Ashis Talukder

Congestion prevention mechanism based on Q-leaning for efficient routing in SDN

SDN (Software-Defined Networking) has been proposed to solve problems caused by difficulties in central management, vendor dependency, and increase in complexity of network due to individual process. In current SDN, however, routing algorithm is mainly based on Dijkstras Algorithm and the shortest Flow Path is selected to deliver packets. This may result in network congestion since bandwidth overhead is not considered when a lot of traffic enters in the network. Therefore, we propose a mechanism to prevent network congestion based on Q-learning for efficient routing in SDN. In this paper, we show the network congestion can be improved by reselecting the path and changing Flow Table using predefined threshold and Q-learning routing algorithm.

Performance analysis of H.264, H.265, VP9 and AV1 video encoders

Videos can portray our thinking in a better way than anything can. As a result, online as well as offline videos in entertainment, health, storytelling, education and in surveillance are increasing day by day which further initiates video coding research to decrease the size while keeping satisfactory quality. Due to the ubiquitous necessity of video encoding, a fully functional, high performance and with easy licensing codec have been sought from industry and academia for a long time. In this paper, we investigate the performance of the software encoders of H.264/AVC, H.265/HEVC, VP9 and the new royalty free encoder AV1 from AOMedia for relatively small video files. To ensure fair analysis, we performed extensive experiments by varying different parameters. With default parameters, for the same video bitrates, AV1 obtains better quality as compared to other encodings. However, when we use the slowest time preset ‘placebo’ for H.264 and H.265, AV1 goes slightly behind H.265. Among the other two, VP9 always outperforms H.264. For all schemes, as quality increases, the encoding time also increase.

An approach of cost optimized influence maximization in social networks

Social networks have gained huge research interest, especially in viral marketing due to their rapid boom in the past years. It is very crucial to identify the influential users in the social networks for viral and target marketing. Influence maximization (IM) problem estimates such influential users in the social networks. With an initial seed set, the IM finds a maximum number of nodes that can be activated in the network under some diffusion models e.g. Linear Threshold model or Independent Cascade model. But previous works in this field have not studied about the minimum cost, termed as opportunity cost (OC), to motivate those seed nodes. In this work, we define a novel Reverse Influence Maximization (RIM) problem to determine the opportunity cost of influence maximization. Employing the influence propagation in opposite order, the RIM determines the minimum number of nodes that must be activated in order to motivate a set of target nodes. We propose Random RIM (R-RIM) and Randomized Linear Threshold RIM (RLT-RIM) models to tackle the RIM problem. We also perform a simulation to evaluate the performance of the algorithms using two real world datasets. The result shows that the proposed models determine the optimized opportunity cost with faster running time margin.

An overlapping coalition formation approach to maximize payoffs in cloud computing environment

Data-intensive applications are anticipated to increase day by day. The amount of computing and storage resources required by these are also increasing simultaneously, creating high demands for cloud resources. Cloud providers limited resources are not adequate to meet the elastic demand of consumers. Cloud coalition is the key approach to deal with such elastic demand of resources in the environment. But traditional coalition formation mechanism allows a cloud provider to participate into a single coalition which leads to under-utilize of coalition resources with increasing security risk like botnet attacks. In this paper, a mathematical framework from cooperative game namely overlapping coalition formation (OCF) game to overcome the under-utilization problem and to reduce security risk for cloud providers (CPs) is introduced by permitting a CP to participate into multiple small coalitions. First, the concepts of OCF game in cloud is presented and then, an algorithm for the stability in OCF game is introduced. We analyze the performance of the proposed approach with traditional coalition formation and no-coalition scenarios in perspective of payoffs, yielding higher payoffs for the participating CPs.