6,824 research outputs found
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
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
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Network slicing is a key technology in 5G communications system. Its purpose
is to dynamically and efficiently allocate resources for diversified services
with distinct requirements over a common underlying physical infrastructure.
Therein, demand-aware resource allocation is of significant importance to
network slicing. In this paper, we consider a scenario that contains several
slices in a radio access network with base stations that share the same
physical resources (e.g., bandwidth or slots). We leverage deep reinforcement
learning (DRL) to solve this problem by considering the varying service demands
as the environment state and the allocated resources as the environment action.
In order to reduce the effects of the annoying randomness and noise embedded in
the received service level agreement (SLA) satisfaction ratio (SSR) and
spectrum efficiency (SE), we primarily propose generative adversarial
network-powered deep distributional Q network (GAN-DDQN) to learn the
action-value distribution driven by minimizing the discrepancy between the
estimated action-value distribution and the target action-value distribution.
We put forward a reward-clipping mechanism to stabilize GAN-DDQN training
against the effects of widely-spanning utility values. Moreover, we further
develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to
learn the action-value distribution by estimating the state-value distribution
and the action advantage function. Finally, we verify the performance of the
proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive
simulations
- …