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An online algorithm for Inference Service Scheduling Using Combinations of Server-Based and Serverless Instances in Cloud Environments

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 11153-11165页

作者： Liu, Siyuan Pan, Li Liu, Shijun Qi, Kaiyuan Shandong Univ Sch Software Jinan 250101 Peoples R China Jinan Inspur Data Technol Co Ltd Jinan 250101 Peoples R China

With the continuous development in the field of machine learning, there is an increasing demand for the cloud-based machine learning inference services, which are latency-sensitive tasks, such as the service requests from the Internet of Things (IoT) devices. These inference services are generally accompanied by fluctuations and uncertainties, so they often require vastly varied numbers of servers at different time spots. As a result, how to dynamically and rationally schedule cloud servers for inference services has become an important issue. Alibaba Cloud currently provides its serverless instances called elastic container instance (ECI), and due to the advantages of pay-as-you-go billing and second-level elasticity, they are well-suited for handling bursty or fluctuating workloads. At the same time, Alibaba Cloud's subscription-based elastic compute service (ECS) instances can be used for steady-state workloads. Our objective is to dynamically combine these two types of instances to deal with inference service requests. In this article, we propose a deterministic online algorithm that can rationally schedule these two types of instances to optimize costs without requiring knowledge of future workloads. We prove that the proposed online algorithm achieves a competitive ratio of no more than 2 compared to the optimal offline algorithm. Through simulation experiments, we demonstrate that our algorithm outperforms three benchmarks, which are all-reserved algorithm, all-on-demand algorithm, and traditional online algorithms that only use ECS instances. Our algorithm exhibits superiority across various workloads and can significantly reduces costs in most cases.

关键词： Cloud computing Costs Pricing Inference algorithms Servers Optimization Machine learning Internet of Things Scheduling Prediction algorithms competitive analysis cost management inference service online algorithm

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online algorithm for Deriving Heart Rate Variability Components and Their Time-Frequency Analysis

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1210-1210页

作者： Adamczyk, Krzysztof Polak, Adam G. Wroclaw Univ Sci & Technol Fac Elect Photon & Microsyst PL-50372 Wroclaw Poland

Featured Application This work makes possible to extract heart rate variability components online in order to monitor the underlying human body systems, in particular to determine the activity of the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) as well as the balance between them, and indirectly in detecting and monitoring many common diseases related to the cardiovascular system and ANS. Such a solution can, for example, be directly embedded into Holter devices. A more precise determination of the components' properties can give the opportunity to link them to specific physiological processes, especially those of very low and ultra-low frequencies, which has not yet been fully achieved, increasing the practical importance of this *** Heart rate variability (HRV) containing four components of high (HF), low (LF), very low (VLF), and ultra-low (ULF) frequencies provides insight into the cardiovascular and autonomic nervous system functions. Classical spectral analysis is most often used in research on HRV and its components. The aim of this work was to develop and validate an online HRV decomposition algorithm for monitoring the associated physiological processes. The online algorithm was developed based on variational mode decomposition (VMD), validated on synthetic HRV with known properties and compared with its offline adaptive version AVMD, standard VMD, continuous wavelet transform (CWT), and wavelet package decomposition (WPD). Finally, it was used to decompose 36 real all-night HRVs from two datasets to analyze the properties of the four extracted components using the Hilbert transform. The statistical tests confirmed that the online VMD (VMDon) algorithm returned results of comparable quality to AVMD and CWT, and outperformed standard VMD and WPD. VMDon, AVMD, and CWT extracted four components from the real HRV with frequency content slightly exceeding the previously recognized ranges, suggesting the possibility

关键词： heart rate variability variational mode decomposition online algorithm amplitude and frequency modulation

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online algorithm of blind source separation based on conjugate gradient method

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2006年第3期25卷 381-388页

作者： Shen, XZ Shi, XZ Meng, G Shanghai Jiao Tong Univ Natl Key Lab Vibrat Shock & Noise Shanghai 200030 Peoples R China Shanghai Inst Technol Mech & Automat Dept Shanghai 200235 Peoples R China

A novel online algorithm is proposed for blind source separation based on the conjugate gradient method. The probability density function is first estimated using a Gram-Charlier expansion, and then the score function is calculated to form the algorithm. The conjugate gradient method is then used in the novel algorithm, and the line search method is applied to find the best learning rate. Simulation and comparison show the algorithm's ability to perform the separation even with an ill-conditioned mixed matrix.

