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28th International Computing and Combinatorics Conference (COCOON)

作者： Li, Sizhe Xue, Jinghui Jin, Mingming Wang, Kai He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9783031221040;9783031221057

In this work, we study the well-known online bin packing problem and propose a new approximation algorithm under the restriction of linear time and constant bounded space. In this setting, Lee and Lee in 1985 [12] introduced an algorithm called Harmonic that achieves the asymptotic approximation ratio (AAR) of T-infinity approximate to 1.69, and showed that no O(1)-space algorithm can obtain competitive ratio better than T-infinity. Zhang et al. in 2000 [15] presented a linear time constant bounded space (number of bins kept during the execution of the algorithm is constant) online algorithm and proved that the absolute worst-case ratio is 7/4 = 1.75. We extend Zhang et al.'s work to consider three types of bins, and show that our proposed algorithm uses no more than Inverted right perpendicular55/32 center dot OPTInverted left perpendicular bins. We first prove that the asymptotic approximation ratio is at most 55/32 = 1.71875 by using the weight function, then we show that the additive term could be reduced from 3 to less than 1 if post-processing repacking is allowed. In the end, we provide a worst-case analysis and prove that the upper bound of 55/32 is tight.

关键词： Bin packing online algorithm Bounded-space Competitive ratio Worse case analysis

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Near-optimal online algorithms for Joint Pricing and Scheduling in EV Charging Networks 23

Near-optimal Online Algorithms for Joint Pricing and Schedul...

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14th ACM International Conference on Future Energy Systems (e-Energy)

作者： Bostandoost, Roozbeh Sun, Bo Joe-Wong, Carlee Hajiesmaili, Mohammad Univ Massachusetts Amherst MA 01003 USA Chinese Univ Hong Kong Hong Kong Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400700323

With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in smart sustainable cities to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy costs. Upon receiving a charging request, the operator offers a price, and the EV decides whether to accept the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve an order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data.

关键词： online algorithm scheduling optimization pricing

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online algorithms for Packet Scheduling

Online Algorithms for Packet Scheduling

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作者： Veselý, Pavel Charles University of Prague

We study online scheduling policies for buffer management models, in which packets are arriving over time to a buffer of a network switch to be sent through its single output port. However, the bandwidth of the port is limited and some packets need to be dropped, based on their weights. The goal of the scheduler is to maximize the weighted throughput, that is, the total weight of packets transmitted. Due to the natural lack of information about future, an optimal performance cannot be achieved, we thus pursue competitive analysis and its refinements to analyze online algorithms on worst-case inputs. Specifically, in the first part of the thesis, we focus on a simple online scheduling model with unit-size packets and deadlines, called Bounded-Delay Packet Scheduling. We design an optimal φ-competitive deterministic algo- rithm for the problem, where φ ≈ 1.618 is the golden ratio. It is based on a detailed understanding of an optimal schedule of pending packets, called the plan, which may be of independent interest. We also propose a semi-online setting with lookahead that allows the algorithm to see a little bit of future, namely, packets arriving in the next few steps. We provide an algorithm with lookahead for instances in which each packet can be scheduled in at most two consecutive slots and prove lower...

关键词： online algorithm competitive analysis online scheduling buffer management resource augmentation

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Novel online algorithms for Nonparametric Correlations with Application to Analyze Sensor Data

Novel Online Algorithms for Nonparametric Correlations with ...

