Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27\% compared to baseline algorithms

Analysis of vibration traits of underwater vehicle propulsion shafting and optimization design of support parameters

In this paper, the calculation model of the propulsion shafting structure was established to solve the problem of flexural vibration of the shafting system for the underwater vehicle with relatively small scale. By using the transfer matrix method and the finite element method, the vibration characteristics of the shafting system subjected to the transverse unsteady excitation force were calculated by MATLAB software and ABAQUS software. Two aspects of the displacement response and the vibration power flow were analyzed and compared. Analysis showed that the results of the two methods were very close to each other and all met the requirements of vibration engineering calculation. The influence of the mass of propeller and the bearing stiffness in different positions on the vibration characteristics were analyzed by using the transfer matrix method. Finally, based on the transfer matrix method, the parameters of the bearing stiffness at different supports were optimized with design optimization, and then use ABAQUS software to verify the effectiveness of the optimization. The analysis results showed that, after optimization calculation, the vibration power flow input to the bases of different bearings were significantly decreased

Efficient Serverless Function Scheduling at the Network Edge

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, previous studies have not studied well the issues of significant cold start latency and highly dynamic workloads in serverless function scheduling, which are exacerbated at the resource-limited network edge. In this paper, we formulate the Serverless Function Scheduling (SFS) problem for resource-limited edge computing, aiming to minimize the average response time. To efficiently solve this intractable scheduling problem, we first consider a simplified offline form of the problem and design a polynomial-time optimal scheduling algorithm based on each function's weight. Furthermore, we propose an Enhanced Shortest Function First (ESFF) algorithm, in which the function weight represents the scheduling urgency. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with dynamic workloads, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings

SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding

Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format. Their performances on NLU tasks are highly related to prompts or demonstrations and are shown to be poor at performing several representative NLU tasks, such as event extraction and entity typing. To this end, we present SeqGPT, a bilingual (i.e., English and Chinese) open-source autoregressive model specially enhanced for open-domain natural language understanding. We express all NLU tasks with two atomic tasks, which define fixed instructions to restrict the input and output format but still ``open'' for arbitrarily varied label sets. The model is first instruction-tuned with extremely fine-grained labeled data synthesized by ChatGPT and then further fine-tuned by 233 different atomic tasks from 152 datasets across various domains. The experimental results show that SeqGPT has decent classification and extraction ability, and is capable of performing language understanding tasks on unseen domains. We also conduct empirical studies on the scaling of data and model size as well as on the transfer across tasks. Our model is accessible at https://github.com/Alibaba-NLP/SeqGPT.Comment: Initial version of SeqGP

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

Abstract End-of-Life (EOL) product recovery is proved to be an attractive way to achieve sustainable manufacturing while extending the producer’s responsibility to closed-loop product service. However, it is still a challenge to provide flexible and smart recovery plans for industrial equipment at different periods of product service. In this paper, we investigate the smart recovery decision-making problem. We propose a system framework for the implementation of smart EOL management based on product condition monitoring. Different product-level EOL business strategies and component-level recovery options are suggested in this recovery decision support system. Then, multi-objective optimization models are formulated to identify the age-dependent recovery roadmap that best matches the product condition and meets the business goals. In order to achieve environmentally friendly recovery, both recovery profits and energy performances are optimized in our models. We conduct a case study of belt lifter used in the automobile assembly line. The Non-dominated Sorting Genetic Algorithm II is used to solve the proposed model. Numerical experiments validate our models and provide practical insights into flexible recovery business

Efficient electrochemical reduction of CO2 to HCOOH over Sub-2 nm SnO2 quantum wires with exposed grain boundaries

Electrochemical reduction of CO2 could mitigate environmental problems originating from CO2 emission. Although grain boundaries (GBs) have been tailored to tune binding energies of reaction intermediates and consequently accelerate the CO2 reduction reaction (CO2 RR), it is challenging to exclusively clarify the correlation between GBs and enhanced reactivity in nanostructured materials with small dimension (<10 nm). Now, sub-2 nm SnO2 quantum wires (QWs) composed of individual quantum dots (QDs) and numerous GBs on the surface were synthesized and examined for CO2 RR toward HCOOH formation. In contrast to SnO2 nanoparticles (NPs) with a larger electrochemically active surface area (ECSA), the ultrathin SnO2 QWs with exposed GBs show enhanced current density (j), an improved Faradaic efficiency (FE) of over 80 % for HCOOH and ca. 90 % for C1 products as well as energy efficiency (EE) of over 50 % in a wide potential window; maximum values of FE (87.3 %) and EE (52.7 %) are achieved.NRF (Natl Research Foundation, S’pore)Accepted versio