3 research outputs found
Data mobility management model for active data cubes
© 2015 IEEE. Cloud computing dramatically reduces the expense and complexity of managing IT systems. Business customers do not need to invest in their own costly IT infrastructure, but can delegate and deploy their services effectively to cloud vendors and service providers. A number of security and protection mechanisms have been proposed to prevent the disclosure of sensitive information or tempering with the data by employing various policy, encryption, and monitoring approaches. However, few efforts have been focused on data mobility issues in terms of protection of data when it is moved within a cloud or to and from a new cloud environment. To allay users' concern of data control, data ownership, security and privacy, we propose a novel data mobility management model which ensures continuity protecting data at new cloud hosts at new data locations. The model provides a mobility service to handle data moving operation that relies on a new location database service. The new model allows the establishment of a proxy supervisor in the new environment and the ability of the active data to record its own location. The experimental outcomes demonstrate the feasibility, proactivity, and efficiency by the full mobility management model
Adaptive reinforcement learning for heterogeneous network selection
Next generation 5G mobile wireless networks will consist of multiple technologies for devices
to access the network at the edge. One of the keys to 5G is therefore the ability for
device to intelligently select its Radio Access Technology (RAT). Current fully distributed
algorithms for RAT selection although guaranteeing convergence to equilibrium states,
are often slow, require high exploration times and may converge to undesirable equilibria.
In this dissertation, we propose three novel reinforcement learning (RL) frameworks
to improve the efficiency of existing distributed RAT selection algorithms in a heterogeneous
environment, where users may potentially apply a number of different RAT selection
procedures. Although our research focuses on solutions for RAT selection in the
current and future mobile wireless networks, the proposed solutions in this dissertation
are general and suitable to apply for any large scale distributed multi-agent systems.
In the first framework, called RL with Non-positive Regret, we propose a novel adaptive
RL for multi-agent non-cooperative repeated games. The main contribution is to use both
positive and negative regrets in RL to improve the convergence speed and fairness of
the well-known regret-based RL procedure. Significant improvements in performance
compared to other related algorithms in the literature are demonstrated.
In the second framework, called RL with Network-Assisted Feedback (RLNF), our core
contribution is to develop a network feedback model that uses network-assisted information
to improve the performance of the distributed RL for RAT selection. RLNF guarantees
no-regret payoff in the long-run for any user adopting it, regardless of what other users
might do and so can work in an environment where not all users use the same learning
strategy. This is an important implementation advantage as RLNF can be implemented
within current mobile network standards.
In the third framework, we propose a novel adaptive RL-based mechanism for RAT selection
that can effectively handle user mobility. The key contribution is to leverage forgetting
methods to rapidly react to the changes in the radio conditions when users move.
We show that our solution improves the performance of wireless networks and converges
much faster when users move compared to the non-adaptive solutions. Another objective of the research is to study the impact of various network models on the
performance of different RAT selection approaches. We propose a unified benchmark to
compare the performances of different algorithms under the same computational environment.
The comparative studies reveal that among all the important network parameters
that influence the performance of RAT selection algorithms, the number of base stations
that a user can connect to has the most significant impact. This finding provides some
guidelines for the proper design of RAT selection algorithms for future 5G. Our evaluation
benchmark can serve as a reference for researchers, network developers, and engineers.
Overall, the thesis provides different reinforcement learning frameworks to improve the
efficiency of current fully distributed algorithms for heterogeneous RAT selection. We
prove the convergence of the proposed reinforcement learning procedures using the differential
inclusion (DI) technique. The theoretical analyses demonstrate that the use of
DI not only provides an effective method to study the convergence properties of adaptive
procedures in game-theoretic learning, but also yields a much more concise and extensible
proof as compared to the classical approaches.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201
Addressing data and user mobility challenges in the cloud
Recently cloud computing has been increasingly gaining attention and spreading in various industries. This novel paradigm also has received significant research focus on the data security and privacy issues due to its outsourced nature. However, few of the research efforts have been focused on data mobility issues which existed intrinsically in highly complex and dynamic cloud services. To address this challenge, this paper discusses full mobility management of cloud data from two aspects: data mobility management and user mobility management. The work is based on our previously proposed active data-centric framework. Taking advantage of this framework, the active data can establish an active binding relationship with its user. Any data mobility-related behavior could inform to its bundled user instantly and safely. The paper also discusses user mobility management via the self-adaptable heterogeneous network handoff mechanism to ensure the ubiquitous informing service in the mobile cloud environment. The experimental outcomes demonstrate the feasibility, proactivity, and efficiency by the full mobility management scheme. © 2013 IEEE