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IEEE International Conference on Big Data (Big Data)

作者： Xiao, Wei Amazon Customer Serv Applicat Seattle WA 98109 USA

ISBN: (纸本)9781728108582

Nonparametric correlations such as Spearman's rank correlation and Kendall's tau correlation are widely applied in scientific and engineering fields. This paper investigates the problem of computing nonparametric correlations on the fly for streaming data. Standard batch algorithms are generally too slow to handle real-world big data applications. They also require too much memory because all the data need to be stored in the memory before processing. This paper proposes a novel online algorithm for computing nonparametric correlations. The algorithm has O(1) time complexity and O(1) memory cost and is quite suitable for edge devices, where only limited memory and processing power are available. You can seek a balance between speed and accuracy by changing the number of cutpoints specified in the algorithm. The online algorithm can compute the nonparametric correlations 10 to 1,000 times faster than the corresponding batch algorithm, and it can compute them based either on all past observations or on fixed-size sliding windows. The algorithm is applied to analyze real-world sensor data from an industrial plant.

关键词： online algorithm nonparametric correlation stream analysis sensor data correlation analysis

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OPTIMAL SEMI-online algorithmS FOR m-BATCH-MACHINE FLOW SHOP SCHEDULING

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DISCRETE MATHEMATICS algorithmS AND APPLICATIONS 2012年第4期4卷 1250051页

作者： Liu, Ming Chu, Chengbin Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Ecole Cent Paris Lab Genie Ind F-92295 Chatenay Malabry France Xi An Jiao Tong Univ Sch Management Xian 710049 Shaanxi Peoples R China

This paper deals with semi-online scheduling on m-batch-machine flow shop. The objective is to minimize the makespan. A parallel batch processing machine can handle up to B jobs simultaneously. We study an unbounded model where B = infinity. The jobs that are processed together construct a batch, and all jobs in a batch start and complete at the same time. The processing time of a batch is given by the longest processing time of any job in the batch. The problem is online in the sense that jobs arrive over time. Let pi,j (i = 1, ... , m) denote the processing time of job J(j) on machines M-i, respectively. Let J(j+1) be the following job of J(j) in a job instance. We study semi-online problem with jobs' nondecreasing processing times. We focus on the case where p(1,j) = . . . = p(m,j) for i = 1, ... , m and p(i, j+1) >= beta p(i,j) (beta >= 1). For this problem, we propose an optimal algorithm W-beta(infinity) with a competitive ratio 2m-m beta+root 4m+beta(2)m(2)/2m.

关键词： Scheduling batch processing machines flow shop online algorithm

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Caching or not: An online cost optimization algorithm for geodistributed data analysis in cloud environments

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 229卷

作者： Yang, Weitao Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan Peoples R China

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage -as -a -Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba's public IaaS cloud products using both real -world Yahoo S2 data and synthesized datasets.

关键词： Big data MapReduce online algorithm Geodistributed

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A cost-driven online auto-scaling algorithm for web applications in cloud environments

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KNOWLEDGE-BASED SYSTEMS 2022年 244卷 108523-108523页

作者： Si, Wen Pan, Li Liu, Shijun Shandong Univ Sch Software 1500 Shunhua Rd Jinan 250101 Shandong Peoples R China

Today, many web application service providers rely on clouds to deploy applications to serve users. Generally, request arrivals faced by web applications are dynamic and uncertain. When a service provider deploys web applications in clouds, for saving costs, it needs to flexibly rent cloud VM (Virtual Machine) instances based on dynamic request arrivals. However, renting an instance too early may incur more rental fees for the new instance being added incorrectly due to few future requests, and renting an instance too late may incur more penalty fees for SLA (service-level agreement) violations due to too many future requests, which indicates that an arbitrary instance scaling decision will incur more costs. For making optimal instance scaling decisions, future request arrival rate curves are needed, but it is generally very hard to predict them precisely. To solve this problem, in this paper, we propose a cost-driven online auto-scaling algorithm which can make optimized instance rental decisions without requiring future knowledge. We show theoretically that the proposed algorithm can achieve a guaranteed competitive ratio which is less than 2. Eventually, we verify the effectiveness of our online auto-scaling algorithm via extensive experiments using workload data which can simulate real end users. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Cloud computing Web application Cost optimization online algorithm Competitive analysis

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Backup or Not: An online Cost Optimal algorithm for Data Analysis Jobs Using Spot Instances

