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Challenges and Opportunities in Algorithmic Solutions for Re-Balancing in Bike Sharing Systems

Tsinghua Science and Technology 2020年第6期25卷 721-733页

作者： Jie Wu the Department of Computer and Information Sciences Temple UniversityPhiladelphia.PA 19122USA

In recent years,the booming of the Bike Sharing System(BSS)has played an important role in offering a convenient means of public transport.The BSS is also viewed as a solution to the first/last mile connection issue in urban cities.The BSS can be classified into dock and dock-less.However,due to imbalance in bike usage over spatial and temporal domains,stations in the BSS may exhibit overflow(full stations)or underflow(empty stations).In this paper,we will take a holistic view of the BSS design by examining the following four components:system design,system prediction,system balancing,and trip advisor.We will focus on system balancing,addressing the issue of overflow/underflow.We will look at two main methods of bike re-balancing:with trucks and with workers.Discussion on the other three components that are related to system balancing will also be given.Specifically,we will study various algorithmic solutions with the availability of data in spacial and temporal domains.Finally,we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.

关键词： algorithmic solutions bike re-balancing Bike Sharing System(BSS) data analytics

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On Maximum Elastic Scheduling in Cloud-Based Data Center Networks for Virtual Machines with the Hose Model

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Journal of Computer Science & Technology 2019年第1期34卷 185-206页

作者： Shuai-Bing Lu Jie Wu Huan-Yang Zheng Zhi-Yi Fang College of Computer Science and Technology Jilin UniversityChangchun 13012China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA19122U.S.A.

With the growing popularity of cloud-based data center networks (Dcns),task resource allocation has become more and more important to the efficient use of resource in Dcns.This paper considers provisioning the maximum admissible load (MAL)of virtual machines (VMs)in physical machines (PMs)with underlying tree-structured Dcns using the hose model for communication.The limitation of static load distribution is that it assigns tasks to nodes in a once-and-for-all manner,and thus requires a priori knowledge of program behavior.To avoid load redistribution during runtime when the load grows,we introduce maximum elasticity scheduling,which has the maximum growth potential subject to the node and link capacities.This paper aims to find the schedule with the maximum elasticity across nodes and links.We first propose a distributed linear solution based on message passing,and we discuss several properties and extensions of the model.Based on the assumptions and conclusions,we extend it to the multiple paths case with a fat tree DCN,and discuss the optimal solution for computing the MAL with both computation and communication constraints.After that,we present the provision scheme with the maximum elasticity for the VMs,which comes with provable optimality guarantee for a fixed flow scheduling strategy in a fat tree DCN.We conduct the evaluations on our testbed and present various simulation results by comparing the proposed maximum elastic scheduling schemes with other methods.Extensive simulations validate the effectiveness of the proposed policies,and the results are shown from different perspectives to provide solutions based on our research.

关键词： data center network (DCN) cloud distributed algorithm elasticity hose model optimization

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A Task Allocation Method for Stream Processing with Recovery Latency Constraint

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Journal of Computer Science & Technology 2018年第6期33卷 1125-1139页

作者： Hong-Liang Li Jie Wu Zhen Jiang Xiang Li Xiao-Hui Wei College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Department of Computer and Information Sciences Temple University Philadelphia PA 19122 U.S.A. Department of Computer Science West Chester University of Pennsylvania West Chester PA 19383 U.S.A.

Stream processing applications continuously process large amounts of online streaming data in real time or nearreal time. They have strict latency constraints. However, the continuous processing makes them vulnerable to any failures,and the recoveries may slow down the entire processing pipeline and break latency constraints. The upstream backupscheme is one of the most widely applied fault-tolerant schemes for stream processing systems. It introduces complexbackup dependencies to tasks, which increases the difficulty of controlling recovery latencies. Moreover, when dependenttasks are located on the same processor, they fail at the same time in processor-level failures, bringing extra recovery latencies that increase the impacts of failures. This paper studies the relationship between the task allocation and therecovery latency of a stream processing application. We present a correlated failure effect model to describe the recoverylatency of a stream topology in processor-level failures under a task allocation plan. We introduce a recovery-latency awaretask allocation problem （RTAP） that seeks task allocation plans for stream topologies that will achieve guaranteed recoverylatencies. We discuss the difference between RTAP and classic task allocation problems and present a heuristic algorithmwith a computational complexity of O（n log2 n） to solve the problem. Extensive experiments were conducted to verify thecorrectness and effectiveness of our approach. It improves the resource usage by 15%-20% on average.

关键词： stream processing task allocation fault-tolerance upstream backup, recovery latency

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A Buffer Scheduling Method Based on Message Priority in Delay Tolerant Networks

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Journal of Computer Science & Technology 2016年第6期31卷 1228-1245页

作者： En Wang Yong-Jian Yang Jie Wu Wen-Bin Liu Department of Computer Science and Technology Jilin University Changchun 130012 China Department of Computer and Information Sciences Temple University Philadelphia 19122 U.S.A. Department of Software Jilin University Changchun 130012 China

Routing protocols in delay tolerant networks usually utilize multiple message copies to guarantee the message delivery, in order to overcome unpredictable node mobility and easily-interrupted connections. A store-carry-and-forward paradigm was also proposed to further improve the message delivery. However, excessive message copies lead to the shortage of buffer and bandwidth. The spray and wait routing protocol has been proposed to reduce the network overload caused by the buffer and transmission of unrestricted message copies. However, when a node＇s buffer is quite constrained, there still exist congestion problems. In this paper, we propose a message scheduling and drop strategy on spray and wait routing protocol （SDSRP）. To improve the delivery ratio, first of all, SDSRP calculates the priority of each message by evaluating the impact of both replicating and dropping a message copy on delivery ratio. Subsequently, scheduling and drop decisions are made according to the priority. In order to further increase delivery ratio, we propose an improved message scheduling and drop strategy on spray and wait routing protocol （ISDSRP） through enhancing the accuracy of estimating parameters. Finally, we conduct extensive simulations based on synthetic and real traces in ONE. The results show that compared with other buffer management strategies, ISDSRP and SDSRP achieve higher delivery ratio, similar average hopcounts, and lower overhead ratio.

关键词： delay tolerant network spray and wait buffer scheduling priority

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