Conference Paper

Permanent URI for this collectionhttps://hdl.handle.net/10361/6933

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Reinforcement learning based autonomic virtual machine management in clouds
(© 2016 IEEE, 11/28/2016) Habib, Arafat; Khan, Muhidulislam; Department of Computer Science and Engineering
Cloud computing is a rapidly emerging field, services and applications are more or less 24/7. Resource dimensioning in this field is a great issue. Research is already going on to imply reinforcement learning to automate decision making process in case of addition, reduction, migration and maintenance of the Virtual Machines (VM) to balance the service level performance and VM management cost. Models have been proposed in this case based on Q-learning, a very popular reinforcement learning technique that is used to find optimal action selection policy for any finite Markov Decision Process (MDP). In this paper we propose to work with the challenges like proper initialization of the early stages, designing the states, actions, transitions using Markov Decision Process (MDP) and solving the MDP with two popular reinforcement learning techniques, Q-learning and SARSA(Λ).
Efficient and fast convergent consensus algorithms for faulty nodes tracking in distributed wireless sensor networks
(© 2016 IEEE, 2016-09) Khan, Muhidulislam; Hossain, Rajkin; Department of Computer Science and Engineering
One of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value then all the nodes decide on that. Every correct nodes must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate.
Cooperative game theory based load balancing in long term evolution network
(© 2015 Institute of Electrical and Electronics Engineers Inc., 2015) Saha, Subarno; Hossain, Rajkin; Khan, Muhidul Islam; Department of Electrical and Electronic Engineering
Long term evolution (LTE) network, incompatible with 2G and 3G networks is the most promising technology for wireless communication with higher speed and capacity. Self-organized load balancing is an important research issue for the wireless networks. Game theory provides an efficient way to provide self-organizing properties in a distributed environment like LTE networks. Load balancing means to assign users from highly loaded cells to neighbor lower loaded cells. The amount of load needs to be offloaded or accepted by a particular cell is not really specified and currently totally vendor specified. In our proposed cooperative game theoretic approach, each cell is considered as a player where they trade the load by forming a coalition by satisfying the overall performance of the network. Simulation results show that our proposed method provides better performance in terms of satisfied users and adjusted load values.

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