Article
Computer Science, Hardware & Architecture
Juan Carlos Salinas-Hilburg, Marina Zapater, Jose M. Moya, Jose L. Ayala
Summary: Energy-aware task scheduling approaches are crucial for improving energy savings in data centers, with the use of application signatures to estimate energy consumption without complete execution of applications. Different scheduling approaches can be combined with application signatures to optimize the makespan of the scheduling process and enhance energy savings, with high accuracy compared to oracle methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Automation & Control Systems
B. Gomathi, B. Saravana Balaji, V. Krishna Kumar, Mohamed Abouhawwash, Sultan Aljahdali, Mehedi Masud, Nina Kuchuk
Summary: This paper proposes a technique using multi-objective particle swarm optimization and composite mutation to improve energy efficiency in cloud environments and minimize service level agreement violations. By using the longest processing time rule, Epsilon Fuzzy Dominance technique, and discrete particle swarm optimization, better results can be achieved compared to existing algorithms.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Diana Andreea Popescu, Andrew W. Moore
Summary: Data-center-based cloud computing has transformed how businesses utilize computing infrastructure. To ensure application performance, we propose NoMora, a cluster scheduling architecture with a latency-driven, application-performance-aware policy. Leveraging the relationship between network latency and application performance, our architecture significantly enhances application performance.
Article
Automation & Control Systems
Tongxin Zhu, Zhipeng Cai, Xiaolin Fang, Junzhou Luo, Ming Yang
Summary: Edge-enabled Industrial Internet of Things (E-IIoT) has greatly improved the computation capacity and efficiency of IIoT networks. The correlation aware scheduling (CAS) algorithm proposed in this article effectively reduces latency by wisely scheduling computation resources.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Sudheer Mangalampalli, Ganesh Reddy Karri, Mohit Kumar, Osama Ibrahim Khalaf, Carlos Andres Tavera Romero, GhaidaMuttashar Abdul Sahib
Summary: Task scheduling in cloud paradigm is a challenging issue due to uncertainty, heterogeneity, and dynamic nature. Many heuristic approaches have been proposed, but scheduling multimedia tasks remains a challenge. To address this, a scheduling mechanism based on Deep Q-learning network model is proposed in this paper. Extensive simulations using Cloudsim toolkit were conducted, with results showing that the proposed scheduler DRLBTSA outperforms baseline algorithms in terms of makespan, SLA violations, and energy consumption.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Garima Singh, Anil Kumar Singh
Summary: Cloud computing attracts customers looking to reduce overall business costs, where trust and SLAs are crucial. Providers offer infrastructure-as-a-service through virtual machines. Migration of VMs requires minimizing total migration time and downtime to reduce penalties for SLA violations.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Mathematics
Cristina Sorina Stangaciu, Eugenia Ana Capota, Valentin Stangaciu, Mihai Victor Micea, Daniel Ioan Curiac
Summary: This paper introduces the concept of the Mixed-Criticality Internet of Things and presents a mathematical model and methodology based on real-time scheduling. The study also offers a model for setting task parameters and evaluates the effectiveness of the task scheduling algorithm.
Article
Computer Science, Information Systems
Abadhan Saumya Sabyasachi, Jogesh K. Muppala
Summary: The growing demand for cloud computing has raised concerns about energy usage and load balancing. A proposed algorithm optimizes virtual machine resources to address these issues effectively.
