The demand for task scheduling in Internet of Things (IoT)-based edge and cloud computing environments is experiencing exponential growth due to the need to address real-world issues, such as load instability, slow co...
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The demand for task scheduling in Internet of Things (IoT)-based edge and cloud computing environments is experiencing exponential growth due to the need to address real-world issues, such as load instability, slow convergence rates, and under-utilization of virtual machine devices. In this paper, a hybrid enhanced optimization method called RFOAOA is designed to solve challenging task scheduling scenarios in edge-cloud computing-based IoT environments. The proposed method leverages the strengths of two powerful search operators, such as Red Fox Optimization (RFO) and Arithmetic Optimization Algorithm (AOA). To evaluate the effectiveness of the proposed method, we conducted experiments on real and synthetic workload traces of NASA Ames iPSC/860 and HPC2N. The comparative analysis demonstrates that the proposed algorithm achieves better performance in terms of Makespan time and energy consumption and outperforms the other state-of-the-art scheduling methods.
In the context of the global energy ecosystem transformation, we introduce a new approach to reduce the carbon emissions of the cloud-computing sector and, at the same time, foster the deployment of small-scale privat...
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In the context of the global energy ecosystem transformation, we introduce a new approach to reduce the carbon emissions of the cloud-computing sector and, at the same time, foster the deployment of small-scale private photovoltaic plants. We consider the opportunity cost of moving some cloud services to private, distributed, solar-powered computing facilities. To this end, we compare the potential revenue of leasing computing resources to a cloud pool with the revenue obtained by selling the surplus energy to the grid. We first estimate the consumption of virtualized cloud computing instances, establishing a metric of computational efficiency per nominal photovoltaic power installed. Based on this metric and characterizing the site's annual solar production, we estimate the total return and payback. The results show that the model is economically viable and technically feasible. We finally depict the still many questions open, such as security, and the fundamental barriers to address, mainly related with a cloud model ruled by a few big players.
We consider a Multiaccess edgecomputing (MEC) network where distributed servers have energy harvesting (e.g., solar) and storage (e.g., batteries) capabilities. Energy from a connected power grid is also available, i...
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ISBN:
(纸本)9781509066315
We consider a Multiaccess edgecomputing (MEC) network where distributed servers have energy harvesting (e.g., solar) and storage (e.g., batteries) capabilities. Energy from a connected power grid is also available, in case that harvested from ambient sources is scarce or absent. Network processors are deployed according to a given network topology, across two tiers, and computing tasks are flexibly allocated depending on considerations related to load balancing, energy consumption (for communication and computing) and energy purchases from the power grid. Specifically, an on-line optimization problem, exploiting a predictive control approach, is formulated to minimize the monetary cost incurred in the energy purchases from the power grid, by dispatching the computation jobs to those servers that have enough energy and computation resources. Our proposed framework uses forecasts of exogenous processes, such as the amount of energy harvested and job arrivals, which are estimated on the fly to steer the allocation of computation jobs to the servers.
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