Asier Garmendia-Orbegozo¹, Jose David Nunez-Gonzalez^1,*, Miguel Angel Anton²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912

Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks… More > Graphic Abstract

Dinesh Mavaluru^1,*, Chettupally Anil Carie², Ahmed I. Alutaibi³, Satish Anamalamudi², Bayapa Reddy Narapureddy⁴, Murali Krishna Enduri², Md Ezaz Ahmed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… More >

Shengyao Sun^1,2, Ying Du³, Jiajun Chen⁴, Xuan Zhang⁵, Jiwei Zhang^6,*, Yiyi Xu⁷

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2879-2900, 2023, DOI:10.32604/cmc.2023.037483

Abstract In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks, it gives priority to non-key… More >

Yongxuan Sang, Jiangpo Wei^*, Zhifeng Zhang, Bo Wang

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2483-2502, 2023, DOI:10.32604/cmc.2023.040485

Abstract Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud computing. In response to these challenges, mobile edge computing (MEC) has emerged as a new paradigm that extends the computational, caching, and communication capabilities of cloud computing. By caching certain services on edge nodes, computational support can be provided for requests that are offloaded to the edges. However, previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by services. This oversight can lead to problems, such as high delays in task executions and invalidation of offloading decisions. To optimize… More >

Ilyоs Abdullaev¹, Natalia Prodanova², K. Aruna Bhaskar³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417

Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,… More >

Kaili Shao¹, Hui Fu¹, Ying Song², Bo Wang^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… 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 >

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 >

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 >