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 method involves analyzing service growth,… 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, which mined the evaluation indicators… 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 applications within the IoT network.… 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 the best resource need to… 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 feature selection (BFS) in the… More >

Y. M. Mahaboob John^1,*, G. Ravi²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3653-3666, 2023, DOI:10.32604/csse.2023.036916

Abstract Mobile Ad-hoc Network (MANET) routing problems are thoroughly studied several approaches are identified in support of MANET. Improve the Quality of Service (QoS) performance of MANET is achieving higher performance. To reduce this drawback, this paper proposes a new secure routing algorithm based on real-time partial ME (Mobility, energy) approximation. The routing method RRME (Real-time Regional Mobility Energy) divides the whole network into several parts, and each node’s various characteristics like mobility and energy are randomly selected neighbors accordingly. It is done in the path discovery phase, estimated to identify and remove malicious nodes. In addition, Trusted Forwarding Factor (TFF)… More >

Ansar Munir Shah¹, Mohammed Aljubayri², Muhammad Faheem Khan¹, Jarallah Alqahtani^2,*, Mahmood ul Hassan³, Adel Sulaiman², Asadullah Shaikh²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2471-2488, 2023, DOI:10.32604/csse.2023.034951

Abstract In the network field, Wireless Sensor Networks (WSN) contain prolonged attention due to afresh augmentations. Industries like health care, traffic, defense, and many more systems espoused the WSN. These networks contain tiny sensor nodes containing embedded processors, Tiny OS, memory, and power source. Sensor nodes are responsible for forwarding the data packets. To manage all these components, there is a need to select appropriate parameters which control the quality of service of WSN. Multiple sensor nodes are involved in transmitting vital information, and there is a need for secure and efficient routing to reach the quality of service. But due… More >

G. John Samuel Babu¹, M. Baskar^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1547-1559, 2023, DOI:10.32604/cmc.2023.033300

Abstract Numerous methods are analysed in detail to improve task scheduling and data security performance in the cloud environment. The methods involve scheduling according to the factors like makespan, waiting time, cost, deadline, and popularity. However, the methods are inappropriate for achieving higher scheduling performance. Regarding data security, existing methods use various encryption schemes but introduce significant service interruption. This article sketches a practical Real-time Application Centric TRS (Throughput-Resource utilization–Success) Scheduling with Data Security (RATRSDS) model by considering all these issues in task scheduling and data security. The method identifies the required resource and their claim time by receiving the service… More >

S. J. Sangeetha^1,*, T. Rajendran²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1169-1181, 2023, DOI:10.32604/csse.2023.033392

Abstract Mobile Ad-hoc Networks (MANETs) connect numerous nodes to communicate data from the sender node to the target node. Due to the lack of an infrastructure network, mobile nodes communicate through wireless without an access point. MANET does not have a centralized controller and has a dynamic network topology, which increases link failure and energy consumption resulting in excessive path delay, loss of Quality of service (QoS), and reduced throughput during data communication. Congestion is a significant problem when the QoS of the link carrying the data is degraded. Routing is one of the vital challenges of MANET due to the… More >

Mahendrakumar Subramaniam^1,*, V. Vedanarayanan², Azath Mubarakali³, S. Sathiya Priya⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1603-1612, 2023, DOI:10.32604/iasc.2023.032396

Abstract Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS… More >