9,310 research outputs found
Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management
In this paper we introduce algorithms for the construction of scale-free
networks and for clustering around the nerve centers, nodes with a high
connectivity in a scale-free networks. We argue that such overlay networks
could support self-organization in a complex system like a cloud computing
infrastructure and allow the implementation of optimal resource management
policies.Comment: 14 pages, 8 Figurs, Journa
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
An SMDP-based Resource Management Scheme for Distributed Cloud Systems
In this paper, the resource management problem in geographically distributed
cloud systems is considered. The Follow Me Cloud concept which enables service
migration across federated data centers (DCs) is adopted. Therefore, there are
two types of service requests to the DC, i.e., new requests (NRs) initiated in
the local service area and migration requests (MRs) generated when mobile users
move across service areas. A novel resource management scheme is proposed to
help the resource manager decide whether to accept the service requests (NRs or
MRs) or not and determine how much resources should be allocated to each
service (if accepted). The optimization objective is to maximize the average
system reward and keep the rejection probability of service requests under a
certain threshold. Numerical results indicate that the proposed scheme can
significantly improve the overall system utility as well as the user experience
compared with other resource management schemes.Comment: 5 pages, 5 figures, conferenc
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