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IEEE ACCESS 2020年 8卷 144945-144956页

作者： Lin, Liduo Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan 250101 Peoples R China

Recently, large-scale public cloud providers begin to offer spot instances. This type of instance has become popular with more and more cloud users in the light of its convenient access mode and low price, especially for those big data analysis jobs with high performance computation requirements. However, using spot instances may carry the risk of being interrupted and lead to extra costs for job re-executions because these instances are generally unstable. Yet, such cost can be greatly reduced if a backup can be made at the right time before interruptions. For convenience and cost efficiency, users can choose the StaaS (Storage-as-a-Service) storage provided by the same cloud provider, whose spot instances are used by the users, to store backup data files for future job execution recovery. Since making backups too often will incur increased costs, users need to make the backup decisions appropriately considering the condition when an abrupt interruption will occur in the future. However, it is hard to know or predict precisely when such an interruption will occur. For solving this problem, in this article, we propose an online algorithm to guide cloud users to make backups when using spot instances to execute big data analysis jobs, without requiring any information about future interruptions. We prove theoretically that our proposed online algorithm can guarantee a bounded competitive ratio less than 2. Finally, according to extensive experiments, we verify the effectiveness of our online algorithm in reducing the additional cost caused by interruptions in using spot instances and find that our online algorithm can still achieve a stable cost optimization even if interruptions occur frequently.

关键词： Spot instance online algorithm back up abrupt termination

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An online multi-source summarization algorithm for text readability in topic-based search

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COMPUTER SPEECH AND LANGUAGE 2021年 66卷 101143-101143页

作者： Curiel, Arturo Gutierrez-Soto, Claudio Rojano-Caceres, Jose-Rafael Univ Veracruzana CONACYT Xalapa Veracruz Mexico Univ Veracruzana Fac Estadist & Informat Xalapa Veracruz Mexico Univ Bio Bio Dept Sistemas Informac Concepcion Chile

Web search users are likely to face problems related to the availability of large amounts of data. As the quantity of online content grows, the risk of missing relevant information during search can only increase. Moreover, external variables such as the users' reading proficiency level can further complicate the task. This article proposes an online multi-document summarization algorithm for text readability, as a means to simplify web search. The algorithm is designed to work over collections of topic-related documents, such as the ones returned as the results to a web query. Contrary to most modern approaches, no preliminary training for the algorithm is required. The algorithm was tested in both English and Spanish language documents, using different metrics of term and sentence relevance. The results were compared against summaries created by both human summarizers and third-party Automatic Text Summarization (ATS) systems in terms of two variables: readability and information content. In both variables, the results show generalized gains with respect to both the human summarizers and the third-party ATS systems. Furthermore, the algorithm achieved these results with a time complexity strictly lower than O(n(2));well below traditional machine learning approaches. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Automatic text summarization Text readability online algorithm Information retrieval

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A quantum online portfolio optimization algorithm

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QUANTUM INFORMATION PROCESSING 2024年第3期23卷 63-63页

作者： Lim, Debbie Rebentrost, Patrick Univ Latvia Riga Latvia Ctr Quantum Technol Singapore Singapore

Portfolio optimization plays a central role in finance to obtain optimal portfolio allocations that aim to achieve certain investment goals. Portfolio optimization also provides a rich area to study the application of quantum computers to obtain advantages over classical computers. In a multi-period setting, we give a sampling version of an existing classical online portfolio optimization algorithm by Helmbold et al., for which we in turn develop a quantum version. The quantum advantage is achieved by using techniques such as quantum state preparation, inner product estimation and multi-sampling. Our quantum algorithm provides a quadratic speedup in the time complexity, in terms of n, where n is the number of assets in the portfolio. The transaction cost of both of our classical and quantum algorithms is independent of n which is especially useful for practical applications with a large number of assets.

关键词： online algorithm Optimization algorithm Portfolio optimization Quantum algorithm Quantum computing

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