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An online algorithm for combined computing workload and energy coordination within a regional data center cluster

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Huang, Shihan Yan, Dongxiang Chen, Yue Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction -free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time -coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well -designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks.

关键词： Data center Lyapunov optimization Energy sharing Distributed coordination online algorithm

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Stochastic Dynamic Programming-Based online algorithm for Energy Management of Integrated Energy Buildings With Electric Vehicles and Flexible Thermal Loads

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IEEE ACCESS 2021年 9卷 58780-58789页

作者： Cheng, Renli Cheng, Weijie Li, Jie Chen, Zeqi Shi, Jun Pan, Zhenning Wu, Yufeng Yu, Tao Shenzhen Power Supply Bur Co Ltd Shenzhen 518000 Peoples R China South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China

With the rapid development of economy and technology, large-scale integrated energy buildings account for an increasing proportion of urban load. However, the randomness of EV owner behaviors, electricity price and outdoor temperature have brought challenges to the energy management of integrated energy buildings. This paper proposes a stochastic dynamic programming-based online algorithm to address the energy management of integrated energy buildings with electric vehicles and flexible thermal loads under multivariate uncertainties. First, an online energy management framework is established, which is further formulated as a multi-stage stochastic sequential decision-making problem. To address the complexities of the problem, a novel stochastic dynamic programming is employed to develop a distribution-free, computationally efficient, and scalable solution. By using extensive training samples, the algorithm is trained offline to learn how to deal with multivariate uncertainties and get the approximate optimal solution, which no longer depends on intraday forecast information. Numerical tests demonstrate the effectiveness of the proposed algorithm compared with other online algorithms in terms of optimality and computation efficiency.

关键词： Buildings Resistance heating Uncertainty Heuristic algorithms Energy management Water heating Stochastic processes Stochastic dynamic programming online algorithm energy management integrated energy building multivariate uncertainties

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An online algorithm for the inventory retrieval problem with an uncertain selling duration, uncertain prices, and price-dependent demands

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COMPUTERS & OPERATIONS RESEARCH 2022年 148卷

作者： He, Xiaozhou Xiang, Jie Xiao, Jin Cheng, T. C. E. Tian, Yuhang Sichuan Univ Business Sch Chengdu 610065 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Kowloon Hong Kong Peoples R China Sichuan Univ Management Sci & Operat Res Inst Chengdu 610065 Peoples R China

We study a multi-period inventory retrieval problem with uncertain selling duration, uncertain future prices, and price-dependent demands. At the beginning of a finite selling horizon, a fixed amount of inventory is available for sale in future periods. In each period, the decision-maker observes the current market price and makes the retrieval decision, and only at the beginning of the last period, he knows that it is the last period. The objective is to maximize the expected total revenue and the difficulty stems from the lack of information on probability distributions of both the selling duration and future market prices, which also causes uncertainty in the random demands. Proposing an online algorithm ALG-IR to solve the problem based on competitive analysis theory, we derive its constant competitive ratio, which is optimal under certain conditions, showing the robust performance of the algorithm. We further indicate that, when applying other price-dependent demand models or considering the impact of reference prices on the current demand, the adjusted algorithm ALG-IR-M retains all the theoretical properties. When considering the holding cost of inventory, a similar algorithm ALG-IR-H is proposed to solve the problem. Through numerical studies, we show the algorithm achieves good performance in both optimality and robustness, especially when the market is not over-supplied or the selling duration will not suddenly end at an early period.

关键词： Inventory retrieval online algorithm Price uncertainty Duration uncertainty Perishable product

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Discrete/continuous-time online algorithm application for time-varying optimal power flow in active distribution networks

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 138卷 107859-107859页

作者： Xie, Kaijun Liu, Mingbo Lu, Wentian Wu, Jiexuan South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Guangdong Power Grid Co Foshan Power Supply Bur Foshan 528000 Peoples R China

This paper discusses and analyzes the latest online algorithm applications for time-varying optimal power flow problems in active distribution networks with numerous power-electronics-interfaced distributed energy resources, which bring significant time-varying characteristics to the optimal operation of active distribution networks. Conventional offline optimization algorithms are time-consuming and unable to track time-varying optimal solutions in highly variable environments. In contrast, online algorithms are characterized by fast calculation speeds and the ability to continuously track optimal solutions, thus having great potential in solving optimization problems that vary rapidly over time. In this paper, we design online time-varying optimal power flow algorithms based on the latest online algorithms, including three discrete-time algorithms (i.e., correction only, prediction-correction, and correction-extra correction algorithms) and one continuous-time algorithm (i.e., prediction-correction interior-point method). According to our survey of the current applications of online algorithms, this study represents the first time that discrete-time prediction-correction and correction-extra correction algorithms have been applied in solving time-varying optimal power flow problems in active distribution networks. Detailed case studies, including correctness, validity, and sensitivity analysis, are carried out on a modified IEEE 33-node system. The numerical results reveal the advantages, disadvantages, and applicability of the proposed algorithms. Moreover, the characteristics of the discrete-time and continuous-time algorithms are compared intuitively. Finally, based on the numerical results, a guide for how to select an appropriate online time-varying optimal power flow algorithm for active distribution network optimization is provided, wherein the discrete-time prediction-correction optimal power flow algorithm is the most frequently recommended choice.

