Power Load Monitoring has been a research hotspot since a few years ago. With development of artificial intelligence, construction of smart grid has become the most important part of power load monitoring. At the same time, task scheduling mechanism combined with the distributed IoT(Internet of Things) improves efficiency of smart grid. In this paper, applications of cloud/edge platform in the data acquisition, processing and scheduling of the IoT is introduced step by step, as well as applications and differences of artificial intelligence algorithm in each step, including Data Acquisition, Load Disaggregation, Load Forecasting and so on. Finally, combined with various optimization methods, future research directions are prospected, including data and network security issues, challenges faced by cloud/edge architecture, adaptive fine-grained Load Disaggregation, Load Forecasting.
In recent years, supply chain management has become an incrementally vital part of industrial sectors, which affects the service quality and production efficiency of relevant enterprises. With the rapid development of cloud computing technology, a distributed datacenter plays a vital role in supply chain management of many infrastructures but suffers from high energy consumption and low service efficiency due to heavy allocation of massive calculation and analysis tasks. In this paper, an efficient multi-swarm particle swarm optimization approach is proposed based on load balancing. Both makespan and completion time variances of all resources have been minimized. An initialization scheme is also presented concerning the convergence rate and adaptive inertia weights. An open dataset from an Alibaba datacenter has been employed to resolve the uneven load events caused by inefficient supply chain arrangement between distributed tasks and resources. Two criteria, makespan and response time during task scheduling have been chosen for performance evaluation. According to the results, the proposed work can improve task scheduling efficiency and sustainability in distributed datacenters supporting diverse supply chain environments.
With the development of cloud computing and data intelligence, datacenters have become an important part of ensuring service quality and production efficiency in intelligent applications. However, datacenters are also facing increasingly complex and heavy task processing requirements currently, and more efficient scheduling methods are urgently needed. Therefore, this paper proposes a multi-swarm particle swarm optimization task scheduling method based on load balancing, aiming at reducing the maximum completion time (makespan) and response time in task scheduling. The proposed method designs a new fitness function for particles, and promotes the load balance of the cluster during the scheduling process by optimizing the combination of makespan and machine completion time variance. And a novel inertia weight is designed to dynamically adjust the particle search performance. The new initialization method and multi-swarm search design are used to improve the quality and diversity of solutions and avoid particles falling into local optimum. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the algorithm can improve the task scheduling performance of datacenters in supply chain management when dealing with different workloads and changes in the number of machines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.