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A burning challenge is balancing the demand for servers in computer data centers. In the modern era, the bulk of computer systems are used for big data and cloud computing. The Internet of Things (IoT) is becoming a hyper-world of physical, cyber and social worlds with big data as a connection. In the latest systems architectures, managing the load in cloud applications is a major challenge. While several techniques and algorithms have been framed to achieve optimality in the load balancing definition, they are limited to the current problems of that time. However, due to a massive increase in data to be handled for the computing activities that are running on servers, exponential petabytes of data that are stored, the complexity of the problem increases day by day. By efficiently balancing the load and high utilization of the available resources from the computing pool, the solution to such problems can be achieved. When these are performed professionally, optimum values can be accomplished with the prominent use of available resources. Genetic Algorithms, Bacteria Foraging, optimizes the algorithm, Artificial Bee Colony, Optimized ABC are current trending algorithms used in load balancing concepts. All these trending algorithms, while strong, lack certain factors. To overcome them, we proposed an EBGO algorithm that has overcome the disadvantages of the trending load balancing algorithms in terms of certain significant parameters such as response time, energy consumption, weighted total cost, procession time, and many. This model aims to achieve overall optimal parameter values, thus efficiently balancing the demand on servers in data centers, resulting in optimal resource use. This model is often appropriate for health care systems, where large number of different types of systems and data are shared between them. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]

Outlier detection in the stream data has emerged as the challenging problems with the precipitously growing demand of applications like intrusion detection, sensor malfunctioning, fraud detection, and system failures, etc. To address these problems many density-based algorithms have been proposed for detecting the outliers in stream data. Still, it suffers a serious problem with the degree of outlierness measures on its neighbors. Using a right number k is not straightforward in the stream data since we do not know prior distribution of neighbor's points. And, it must improvise with incoming data as appeared in the stream data. Additionally, stream-based algorithms are not able to detect sequential outliers as well as are having memory constraints. These challenges motivate the authors to propose "Self-Adaptive Density Summarizing incremental Natural Outlier Detection in Data Stream (ADINOF) with skipping scheme and without skipping scheme (ADINOF_NS)" that successfully overcome the challenges. Our comprehensive experimental evaluations demonstrate that ADINOF and ADINOF_NS significantly outperforms the competitive executed algorithms (iLOF, MiLOF, TADILOF, DILOF, and DILOF_NS). [ABSTRACT FROM AUTHOR]

This paper presents a quality of service (QoS) concerned energy efficient data gathering protocol for wireless sensor network (WSN) by means of a mobile data collector (MDC). Several resource constraints of WSN nodes make the QoS preservation very challenging, particularly the energy constraint. Moreover, the energy hole problem during data communication has emerged as a major threat to QoS preservation in WSN. The state-of-the-art algorithms have employed MDC as a promising approach to effectively alleviate the energy hole problem. Efficient trajectory design can allow the MDC to sojourn at limited data collection points (CPs) while covering significantly more nodes, reducing energy consumption and latency in data collection. However, estimation of the optimal number of CPs and MDC trajectory design is an NP-hard problem in a large-scale network. To solve this problem, variable dimensional particle swarm optimization (VD-PSO) is leveraged for the optimal nomination of CPs, and ant colony optimization is applied to determine the best trajectory visiting all the nominated CPs. A multi-objective fitness function is designed to assess the quality of the nominated CPs by considering various QoS metrics. In order to accelerate the convergence of the VD-PSO algorithm, a modified particle update procedure and dimensionality pruning are also introduced. The simulation results depict the effectiveness of the proposed protocol in terms of network lifetime, total energy consumption, end-to-end delay, packet delivery ratio, etc. [ABSTRACT FROM AUTHOR]