Article
Computer Science, Information Systems
Xiaoping Li, Wei Yu, Ruben Ruiz, Jie Zhu
Summary: This article proposes an energy-aware workflow scheduling algorithm to minimize the total electricity cost in geographically distributed data centers. Strategies are developed to sequence workflow applications, divide deadlines, and sort tasks. An adaptive local search method is used to improve solutions. Experimental results demonstrate the effectiveness of the proposed algorithm for the problem considered.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Hanlong Liao, Xinyi Li, Deke Guo, Wenjie Kang, Jiangfan Li
Summary: Mobile-edge computing (MEC) has experienced rapid growth in fulfilling the low-latency requirements of applications on end devices. This article introduces a novel method named Daas, which models the dependencies among tasks of an application and optimizes the assignment and scheduling to improve application execution. Experimental results demonstrate that Daas outperforms other methods, enabling more applications to meet their deadlines.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Mallu Shiva Rama Krishna, Sudheer Mangalampalli
Summary: Task scheduling in cloud computing faces various challenges, with the avoidance of single points of failure being the most crucial. This study proposes a method using deep reinforcement learning to calculate task priorities based on unit electricity cost, improving fault tolerance and reducing downtime. The results show that this approach outperforms other algorithms in minimizing makespan, reducing failure rates, and saving energy.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Theory & Methods
Long Cheng, Ying Wang, Qingzhi Liu, Dick H. J. Epema, Cheng Liu, Ying Mao, John Murphy
Summary: Large data centers serve as the mainstream infrastructures for big data processing, with challenges in the efficient execution of distributed data operators. Current methods focus on either application-layer data locality optimization or network-layer data flow optimization independently. The NEAL approach bridges this gap and aims to reduce communication time for distributed big data operators.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Mehboob Hussain, Lian-Fu Wei, Amir Rehman, Fakhar Abbas, Abid Hussain, Muqadar Ali
Summary: The increasing concern over the energy cost of cloud data centers has led to the urgent problem of minimizing energy cost. This paper proposes a DEWS algorithm that addresses the challenge of scheduling workflow tasks in geographically distributed data centers to minimize energy costs. The algorithm includes task sequencing, data center searches, task sequence adjustment, and VM searching with DVFS, resulting in a 5%-20% reduction in energy cost.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Information Systems
Fatemeh Shabestari, Amir Masoud Rahmani, Nima Jafari Navimipour, Sam Jabbehdari
Summary: This paper proposes an energy-efficient deadline-aware model for scheduling in Hadoop. The algorithm, based on the Moth-Flame Optimization algorithm, minimizes energy consumption and executes applications within a soft deadline. Experimental results demonstrate that this method outperforms other scheduling algorithms.
JOURNAL OF GRID COMPUTING
(2022)
Article
Computer Science, Information Systems
Jianmin Li, Ying Zhong, Shunzhi Zhu, Yongsheng Hao
Summary: This paper examines an energy-aware heuristic for service composition (EASC) in a multi-Cloud environment to reduce energy consumption caused by executing atomic services. By composing services in one cloud to minimize file transfer energy consumption between atomic services and considering the influence of split-point positions, the proposed method demonstrates good performance in reducing execution time and energy consumption.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Zihang Zhen, Xiaoding Wang, Hui Lin, Sahil Garg, Prabhat Kumar, M. Shamim Hossain
Summary: In this paper, a blockchain architecture based on dynamic state sharding (DSSBD) is proposed to solve the problems caused by cross-shard transactions and reconfiguration. By utilizing deep reinforcement learning, the number of shards, block spacing, and block size can be dynamically adjusted to improve the performance of the blockchain. The experimental results show that the crowdsourcing system with DSSBD has better performance in terms of throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, while ensuring security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Gabriel F. C. de Queiroz, Jose F. de Rezende, Valmir C. Barbosa
Summary: Multi-access Edge Computing (MEC) is a technology that enables faster task processing at the network edge by deploying servers closer to end users. This paper proposes the FlexDO algorithm to solve the DAG application partitioning and offloading problem, and compares it with other solutions to demonstrate its superior performance in various test scenarios.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shahid Latif, Wadii Boulila, Anis Koubaa, Zhuo Zou, Jawad Ahmad
Summary: In the field of Industrial Internet of Things (IIoT), networks are increasingly vulnerable to cyberattacks. This research introduces an optimized Intrusion Detection System based on Deep Transfer Learning (DTL) for heterogeneous IIoT networks, combining Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and ensemble techniques. Through rigorous evaluation, the framework achieves exceptional performance and accurate detection of various cyberattacks.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Rongji Liao, Yuan Zhang, Jinyao Yan, Yang Cai, Narisu Tao