关键词： Distributed energy resource Active distribution network online algorithm Time-varying optimization Optimal power flow

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An online algorithm for scheduling big data analysis jobs in cloud environments

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KNOWLEDGE-BASED SYSTEMS 2022年 245卷 1页

作者： Kang, Youyou Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan Peoples R China

Cloud computing has become a popular platform for processing big data analysis jobs with its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis service providers use cloud instances to process users' big data analysis job execution requests and they need efficient scheduling algorithms to improve job execution efficiency and economic benefits. This paper presents a problem of minimizing the execution time of a batch of big data analysis jobs without changing the number of cloud instances. Solving this problem can not only improve big data job execution efficiency in cloud environments and user satisfaction, but also bring higher economic benefits to big data analysis service providers. This paper proposes an online scheduling algorithm, which can make full use of the parallelism of big data analysis jobs to optimize job scheduling decisions on the premise that the job execution time cannot be accurately known. For evaluating the performance of the proposed online scheduling algorithm, a traditional two-phase scheduling algorithm is introduced as a benchmark for comparison in this paper. Theoretical analysis and extensive simulation experiments based on real datasets show that the online scheduling algorithm proposed in this paper can achieve more stable performance compared with the benchmark two-phase scheduling algorithm. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Big data Cloud computing Job scheduling online algorithm

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An online algorithm for VNF Service Chain Scaling in Datacenters

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第3期28卷 1061-1073页

作者： Luo, Ziyue Wu, Chuan Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Built on top of virtualization technologies, network function virtualization (NFV) provides flexible and scalable software implementation of various network functions. Virtual network functions (VNFs), which are network functions implemented as virtual machines, are chained together to provide network services. Dynamic deployment of VNFs while satisfying incoming network traffic demand is the key to cost optimization of an NFV system. Besides considering server resource capacity and incoming traffic rates, an optimal scaling policy needs to strike a balance between VNF's operational costs, the costs for maintaining VNF instances, and VNF deployment costs, additional costs when setting up new VNF instances on a server. This paper targets dynamic scaling of VNF instances in a cloud data center where multiple VNF chains are running. We propose an online scaling algorithm to adjust the deployment of VNF instances according to time-varying traffic demand, ensuring a good competitive ratio. Through theoretical analysis and trace-driven simulation, we demonstrate effectiveness of the proposed online VNF scaling algorithm.

关键词： Servers Heuristic algorithms Cloud computing Optimization Middleboxes Prediction algorithms IEEE transactions Network function virtualization service chains online algorithm dynamic scaling

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A Space-Efficient One-Pass online SVM algorithm

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International Journal of Computational Geometry and Applications 2024年第1-2期34卷 63-79页

作者： Liu, Yangwei Huang, Ziyun Xu, Jinhui Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY United States Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA United States

In this paper we consider the problem of training a Support Vector Machine (SVM) online using a stream of data in random order. We provide a fast online training algorithm for general SVM on very large datasets. Based on the geometric interpretation of SVM known as the polytope distance, our algorithm uses a gradient descent procedure to solve the problem. With high probability our algorithm outputs an (ϵ, δ)-approximation result in constant time and space, which is independent of the size of the dataset, where (ϵ, δ)-approximation means that the separating margin of the classifier is almost optimal (with error ≤ ϵ), and the number of misclassified training points is very small (with error ≤ δ). Experimental results show that our algorithm outperforms most of existing online algorithms, especially in the space requirement aspect, while maintaining high accuracy. © 2024 World Scientific Publishing Company.

关键词： online algorithm SVM

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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 Optimal online algorithm for Scheduling with Learning Consideration

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2021年第5期38卷 2140002-2140002页

作者： Ma, Ran Han, Wenwen Miao, Cuixia Zou, Juan Qingdao Univ Technol Sch Management Engn Qingdao 266525 Peoples R China Univ Res Ctr Smart City Construct & Management Sh Qingdao 266525 Peoples R China Qufu Normal Univ Sch Math Sci Qufu 273165 Shandong Peoples R China

This paper investigates a classic online scheduling problem with learning effect on a single machine. Specifically, a number of independent jobs that arrive online over time will be processed on a single machine and learning effect implies that the real processing time of job Jj is a non-increasing function of its position h, i.e., p(jh) = p(j)alpha(h-1), where p(j) is the basic processing time of job J(j) and 0 < alpha < 1 is the learning index. Our goal is to minimize the total completion time of all jobs. For the problem, we develop a deterministic polynomial time online algorithm called Delayed Shortest Basic Processing Time (DSBPT) and state that it is an online algorithm with a competitive ratio of 2, which matches the lower bound of the online scheduling problem we focus on.

关键词： Scheduling learning effect online algorithm competitive ratio

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An online algorithm for local address assignment supporting P2P distributed computing

An online algorithm for local address assignment supporting ...

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Chinese Automation Congress (CAC)

作者： Zhong, Dianyu Zhao, Qianchuan Zhang, Ziyou Wang, Yifan Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781728176871

In this paper, we consider a special type of Peer to Peer (P2P) networks that all nodes have only a small number of reusable local addresses and there is no global unique address for each node. Use of local address avoids the problem of managing global unique address for large networks. There are two constraints on the local address assignment of distributed networks. One is that a node cannot have the same address as its neighbors and all neighbors of a node should have distinguish addresses. The other is that when a node is not assigned an address, it is in a "dormant" state and cannot establish communication with the surrounding nodes. Based on the above two assumptions, we propose an online algorithm for local address assignment supporting P2P distributed computing. Compared with the traditional off-line algorithm, it can dynamically assign addresses to new nodes and handle conflicts in existing online distributed networks. Analogous to the 2nd-order graph coloring problem, we proved that at most Delta(2) +1 local addresses are needed for P2P distributed computing.

关键词： Peer to Peer network local address assignment online algorithm

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