Sustainable Computing-Informatics & Systems最新文献

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user’s workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users’ satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation.

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR).

Cloud computing has emerged as a cornerstone technology for modern computational paradigms due to its scalability and flexibility. One critical aspect of cloud computing is efficient task scheduling, which directly impacts system performance and resource utilization. In this paper, we propose an enhanced optimization algorithm tailored for task scheduling in cloud environments. Building upon the foundation of the Jaya algorithm and Synergistic Swarm Optimization (SSO), our approach integrates a Levy flight mechanism to enhance exploration-exploitation trade-offs and improve convergence speed. The Jaya algorithm's ability to exploit the current best solutions is complemented by the SSO's collaborative search strategy, resulting in a synergistic optimization framework. Moreover, the incorporation of Levy flights injects stochasticity into the search process, enabling the algorithm to escape local optima and navigate complex solution spaces more effectively. We evaluate the proposed algorithm against state-of-the-art approaches using benchmark task scheduling problems in cloud environments. Experimental results demonstrate the superiority of our method in terms of solution quality, convergence speed, and scalability. Overall, our proposed Improved Jaya Synergistic Swarm Optimization Algorithm offers a promising solution for optimizing TSCC (TSCC), contributing to enhanced resource utilization and system performance in cloud-based applications. The proposed method got 88 % accuracy overall and 10 % enhancement compared to the original method.

Establishing robust electrical interconnections between nations is a pivotal foundation for significant investments and addressing energy shortfalls in regions grappling with generational challenges. However, developing electrical load disruptions within interconnected systems can lead to substantial variations in frequencies and energy transmission. Load Frequency Control (LFC) is a crucial mechanism to mitigate these disruptions and ensure stable operations in interconnected regions. While meta-heuristics have been employed for LFC design, some techniques face challenges like early convergence and poor accuracy due to a lack of population diversity. In this study, a novel Modified Cheetah Optimizer (mCO) is proposed to optimize the parameters of LFC system, incorporating fractional-order proportional integral derivative (FOPID) controllers within multi-interconnected system with renewable energy integration. The mCO integrates learning-from-experience and random contraction strategies to enhance convergence accuracy and overcome local optima, demonstrating superior efficiency in solving optimization problems. The proposed mCO is evaluated by solving twelve functions from the CEC2022 test suite, showcasing its effectiveness. The optimization problem involves minimizing the Integral Time Absolute Error (ITAE) of the area control error, considering changes in frequencies and exchanged power, with controller parameters ${\lambda }_d$, $k_d$, $k_i$, $k_p$, and $\mu$ to be identified. Two interconnected systems, photovoltaic (PV)-thermal and thermal-wind turbine (WT)-thermal-PV, are assessed under various load disturbances. The mCO is compared with other methods, including Modified Hunger Games Search Optimizer (MHGS), Driving Training-Based Optimizer (DTBO), Grey Wolf Optimizer (GWO), Aquila Optimal Search (AOS), and Cheetah Optimizer (CO). In the case of PV-thermal linked system, the proposed mCO succeeded in mitigating the ITAE by 19.21% compared to the reported MHGS and 8.63% compared to the conventional CO. In the four interconnected systems, the suggested approach reduced the ITAE by 89.21% and 15.26% compared to the reported MHGS and conventional CO, respectively. This confirmed the efficacy of FOPID-LFC, which was designed using the proposed mCO in all examined scenarios.

In this paper, an uncertainty-aware task scheduling approach capable of dynamically applying on multiprocessor embedded systems called ”DyUnS” is presented. This method is based on a type-2 fuzzy inference system to consider all design challenges of multiprocessor embedded systems along with their unavoidable uncertainty caused by the differences in models and measurements. The proposed method employs a fuzzy inference system to approximate the appropriate assignment of the application’s tasks to processing cores based on a defined rank including the main design challenges of the system including execution time, temperature, power consumption, and reliability. Moreover, an uncertainty level is defined for various design challenges as the footprint of uncertainty during the scheduling process to tackle the existing inaccuracy between the static models and dynamic environment. Thus, the generated uncertainty-aware solution could be efficiently employed as a dynamic scheduling at runtime. To demonstrate the effectiveness of DyUnS in tolerating uncertainty, several experiments on various application graphs are performed and its effectually is compared to related studies. Based on these experiments, DyUnS jointly optimizes the main design parameters, and its generated solution could be employed dynamically without violating the system’s thresholds. Moreover, its average difference compared to Monte Carlo uncertainty analysis is about 0.2 for all design parameters in three levels of uncertainty.

