Proceedings ArticleDOI
An Energy-Aware Edge Server Placement Algorithm in Mobile Edge Computing
Yuanzhe Li,Shangguang Wang +1 more
- pp 66-73
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TLDR
This paper designs a particle swarm optimization based energy-aware edge server placement algorithm that can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.Abstract:
Edge server placement problem is a hot topic in mobile edge computing. In this paper, we study the problem of energy-aware edge server placement and try to find a more effective placement scheme with low energy consumption. Then, we formulate the problem as a multi-objective optimization problem and devise a particle swarm optimization based energy-aware edge server placement algorithm to find the optimal solution. We evaluate the algorithm based on the real dataset from Shanghai Telecom and the results show our algorithm can reduce more than 10% energy consumption with over 15% improvement in computing resource utilization, compared to other algorithms.read more
Citations
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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures
TL;DR: A detailed review of the security-related challenges and sources of threat in the IoT applications is presented and four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.
Journal ArticleDOI
Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective
TL;DR: A comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area and helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in M EC.
Journal ArticleDOI
Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization
TL;DR: Two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations.
Journal ArticleDOI
Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV
Xiaolong Xu,Bowen Shen,Xiaochun Yin,Mohammad Reza Khosravi,Huaming Wu,Lianyong Qi,Shaohua Wan +6 more
TL;DR: A collaborative method for the quantification and placement of ESs, named CQP, is developed for social media services in industrial CIoV, and is evaluated with a real-world ITS social media data set from China.
Journal ArticleDOI
A Survey of Virtual Machine Management in Edge Computing
TL;DR: The engineering and research trends of achieving efficient VM management in edge computing are introduced and the virtualization frameworks for edge computing developed in both the industry and the academia are elaborate.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Journal ArticleDOI
Edge Computing: Vision and Challenges
TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
Proceedings ArticleDOI
Power provisioning for a warehouse-sized computer
TL;DR: This paper presents the aggregate power usage characteristics of large collections of servers for different classes of applications over a period of approximately six months, and uses the modelling framework to estimate the potential of power management schemes to reduce peak power and energy usage.
Proceedings ArticleDOI
A comparison of particle swarm optimization and the genetic algorithm
TL;DR: This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency by implementing statistical analysis and formal hypothesis testing.