Darakhshan Syed^*, Ghulam Muhammad, Safdar Rizvi

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 437-476, 2024, DOI:10.32604/iasc.2024.050681

Abstract Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. More >

Meignanamoorthi Dhandapani^*, V. Vetriselvi, R. Aishwarya

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2449-2486, 2024, DOI:10.32604/cmes.2024.050986

Abstract The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale. Network slicing is crucial in delivering services for different, demanding vertical applications in this context. Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations. However, the existing IP (Internet Protocol) over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators. Conventional inter-domain routing methods, like Border Gateway Protocol (BGP), cannot make routing decisions based on performance,… More >

Yu Song¹, Xusheng Qian², Nan Zhang³, Wei Wang², Ao Xiong^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3007-3021, 2024, DOI:10.32604/cmc.2024.051217

Abstract To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront the challenge of managing the surging demand for data traffic. Within this realm, the network imposes stringent Quality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanisms in accommodating such extensive data flows. In response to the imperative of handling a substantial influx of data requests promptly and alleviating the constraints of existing technologies and network congestion, we present an architecture for QoS routing optimization with in Software Defined Network (SDN), leveraging deep reinforcement learning. This… More >

Shiyan Zhang^1,*, Ruohan Xu², Zhangbo Xu³, Cenhua Yu¹, Yuyang Jiang¹, Yuting Zhao⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2287-2308, 2024, DOI:10.32604/cmc.2023.046550

Abstract The 6th generation mobile networks (6G) network is a kind of multi-network interconnection and multi-scenario coexistence network, where multiple network domains break the original fixed boundaries to form connections and convergence. In this paper, with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness, this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration. Due to the conflict between the utility of different flows, the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward… More >

Shimaa M. Amer¹, Ashraf A. M. Khalaf², Amr H. Hussein^3,4, Salman A. Alqahtani⁵, Mostafa H. Dahshan⁶, Hossam M. Kassem^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138

Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s… More >

S. Priya¹, R. S. Ponmagal^2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1895-1907, 2023, DOI:10.32604/iasc.2023.039814

Abstract Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks. To address such threats towards cloud services, numerous techniques exist that mitigate the service threats according to different metrics. The rule-based approaches are unsuitable for new threats, whereas trust-based systems estimate trust value based on behavior, flow, and other features. However, the methods suffer from mitigating intrusion attacks at a higher rate. This article presents a novel Multi Fractal Trust Evaluation Model (MFTEM) to overcome these deficiencies. The… More >

Wenlong Ma^1,2,*, Youhong Xu¹, Jianwei Zheng², Sadaqat ur Rehman³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1499-1512, 2023, DOI:10.32604/iasc.2023.030484

Abstract In a cloud manufacturing environment with abundant functionally equivalent cloud services, users naturally desire the highest-quality service(s). Thus, a comprehensive measurement of quality of service (QoS) is needed. Optimizing the plethora of cloud services has thus become a top priority. Cloud service optimization is negatively affected by untrusted QoS data, which are inevitably provided by some users. To resolve these problems, this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms. Untrusted data are assessed by an information correction method. The weights discovered by the variable precision Rough Set, More >

Wan Norsyafizan W. Muhamad¹, Kaharudin Dimyati², Muhammad Awais Javed³, Suzi Seroja Sarnin^1,*, Divine Senanu Ametefe¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1291-1308, 2023, DOI:10.32604/cmc.2023.037214

Abstract The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different… More >

G. Manimala^*, A. Chinnasamy

Computer Systems Science and Engineering, Vol.47, No.1, pp. 393-412, 2023, DOI:10.32604/csse.2023.037066

Abstract Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service (SaaS). Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources. In this context, the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests, as the services stored over the cloud are too complex and scalable. To achieve better service composition, the parameters of Quality of Service (QoS) related to each service considered to be… More >

A. Richard William^1,*, J. Senthilkumar², Y. Suresh², V. Mohanraj²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with… More >