Yogesh Kumar^1,*, Jitender Kumar¹, Poonam Sheoran²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5785-5800, 2023, DOI:10.32604/cmc.2023.039276

Abstract On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains. Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’ cognitive capacity. However, the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines (VMs). Furthermore, any delays at the cloud end would further aggravate the miseries of real-time tasks. To resolve these issues, this paper proposes an auto-scaling framework (ACF) that strives to maintain the quality of service (QoS) for the… More >

Samah Ibrahim AlShathri, Dina S. M. Hassan^*, Samia Allaoua Chelloug

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1501-1526, 2023, DOI:10.32604/cmc.2023.035602

Abstract Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility… More >

Duan Xue^1,2, Yan Guo^1,*, Ning Li¹, Xiaoxiang Song¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 609-631, 2023, DOI:10.32604/cmc.2023.035177

Abstract The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud.… More >

Sanghyuck Nam¹, Suhwan Kwak¹, Jaehwan Lee², Sangoh Park^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 659-670, 2023, DOI:10.32604/csse.2023.034994

Abstract Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process computation tasks within 100 ms which… More >

R. Shanthakumari¹, Yun-Cheol Nam², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1991-2005, 2023, DOI:10.32604/iasc.2023.033696

Abstract Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The Attribute Decision Making with Capacitated… More >

Ahmad Naseem Alvi¹, Muhammad Awais Javed^1,*, Mozaherul Hoque Abul Hasanat², Muhammad Badruddin Khan², Abdul Khader Jilani Saudagar², Mohammed Alkhathami²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6321-6334, 2023, DOI:10.32604/cmc.2023.029913

Abstract Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes… More >

Alla Abbas Khadir¹, Seyed Amin Hosseini Seno^1,2,*, Baydaa Fadhil Dhahir^2,3, Rahmat Budiarto⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2223-2234, 2023, DOI:10.32604/csse.2023.032316

Abstract Fog computing became a traditional OffLad Destination (OLD) to compute the offloaded tasks of the Internet of Vehicles (IoV). Nevertheless, the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud. Thus, the IoV will incur additional offloading costs. In this paper, we propose a new offloading scheme by utilizing RoadSide Parked Vehicles (RSPV) as an alternative OLD for IoV. The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud. Finally, a performance evaluation of the proposed scheme… More >

Abdelhamied A. Ateya¹, Amel Ali Alhussan^2,*, Hanaa A. Abdallah³, Mona A. Al duailij², Abdukodir Khakimov⁴, Ammar Muthanna⁵

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031841

Abstract Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration… More >

Prohim Tam¹, Sa Math¹, Seokhoon Kim^1,2,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2141-2154, 2023, DOI:10.32604/csse.2023.030984

Abstract Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to… More >

Jyoti Yadav^*, Suman

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1063-1078, 2023, DOI:10.32604/iasc.2023.032883

Abstract Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the… More >