Cloud Computing
Gowri S; Jaganathan Rathi
Abstract
Background and Objectives: Cloud computing can play a vital role in promoting environmental sustainability by leveraging eco-friendly dedicated servers that adhere to green computing standards. The concept of "green cloud computing" revolves around harnessing cutting-edge technologies to minimize the ...
Read More
Background and Objectives: Cloud computing can play a vital role in promoting environmental sustainability by leveraging eco-friendly dedicated servers that adhere to green computing standards. The concept of "green cloud computing" revolves around harnessing cutting-edge technologies to minimize the environmental footprint of computing systems. One of the significant challenges in cloud-based systems is task scheduling, which must be optimized to enhance system efficiency, user experience, and environmental sustainability.Method: This paper proposes a novel Hybrid HEES (Hierarchical Energy-Efficient Scheduling) method that optimizes energy consumption and task scheduling in cloud computing environments. By combining genetic algorithm optimization, workflow-based scheduling, and energy-aware resource allocation, HEES achieves significant reductions in energy consumption and average task completion time.Results: The method is evaluated through simulations, demonstrating its effectiveness in optimizing energy efficiency and task scheduling performance. The Hybrid HEES method has the potential to reduce energy consumption, improve computing performance, and enhance sustainability in cloud computing environments.Conclusion: To evaluate a proposed HEES method through cloudsim 3.0 simulations, the numerical results confirm the effectiveness of HEES algorithm, which achieves average Energy consumption performance improvements of around 12% compared to GP and 8% compared to RR existing methods.
Cloud Computing
M. Farmani; S. Farnam; M. J. Khani; Z. Torabi; Z. Shirmohammadi
Abstract
Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of ...
Read More
Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture using the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, processing delay. Computational paradigm is a key solution to solve the problems of time delay, security, storage space management, real-time analysis. Computing paradigms include cloud, fog and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications.Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges.Results: Computational paradigms have significant advantages. Combining these paradigms with each other in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture.Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges.
Cloud Computing
H. Jahanpour; H. Barati; A. Mehranzadeh
Abstract
Background and Objectives: Cloud Computing has brought a new dimension to the IT world. The technology of cloud computing allows employing a large number of Virtual Machines to run intensive applications. Each failure in running applications fails system operations. To solve the problem, it is required ...
Read More
Background and Objectives: Cloud Computing has brought a new dimension to the IT world. The technology of cloud computing allows employing a large number of Virtual Machines to run intensive applications. Each failure in running applications fails system operations. To solve the problem, it is required to restart the systems.Methods: In this paper, to predict and avoid failure in HPC systems, a method of fault tolerance to High-Performance Computing systems (HPC) in the cloud is called Daemon-COA-MMT (DCM), has been proposed. In the proposed method, the Daemon Fault Tolerance technique has been enhanced, and COA-MMT has been utilized for load balancing. The method consists of four modules, which are used to determine the host state. When the system is in the alarm state, the current host may face failure. Then the most optimal host for migration is selected, and process-level migration is performed. The method causes decreased migration overheads, decreased system performance speed, optimal use of underutilized hosts instead of leasing new hosts, appropriate load balancing, equal use of hardware resources by all hosts, focusing on QoS and SLA, and the significant decrease of energy consumption.Results: The simulation results revealed that in terms of parameters, the proposed method declines average job makespan, average response time, and average task execution cost by 18.06%, 35.68%, and 24.6%, respectively. The proposed fault tolerance algorithm has improved energy consumption by 30% and decreased the HPC systems' failure rate.Conclusion: In this study, the Daemon Fault Tolerance technique has been enhanced, and COA-MMT has been utilized for load balancing in high performance computing in the cloud computing.
