An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks

Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

/pdf/computation-offloading-in-mobile-cloud-computing-and-mobile-21hjlajj.pdf

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. 

pdf/modern-computing-vision-and-challenges-n2vf4r8em1.pdf

Modern computing: Vision and challenges

Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

An Energy-Efficient Data Offloading Strategy for 5G-Enabled Vehicular Edge Computing Networks Using Double Deep Q-Network

A survey of research on computation offloading in mobile cloud computing

Optimal deployment of mobile cloudlets for mobile applications in edge computing

Fifth generation (5G) new radio (NR) signals present unique opportunities for users and devices localization due to their higher bandwidth and more antenna elements. We propose an efficient deep learning-based end-to-end algorithm for 5G or beyond 5G (B5G) positioning. Both the channel geometric parameters and complex impulse response are utilized to exploit the connection between propagation channel and environment. A new multifeature fusion network architecture is proposed. Simulation results and complexity analysis are provided to demonstrate the efficacy of the proposed algorithm.

A Deep Learning-Based End-to-End Algorithm for 5G Positioning

Resource utilization and cost optimization oriented container placement for edge computing in industrial internet

Evolution algorithm (EA) is an attractive way for community detection (CD) on complex networks since it does not require any prior knowledge about the community structure, which is usually not available for real-world networks. The differential evolution (DE) is claimed as one of the most powerful EA currently in use for real-parameter optimization. One fundamental issue of EA is how to encode a problem solution into an evolution individual. In this paper, the indexed locus-based adjacency representation (ILAR) for evolution individual encoding in CD problem is proposed. As a result, a CD problem could be converted to a discrete integer optimization problem. Based on the ILAR, a community detection algorithm using DE as the search engine is developed. A number of experiments are conducted on synthesized and real-world networks, and the results show that the proposed DE based algorithm is very competitive for community detection as the community structure of a network is sound.

A community detection algorithm using differential evolution

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