Summary: This paper proposes a joint control approach called STOP to guarantee user-perceived deadline using curriculum-guided deep reinforcement learning. Experimental results show that the STOP scheme achieves a significantly higher average arrival ratio in NS-3.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Miguel Rodriguez-Perez, Sergio Herreria-Alonso, J. Carlos Lopez-Ardao, Raul F. Rodriguez-Rubio
Summary: This paper presents an implementation of an active queue management (AQM) algorithm for the Named-Data Networking (NDN) architecture and its application in congestion control protocols. By utilizing the congestion mark field in NDN packets, information about each transmission queue is encoded to achieve a scalable AQM solution.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Angel Canete, Mercedes Amor, Lidia Fuentes
Summary: This paper proposes an energy-aware placement of service function chains of Virtual Network Functions (VNFs) and a resource-allocation solution for heterogeneous edge infrastructures. The solution has been integrated with an open source management and orchestration project and has been successfully applied to augmented reality services, achieving significant reduction in power consumption and ensuring quality of service compliance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Sachin Kadam, Kaustubh S. Bhargao, Gaurav S. Kasbekar
Summary: This paper discusses the problem of estimating the node cardinality of each node type in a heterogeneous wireless network. Two schemes, HSRC-M1 and HSRC-M2, are proposed to rapidly estimate the number of nodes of each type. The accuracy and efficiency of these schemes are proven through mathematical analysis and simulation experiments.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Jean Nestor M. Dahj, Kingsley A. Ogudo, Leandro Boonzaaier
Summary: The launch of commercial 5G networks has opened up opportunities for heavy data users and highspeed applications, but traditional monitoring and evaluation techniques have limitations in the 5G networks. This paper presents a cost-effective hybrid analytical approach for detecting and evaluating user experience in real-time 5G networks, using statistical methods to calculate the user quality index.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O. Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Summary: The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. This study introduces a resource allocation framework that leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to improve energy efficiency. Through the use of a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG), the proposed approach optimizes user association, cache design, and transmission power control, resulting in enhanced energy efficiency and reduced time delays compared to existing methods.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Wu Chen, Jiayi Zhu, Jiajia Liu, Hongzhi Guo
Summary: With advancements in technology, large-scale drone swarms will be widely used in commercial and military fields. Current application methods are mainly divided into autonomous methods and controlled methods. This paper proposes a new framework for global coordination through local interaction.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Peiying Zhang, Zhihu Luo, Neeraj Kumar, Mohsen Guizani, Hongxia Zhang, Jian Wang
Summary: With the development of Industry 5.0, the demand for network access devices is increasing, especially in areas such as financial transactions, drone control, and telemedicine where low latency is crucial. However, traditional network architectures limit the construction of low-latency networks due to the tight coupling of control and data forwarding functions. To overcome this problem, researchers propose a constraint escalation virtual network embedding algorithm assisted by Graph Convolutional Networks (GCN), which automatically extracts network features and accelerates the learning process to improve network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Review
Computer Science, Hardware & Architecture
P. Anitha, H. S. Vimala, J. Shreyas
Summary: Congestion control is crucial for maintaining network stability, reliability, and performance in IoT. It ensures that critical applications can operate seamlessly and that IoT devices can communicate efficiently without overwhelming the network. Congestion control algorithms ensure that the network operates within its capacity, preventing network overload and maintaining network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shunmugapriya Ramanathan, Abhishek Bhattacharyya, Koteswararao Kondepu, Andrea Fumagalli
Summary: This article presents an experiment that achieves live migration of a containerized 5G Central Unit module using modified open-source migration software. By comparing different migration techniques, it is found that the hybrid migration technique can reduce end-user service recovery time by 36% compared to the traditional cold migration technique.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Fatma Foad Ashrif, Elankovan A. Sundararajan, Rami Ahmad, Mohammad Kamrul Hasan, Elaheh Yadegaridehkordi
Summary: This article introduces the development and current status of authentication protocols in 6LoWPAN, and proposes an innovative perspective to fill the research gap. The article comprehensively surveys and evaluates AKA protocols, analyzing their suitability in wireless sensor networks and the Internet of Things, and proposes future research directions and issues.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Pranjal Kumar Nandi, Md. Rejaul Islam Reaj, Sujan Sarker, Md. Abdur Razzaque, Md. Mamun-or-Rashid, Palash Roy
Summary: This paper proposes a task offloading policy for IoT devices to a mobile edge computing system, aiming to balance device utility and execution cost. A meta heuristic approach is developed to solve the offloading problem, and the results show its potential in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)