Rathinaraja Jeyaraj

This research aims to explore the impact of machine learning (ML) on the evolution and efficacy of recommendation systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of recommendation engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resources, typically through a cluster of virtual machines (VMs) with big data tools such as Hadoop MapReduce. In such a distributed environment, job latency and makespan are highly affected by excessive non-local executions due to various heterogeneities (hardware, VM, performance, and workload level). Existing approaches handle one or more of these heterogeneities; however, they do not account for the varying performance of storage disks. In this paper, we propose a prediction-based method for placing data blocks in virtual clusters to minimize the number of non-local executions. This is accomplished by applying a linear regression algorithm to determine the performance of disk storage on each physical machine hosting a virtual cluster. This allows us to place data blocks and execute map tasks where the data blocks are located. Furthermore, map tasks are scheduled based on VM performance to reduce job latency and makespan. We simulated our ideas and compared them with the existing schedulers in the Hadoop framework. The results show that the proposed method improves MapReduce performance in terms of job latency and makespan by minimizing non-local executions compared to other methods taken for evaluation.

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications.

Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible when MapReduce workloads and virtual machines (VM) are highly heterogeneous. Therefore, in this paper, we addressed these heterogeneities and proposed an efficient MapReduce scheduler to improve resource utilization by placing the right combination of the map and reduce tasks in each VM in the virtual cluster. To achieve this, we transformed the MapReduce task scheduling problem into a 2-Dimensional (2D) bin packing model and obtained an optimal schedule using the ant colony optimization (ACO) algorithm. As an added advantage, our proposed ACO based bin packing (ACO-BP) scheduler minimized the makespan for a batch of jobs. To showcase the performance improvement, we compared our proposed scheduler with three existing schedulers that work well in a heterogeneous environment. As expected, results show that ACO-BP significantly outperformed the existing schedulers while dealing with workload and VM level heterogeneities.

Agriculture plays a significant role in Indian economy. Nowadays, presence of tree and plant disease and organic insufficiency affects the agricultural cultivation. Classically, the tree, plant, leaf disease, and harms were identified by agricultural experts with visual inspection features like shape, color, and texture of the crop field. This system leads to time consuming, less efficiency, and expensive. To overcome this difficulty, smart farming will be helpful for farmers to prevent the plant disease. A combined Internet of Things (IoT) and image processing methods used for smart farming. This combined system enhanced product quality, volumes, cost management, waste reduction, and process automation. The significant benefit of this system is it improves agriculture cultivation. In order to implement this, first image data is collected by smart sensors using a camera or drones. For further processing, the image database is created; it contains the input images that are collected from the smart sensors. The image data set will be analyzed and it offers improved control over the inner processes. Finally, Image Processing techniques are used for disease prediction. This automated smart farming system is beneficial to those farmers who are unable to get experts knowledge and advice. This chapter aims to provide the fundamental concepts of smart farming, advantage of smart forming and application areas of smart farming.

Improving the performance of MapReduce scheduler is a primary objective, especially in a heterogeneous virtualized cloud environment. A map task is typically assigned with an input split, which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling schemes, data blocks are copied over the network to a node where the map task is running. This increases job latency and consumes considerable network bandwidth within and between racks in the cloud data centre. Considering this situation, we propose a methodology, “improving data locality using ant colony optimization (IDLACO),” to minimize the number of non-local executions and virtual network bandwidth consumption when input split is assigned to more than one data block. First, IDLACO determines a set of data blocks for each map task of a MapReduce job to perform non-local executions to minimize the job latency and virtual network consumption. Then, the target virtual machine to execute map task is determined based on its heterogeneous performance. Finally, if a set of data blocks is transferred to the same node for repeated job execution, it is decided to temporarily cache them in the target virtual machine. The performance of IDLACO is analysed and compared with fair scheduler and Holistic scheduler based on the parameters, such as the number of non-local executions, average map task latency, job latency, and amount of bandwidth consumed for a MapReduce job. Results show that IDLACO significantly outperformed the classical fair scheduler and Holistic scheduler.