Today, cloud computing technology has attracted the attention of many researchers. According to the needs of users to quickly execute requests and provide quality services, optimal allocation of resources and timing o...
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Today, cloud computing technology has attracted the attention of many researchers. According to the needs of users to quickly execute requests and provide quality services, optimal allocation of resources and timing of task execution between virtual machines in cloud computing are of great importance. One of the important challenges that cloud service providers face is the effective management of resources by physical infrastructure. Therefore, in this paper, an autonomous system based on the Clipped Double Deep Q-Learning (CDDQL) Algorithm and the meta-heuristic Particle Swarm Optimization (PSO) for resource allocation is proposed in the Fog-cloud computing infrastructure. The PSO algorithm is used to prioritize the tasks and CDDQL is used as the core of the autonomous system (Auto-CDDQL) to allocate the desired VM resources to the tasks. The proposed Auto-CDDQL is implemented in the Fog and performs this process autonomously. By evaluating the results, it was observed that the amount of Make Span, response time, task completion, resource utilization, and energy consumption rate in the proposed AutoCDDQL on the c-hilo dataset, compared to the FCFS, RR, and PBTS methods, are significantly improved.
cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In th...
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cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual *** framework environment is used for the implementation. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud.
A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While c...
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A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%.
In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this researc...
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In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio.
In recent years, the development of IOTA, a new type of Distributed Ledger (DL) for internet of things (IoT), has gained significant attention. IOTA DL offers key features like scalability, fast and free transactions,...
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In recent years, the development of IOTA, a new type of Distributed Ledger (DL) for internet of things (IoT), has gained significant attention. IOTA DL offers key features like scalability, fast and free transactions, making it an optimal choice for IoT devices. However, a major concern with IOTA DL is its reliance on a single coordinator for transaction confirmation. This default coordinator introduces issues of single point of failure and incomplete distribution. To address these limitations, this paper proposes the Multiple Coordinator Selection (MCS) algorithm. MCS aims to overcome the problem by involving multiple coordinators in the consensus process. Four metrics, namely "trust level," "distance from input transactions," "node activity," and "transaction distribution," are defined as properties for coordinator selection. Additionally, a checklist is employed to minimize the probability of collusion within the system. Furthermore, the paper introduces a three-layered architecture based on cloud and fog computing, where the MCS algorithm is implemented. Experimental results demonstrate improved security and distribution of the system, while reducing the chances of collusion and single point of failure.
The digital economy constantly evolves and blockchain technology has become crucial in assessing risks. The security of the economic sector is of utmost importance, and blockchain offers improved security measures whi...
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The digital economy constantly evolves and blockchain technology has become crucial in assessing risks. The security of the economic sector is of utmost importance, and blockchain offers improved security measures while also introducing potential risks. This study conducted a thorough investigation and research on the risk assessment and analysis of blockchain and cloud computing services. It provides a comprehensive overview of the risks associated with blockchain and cloud computing, covering various aspects. This research offers a revolutionary blockchain-and cloud-based digital economic risk assessment system to solve these problems. The system employs blockchain technology to make risk assessment records and processes secure, immutable, and auditable. The algorithm used in blockchain mining is crucial for ensuring privacy and security, particularly in data access transactions. This study introduces differential privacy within the M-kCCIA and BC-PPkCA algorithms. These enhancements increase privacy and security in a digital economic risk assessment system. These advancements enable the public to utilize blockchain cloud services more efficiently and securely. The digital economic landscape within the blockchain ecosystem is complex. This study examines the industry's economic situation, investigates risk assessment, and compares the advantages of a blockchain-based digital assessment system. Furthermore, it evaluates the risks associated with the digital economy and provides corresponding analyses. The presented system uses cloud storage services to store assessment files and their hashes on various blockchains. That overcomes the challenges of blockchain's storage capacity and ensures data integrity in the system. Security and performance analysis demonstrate the efficacy and authenticity of the technique. A verifier can validate query results returned by blockchain nodes;it just needs to store block headers.
cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important diff...
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cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important difficulties because a solid mapping between available resources and users' activities is essential to reduce the execution time of users' jobs (i.e., minimize makespan) and maximize resource utilization. Because the service provider must offer several customers' benefits at distinct times and from distinct locations, task scheduling is indeed a serious challenge in CC. As a result, in the CC environment, these operations must be scheduled in a more dynamic and timely manner. The objective is to provide an enhanced task scheduling algorithm for allocating the task of the user to different computing resources. The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd-squirrel search algorithm (HO-SSA). The developed HO-SSA assists in introducing a multiobjective optimization for efficiently handling task scheduling issues in the cloud sector. The proposed HO-SSA method for the task scheduling in CC model in terms of cost is 22.22%, 15.73%, and 38.74% better than SSA, HOA, and TSA, respectively. Similarly, the proposed HO-SSA method for the task scheduling in CC model with respect to energy is 9.68%, 5.35%, and 22.50% superior to SSA, HOA, and TSA, respectively. The proposed method outperformed the existing methods like SSA, HOA, and TSA in terms of cost, energy, degree of imbalance, makespan, speedup, and efficiency.
In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of in...
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In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of internet users can get able to serve various kinds of services like on-demand access, storage space, software building platforms, data recovery, etc., and pay only for that service that they have consumed. Enormous challenges in the cloud domain such as fault tolerance, energy efficiency, scheduling, resource provisioning, load balancing, etc. This paper is focused on load balancing domain. This can be defined as a redistribution of the workload among various available virtual machines in such an identical manner that would lead to a balanced state. This paper presents the evaluative and inclusive review of numerous load balancing (LB) methods. Quality of services(QoS) is vital role that contain various parameters to evaluate the load balancing methods in respect of makespan, speedup, cost, throughput, etc. This paper is highlighted numerous of load balancing methods with their brief explaination, platform used, different simulator and tools used by these methods and based on QoS parameters.
In recent years, both artificial intelligence (AI) and cloud computing have experienced a boom across industries, including the sports industry. AI algorithms, machine learning, computer vision, cloud computing and na...
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In recent years, both artificial intelligence (AI) and cloud computing have experienced a boom across industries, including the sports industry. AI algorithms, machine learning, computer vision, cloud computing and natural language processing are utilized to extract valuable insights from sports data. This comprehensive survey examines the application of AI and cloud computing, highlights their potential to revolutionize the sports industry by enhancing athlete performance, strengthening injury prevention and rehabilitation, enriching fan experiences, and optimizing business operations. It also addresses challenges related to data privacy, security, transparency, and ethical considerations. The survey emphasizes the need for responsible AI adoption and highlights emerging trends such as edge computing, blockchain, and augmented reality (AR). The findings contribute to the understanding of AI's and cloud's transformative potential in sports and provide insights for researchers, practitioners, and stakeholders interested in leveraging AI and cloud computing to drive innovation and success in the sports industry.
cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computi...
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cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max-Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max-Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max-Min Algorithm outperforms the traditional Max-Min, Min-Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization.
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