In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In this context, swarm intelligence-based algorithms are identified to be suitable and ideal for determining near-optimal and optimal solutions in the search space. On the other hand, APTEEN routing protocol possesses the issues that are related to unnecessary energy drain, ineffective overall network coverage and premature death of certain nodes. To address these issues, an attempt to optimize the APTEEN routing protocol using Dingo Optimization Algorithm (DOA) and Beluga Whale Optimization Algorithm (BWOA) is made in this proposed clustering protocol. With this motivation, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimized the energy consumption and maximize the lifetime of the network. It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination. It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime. The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation.

The Task Scheduling Problem (TSP) in Multiprocessor Systems (MPS) is an emerging research area in heterogeneous distributed computing environments. Managing complex tasks and achieving optimal efficiency while scheduling tasks in MPS presents challenges. Although adding more processors to a single system greatly increases its processing power, the energy these processors produce is the main drawback. Here, we employed a multi-objective optimization strategy to reduce the makespan and Energy Consumption (EC). To reduce processor EC, we considered an effective Dynamic Voltage and Frequency Scaling (DVFS) technique. Three distinct energy sources are considered, originating from the processors' communication, idle, and active states. The scheduling sequence of tasks and the assignment of tasks to processors are two vital aspects of TSP. Here, the tasks are arranged based on priority using the Upward Rank technique. To minimize the makespan and EC while allocating tasks to processors, we use the population-based metaheuristic method called Enhanced Honey Badger Optimisation (EHBO). We proposed three improvements to the HBO algorithm in EHBO. Initially, we addressed opposite learning-based population initialization to exclude the least suitable candidates and generate a candidate scheduling population that adheres to task precedence. Subsequently, the levy-flight technique is employed to improve the local and global search and preserve their ideal healthy balance. Finally, using dynamic values rather than constant value improves the ability to obtain maximum food. Several experiments are conducted on random task graphs and real-life data sets. Additionally, the results are compared with other upgraded meta-heuristic algorithms, demonstrating the superiority of EHBO.

The users of home energy management systems schedule their real-time energy consumption thanks to advancements in communication technology and smart metering infrastructures. In this paper, a data-driven strategy is proposed, which is an Online Home Energy Management System (ON-HEM) that uses reinforcement learning algorithms (Q-Learning and Deep Q-Learning) to control the optimal energy consumption of a smart home system. The proposed system comprises power resources (grid, photovoltaic), communication networks, and appliances with their agents classified into four groups: deferrable, non-deferrable, power level controllable, and electric vehicle. The system reduces electricity costs and high peak demands while considering the cost of user dissatisfaction with real-life data. Simulations are performed on the proposed ON-HEM considering different pricing approaches (Real Time Pricing and Time of Use Pricing) with Q-Learning and Deep Q-Learning (DQL) algorithms using PyCharm Professional Edition software. The findings demonstrate both the superiority of DQL over Q-Learning and the efficiency of the proposed ON-HEM in decreasing high peak demand, electricity costs, and customer dissatisfaction costs. The efficiency and dependability of the proposed system were verified by utilizing simulation-based findings with real-life data using IBM SPSS Statistics software.

The field of smart cities has seen significant advancements in recent years to improve citizens' quality of life. Technologies such as the Internet of Things (IoT) and Edge Computing (EC), along with Artificial Intelligence (AI), are being utilized to achieve this goal. This study focuses on a specific branch of IoT known as Visual IoT, which uses digital cameras as sensors and relies on visual data. Advances in AI have enabled researchers to integrate AI models into camera-based edge devices, increasing the use of AI-powered Visual IoT systems in smart cities. However, since the energy consumption in battery-powered systems is naturally a concern, being deployed outdoors for visual data gathering with the integration of AI-based processing raises a significant challenge. This paper examines AI-powered Visual IoT systems in smart cities with a special emphasis on energy efficiency. Our goal is not only to evaluate how AI is used in Visual IoT systems in the context of smart cities but also to evaluate the level of consideration given to the energy efficiency aspect in the reviewed studies. Furthermore, we explore all of the methods used to address it. Through our work, readers will gain insights into the current landscape of Visual IoT in smart cities and an understanding of how much importance is placed on energy consumption in AI-integrated solutions.