A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI)

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Navigating the Cloud: An In-Depth Exploration of HISA Load Balancing for Dynamic Task Appropriation

In a cloud computing (CC) environs,job exhibit variations in durations, start times, and execution times when assigned to virtual machines (VMs). Therefore, achieving load balancing (LB) across these VMs becomes crucial to optimize system proficiency and presentation. The present research introduces a novel LB method leveraging two optimization algorithms to address VM load balancing challenges. The initiated Dynamic Improved HISA Load Balancing proposal integrates an augment harmony-inspired algorithm with a simulated annealing algorithm for dynamic task allocation.In the harmony-inspired algorithm, an improved strategy for calculating Harmony Memory Consideration Rate (HMCR) is employed through a linear decreasing approach, updating HMCR and Pitch Adjustment Rate (PAR) values dynamically. A threshold probability is then evaluated to determine the finest suitability of the current Harmony, choosing eachof the make better harmony-inspired algorithm or simulated annealing for task allocation across available cloud resources.Simulations are conducted using the CloudSim simulator, considering scenarios with 3 or 5 VMs and 10 to 50 cloudlets. Each scenario is tested five times under operational conditions, and only the best performance outcomes are reported. Experimental results specify such a initiated Dynamic Enhanced HISA-LB proposal outperforms the prevail LBMPSO approach, demonstrating either minimized makespan or enhanced resource utilization with increased performance

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

Integrating the EGC, EF, and ECS Trio Approaches to Ensure Security and Load Balancing in the Cloud

According to data protection studies, "Distributed Denial-of-Service (DDoS)" threats have cost governments and businesses throughout the globe a large number of financial resources. Despite this, the existing practices fall short of the standards set by "Cloud Computing (CC)" monitoring technology. They ignore the "Intrusion Detection Systems (IDS)" techniques, which take advantage of the CC's multiple tenants and elasticity qualities, and also the hardware limitations. Attackers are finding increasing ways to effectively exploit them because of their rising complexity. DDoS assaults of this scale have never been observed online before 2018. As online services get more popular, so does the amount of DDoS assaults and malevolent hackers leading to terrible. Numerous IDS for DDoS are already in place to address this problem. One of the most challenging aspects of virtualization is establishing a "Trust Model (TM)" between the many "Virtual Machines (VMs)". The lack of a standard formulation for generating a TM would be the primary reason. As a consequence, the integrity of every VM might not have been recognized by an independent trust, which might lead to a decrease in trust value. In this research for TM creation, "Enhanced Graph Based Clustering (EGC)" is proposed, while "Enhanced Fuzzy (EF)" is used for detecting attacks, and the "Enhanced Cuckoo Search (ECS)" method is used to find the ideal "Load Balancing (LB)" distribution. By creating a new TM, the proposed (EGC-EF-ECS) system strengthens trust value. To expand the CC model's stability, it optimizes attacker recognition percentage and makes better use of resources by restricting each VM's processing, bandwidth, and storage requirements. The proposed EGC-EF-ECS outperformed the previously used BPA-SAB, and DCRI-RI approaches in terms of the "Intrusion-Detection-Rate (IDR)", "Load-Balancing-Efficiency (LBE)", and "Data-Accessing-Time (DAT)" evaluation metrics