关键词： blind source separation conjugate gradient method online algorithm Gram-Charlier expansion

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online algorithm for Optimal Real-Time Energy Distribution in the Smart Grid

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2013年第1期1卷 10-21页

作者： Wang, Yu Mao, Shiwen Nelms, R. M. Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

The two-way energy and information flows in a smart grid, together with the smart devices, bring new perspectives to energy management and demand response. This paper investigates an online algorithm for electricity energy distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user's utility and cost, grid load smoothing, dynamic pricing, and energy provisioning cost. The problem is shown to be convex and can be solved with an offline algorithm if future user and grid related information are known a priori. We then develop an online algorithm that only requires past and present information about users and the grid, and prove that the online solution is asymptotically optimal. The proposed energy distribution framework and the online algorithm are quite general, suitable for a wide range of utility, cost, and pricing functions. It is evaluated with trace -driven simulations and shown to outperform a benchmark scheme.

关键词： Convex optimization demand response electricity scheduling online algorithm smart grid

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online algorithm for Opportunistic Handling of Received Packets in Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第1期20卷 285-296页

作者： Al-Fuqaha, Ala Gharaibeh, Ammar Mohammed, Ihab Hussini, Sayed Jahed Khreishah, Abdallah Khalil, Issa Western Michigan Univ Dept Comp Sci Kalamazoo MI 49008 USA German Jordanian Univ Dept Comp Engn Amman 11180 Jordan New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha Qatar

In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast;sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.

关键词： VANET online algorithm packet scheduling

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An online algorithm Based on Replication for Using Spot Instances in IaaS Clouds

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Journal of Computer Science & Technology 2024年第1期39卷 103-115页

作者：许志伟潘丽刘士军 School of Software Shandong UniversityJinan 250101China

Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying *** can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant ***,users have to carefully weigh the low cost of spot instances against their poor *** instances will be revoked when the revocation event ***,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk *** on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational *** prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a *** a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.

关键词： Infrastructure-as-a-Service(IaaS)cloud cost management competitive analysis online algorithm spot instance

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Distributed online algorithm for Optimal Real-Time Energy Distribution in the Smart Grid

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IEEE INTERNET OF THINGS JOURNAL 2014年第1期1卷 70-80页

作者： Wang, Yu Mao, Shiwen Nelms, R. M. Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

In recent years, the smart grid has been recognized as an important form of the Internet of Things (IoT). The two-way energy and information flows in a smart gird, together with the smart devices, bring about new perspectives to energy management. This paper investigates a distributed online algorithm for electricity distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user's utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with a centralized online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm that decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and mitigates the user privacy issue by not sharing user utility functions. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.

关键词： Convex optimization demand response distributed algorithm Enernet online algorithm smart grid

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online algorithm for Clustering with Capacity Constraints 24

Online Algorithm for Clustering with Capacity Constraints

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7th ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD) / 11th ACM IKDD CODS Conference / 29th COMAD Conference

作者： Gupta, Shivam Jain, Shweta Krishnan, Narayanan C. Ghalme, Ganesh Hemachandra, Nandyala IIT Ropar Rupnagar Punjab India IIT Hyderabad Hyderabad Andhra Pradesh India IIT Palakkad Palakkad Kerala India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9798400716348

Traditional clustering often results in imbalanced clusters, limiting its suitability for real-world problems. In response, capacitated clustering methods have emerged, aiming to achieve balanced clusters by limiting points in each cluster. In this paper, we introduce on-line algorithms with provable bounds on opened centers and cost approximation. We validate our methods experimentally.

关键词： Unsupervised Learning Capacitated Clustering online algorithm

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An Optimal online algorithm for Fractional Scheduling on Uniform Machines with Three Hierarchies

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Journal of Systems Science & Complexity 2016年第6期29卷 1650-1657页

作者： LU Xinrong LIU Zhaohui Department of Mathematics East China University of Science and Technology

This paper is concerned with the fractional version of online hierarchical scheduling problem on uniform *** the problem,the jobs and machines have several different hierarchies and each job can be arbitrarily split between the machines with hierarchies not above the hierarchy of the *** objective is to minimize the *** authors present an optimal algorithm for the problem with three hierarchies.

关键词： Fractional scheduling hierarchical scheduling online algorithm uniform machine.

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online algorithm for application layer anycast 4

Online algorithm for application layer anycast

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4th International Conference on Active Media Technology

作者： Yu, Shui Li, Zhongwen Liu, Bin Deakin Univ Sch Engn & Informat Technol Melbourne Vic 3125 Australia

ISBN: (纸本)9781586036157

Application layer anycast possesses the property of flexibility, however, all the proposed application layer anycast routing algorithms are based on probing so far. One disadvantage of probing algorithms is that there are too many probing packets, which degrade the network performance, wast network bandwidth. In this paper, we propose an online algorithm, balance algorithm, for application layer anycast. Compared with the probing algorithms, the proposed algorithm has no probing cost at all. We model the two kinds of algorithms, and analyse the performance of the two algorithms. The results show that the online balance algorithm is better than the probing algorithms in terms of performance. A simulation is conducting as future work.

关键词： online algorithm anycast queue performance

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