang
Keyword(s):  
Resource Allocation ◽  
Optimal Allocation ◽  
Short Term Memory ◽  
Fundamental Problem ◽  
Cloud Provider ◽  
Resource Usage ◽  
Performance Models ◽  
Model Free ◽  
Cloud Resource ◽  
Real World Datasets

Cloud computing has been widely adopted to support various computation services. A fundamental problem faced by cloud providers is how to efficiently allocate resources upon user requests and price the resource usage, in order to maximize resource efficiency and hence provider profit. Existing studies establish detailed performance models of cloud resource usage, and propose offline or online algorithms to decide allocation and pricing. Differently, we adopt a blackbox approach, and leverage model-free Deep Reinforcement Learning (DRL) to capture dynamics of cloud users and better characterize inherent connections between an optimal allocation/pricing policy and the states of the dynamic cloud system. The goal is to learn a policy that maximizes net profit of the cloud provider through trial and error, which is better than decisions made on explicit performance models. We combine long short-term memory (LSTM) units with fully-connected neural networks in our DRL to deal with online user arrivals, and adjust the output and update methods of basic DRL algorithms to address both resource allocation and pricing. Evaluation based on real-world datasets shows that our DRL approach outperforms basic DRL algorithms and state-of-theart white-box online cloud resource allocation/pricing algorithms significantly, in terms of both profit and the number of accepted users.

A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation

2018 ◽  
Vol 78 ◽  
pp. 343-352 ◽  
Author(s):  
Massimo Ficco ◽  
Christian Esposito ◽  
Francesco Palmieri ◽  
Aniello Castiglione
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Coral Reefs ◽  
Cloud Resource

Dynamic Cloud Resource Allocation Considering Demand Uncertainty

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Seyedehmehrnaz Mireslami ◽  
Logan Rakai ◽  
Mea Wang ◽  
Behrouz H. Far
Keyword(s):  
Resource Allocation ◽  
Demand Uncertainty ◽  
Cloud Resource
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