Isma Farah Siddiqui

Storage-Tag-Aware Scheduler for Hadoop Cluster

Big data analytics has simplified the processing complexity of extremely large data sets through ecosystems, such as Hadoop, MapR, and Cloudera. Apache Hadoop is an open-source ecosystem that manages large data sets in a distributed environment. MapReduce is a programming model that processes massive amount of unstructured data sets over Hadoop cluster. Recently, Hadoop enhances its homogeneous storage function to heterogeneous storage and stores data sets into multiple storage media, i.e., SSD, RAM, and DISK. This development increases the performance of data block placement strategy and allows a client to store large data sets into multiple storage media efficiently than homogeneous storage. However, this evolution increases the consumption of computing capacity and memory usage over MapReduce job scheduling. The scheduler processes MapReduce job into homogeneous container having configuration of CPU, memory, DISK volume, and network I/O, and accesses, processes, and stores data sets over heterogeneous storage media. This produces a processing latency of locating and pairing source data set to MapReduce tasks and results an abnormal high consumption of computing capacity and memory usage in a Datanode. Similarly, when scheduler assigns MapReduce jobs to multiple Datanodes, the same processing latency can severely affect the performance of whole cluster. In this paper, we present Storage-Tag-Aware Scheduler (STAS) that reduces processing latency by scheduling MapReduce jobs into heterogeneous storage containers, i.e., SSD, DISK, and RAM container. STAS endorses job with a tag of storage media, such as

Optimizing Lifespan and Energy Consumption by Smart Meters in Green-Cloud-Based Smart Grids

Job_{SSD}

Binary Pattern for Nested Cardinality Constraints for Software Product Line of IoT-Based Feature Models

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An Advanced Hyper-Efficient Design to Detect Random Peer-to-Peer Botnets

Job_{DISK}

Multi-Objective Optimization of Feature Model in Software Product Line: Perspectives and Challenges

, and

An Approach for Optimized Feature Selection in Software Product Lines using Union-Find and Genetic Algorithms

Job_{RAM}

Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line

and parses them into heterogeneous shared-queues, which assign processing configuration to enlist jobs. STAS manager then schedules shared-queue jobs into heterogeneous MapReduce containers and generates an output into storage media of the cluster. The experimental evaluation shows that STAS optimizes the consumption of computing capacity and memory usage efficiently than available schedulers in a Hadoop cluster.

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Green clouds optimally use energy resources in large-scale distributed computing environments. Large scale industries such as smart grids are adopting green cloud paradigm to optimize energy needs and to maximize lifespan of smart devices such as smart meters. Both, energy consumption and lifespan of smart meters are critical factors in smart grid applications where performance of these factors decreases with each cycle of grid operation such as record reading and dispatching to the edge nodes. Also, considering large-scale infrastructure of smart grid, replacing out-of-energy and faulty meters is not an economical solution. Therefore, to optimize the energy consumption and lifespan of smart meters, we present a knowledge-based usage strategy for smart meters in this paper. Our proposed scheme is novel and generates custom graph of smart meter tuple datasets and fetches the frequency of lifespan and energy consumption factors. Due to very large-scale dataset graphs, the said factors are fine-grained through R3F filter over modified Hungarian algorithm for smart grid repository. After receiving the exact status of usage, the grid places smart meters in logical partitions according to their utilization frequency. The experimental evaluation shows that the proposed approach enhances lifespan frequency of 100 smart meters by 72% and optimizes energy consumption at an overall percentile of 21% in the green cloud-based smart grid.

Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid

Software product line (SPL) is extensively used for reusability of resources in family of products. Feature modeling is an important technique used to manage common and variable features of SPL in applications, such as Internet of Things (IoT). In order to adopt SPL for application development, organizations require information, such as cost, scope, complexity, number of features, total number of products, and combination of features for each product to start the application development. Application development of IoT is varied in different contexts, such as heat sensor indoor and outdoor environment. Variability management of IoT applications enables to find the cost, scope, and complexity. All possible combinations of features make it easy to find the cost of individual application. However, exact number of all possible products and features combination for each product is more valuable information for an organization to adopt product line. In this paper, we have proposed binary pattern for nested cardinality constraints (BPNCC), which is simple and effective approach to calculate the exact number of products with complex relationships between application’s feature models. Furthermore, BPNCC approach identifies the feasible features combinations of each IoT application by tracing the constraint relationship from top-to-bottom. BPNCC is an open source and tool-independent approach that does not hide the internal information of selected and non-selected IoT features. The proposed method is validated by implementing it on small and large IoT application feature models with “n” number of constraints, and it is found that the total number of products and all features combinations in each product without any constraint violation.

Comparative Analysis of Centralized Vs. Distributed Locality-based Repository over IoT-Enabled Big Data in Smart Grid Environment

Botnets have become one of the most solemn threats to Internet security. Botnets comprises over a network of infected nodes known as ‘bot’. Bots are controlled by human operators (botmasters). Random nature of Peer-to-Peer botnets has influenced sinkhole researchers to compromise over occupation of hunted command and control in a complex manner and due to variable nature of action, they are often good deserters. In this paper, we present a design of an advanced hyper-efficient mechanism which has the ability to pursue Peer-to-Peer randomized botnets. It provides capacity to detain targeted sinkholes and identify arbitrary execution of contagion in infected nodes. In the end, method acquires the composition of different cubic formations for proper lookup of random natured Peer-to-Peer botnets.