检索结果-内蒙古大学图书馆

Fence: Fee-Based online Balance-Aware Routing in Payment Channel Networks

IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第2期32卷 1661-1676页

作者： Wang, Xiaojian Yu, Ruozhou Yang, Dejun Xue, Guoliang Gu, Huayue Li, Zhouyu Zhou, Fangtong North Carolina State Univ Dept Comp Sci Raleigh NC 27606 USA Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85287 USA

Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.

关键词： Routing Throughput Blockchains Bitcoin Security Scalability Privacy Blockchain payment channel network routing online algorithm competitive analysis

来源：评论

学校读者我要写书评

暂无评论

online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

引用

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 224卷

作者： Aldailamy, Ali Y. Muhammed, Abdullah Latip, Rohaya Hamid, Nor Asilah Wati Abdul Ismail, Waidah Univ Putra Malaysia Fac Comp Sci & Informat Technol Dept Commun Technol & Networking Serdang 43400 Selangor Malaysia Univ Sains Islam Malaysia Fac Sci & Technol Nilai 71800 Negeri Sembilan Malaysia

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four -fold obstacles: vendor lock -in, service availability, cost -ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

关键词： Access latency Cost optimization Data placement Data replication online algorithm online social network

来源：评论

学校读者我要写书评

暂无评论

Optimal algorithms for solving green online reheat furnace scheduling problems with deterioration effect

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Xu, Juannian Ma, Ran Zhang, Houkun Qingdao Univ Technol Sch Management Engn Qingdao Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Peoples R China

Optimizing reheating furnace scheduling in terms of energy saving and reheating time reduction is a vital component in improving competitive advantage of the steel enterprises. However, current reheating furnace scheduling models are still limited since (1) most of the scheduling problems involving reheating furnace suppose that the steel orders and their information are known in advance and (2) the reheating time of a slab in reheating operation is a constant in the majority of current reheating furnace scheduling models. To address these issues, three online reheat furnace scheduling models with deterioration effect are proposed in order to minimize the maximum residence time, the maximum energy consumption as well as total energy consumption of all slabs in reheating operation, respectively. Furthermore, for these online scheduling problems, deterministic online algorithms are presented and shown to be optimal, respectively. In addition, the computational experiments confirm that the three online algorithms developed in this paper are particularly efficient and effective in practice.

关键词： Scheduling online algorithm Energy consumption Maximum residence time

来源：评论

学校读者我要写书评

暂无评论

Incentive Mechanisms for online Task Offloading With Privacy-Preserving in UAV-Assisted Mobile Edge Computing

引用

IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第3期32卷 2646-2661页

作者： Zhang, Renli Zhou, Ruiting Wang, Yufeng Tan, Haisheng He, Kun Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan 430072 Peoples R China Southeast Univ Sch Comp Sci Engn Nanjing 210096 Peoples R China Univ Sci & Technol China LINKE Lab Hefei 230026 Peoples R China

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to provide low-latency mobile edge computing (MEC) services. To fully utilize the potential of UAV-assisted MEC in practice, both technical and economic challenges need to be addressed: how to optimize UAV trajectory for online task offloading and incentivize the participation of UAVs without compromising the privacy of user equipment (UE). In this work, we consider unique features of UAVs, i.e., high mobility as well as limited energy and computing capacity, and propose privacy-preserving auction frameworks, Ptero, to schedule offloading tasks on the fly and incentivize UAVs' participation. Specifically, Ptero first decomposes the online task offloading problem into a series of one-round problems by scaling the UAV's energy constraint into the objective. To protect UE's privacy, Ptero calculates UAV's coverage based on subset-anonymity. At each round, Ptero schedules UAVs greedily, computes remuneration for working UAVs, and processes unserved tasks in the cloud to maximize the system's utility ( i.e., minimize social cost). Theoretical analysis proves that Ptero achieves truthfulness, individual rationality, computational efficiency, privacy-preserving and a nontrivial competitive ratio. Trace-driven evaluations further verify that Ptero can reduce the social cost by up to 116% compared with four state-of-the-art algorithms.

关键词： UAV trajectory task offloading online algorithm incentive mechanism

来源：评论

学校读者我要写书评

暂无评论

Real-Time Feedback Based online Aggregate EV Power Flexibility Characterization

引用

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第1期15卷 658-673页

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

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Flexible EVs can enhance power systems' ability to handle renewable generation uncertainties. How EV flexibility can be utilized in power grid operation has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity and large number. Hence, it is the aggregator that interacts with the grid on behalf of the EVs by characterizing their aggregate flexibility. In this article, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate EV power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We provide a theoretical bound for the maximum charging delay under the proposed online algorithm. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.

关键词： Aggregate flexibility charging station electric vehicle (EV) Lyapunov optimization online algorithm

来源：评论

学校读者我要写书评

暂无评论

online computation offloading for deadline-aware tasks in edge computing

引用

WIRELESS NETWORKS 2024年第5期30卷 4073-4092页

作者： He, Xin Zheng, Jiaqi He, Qiang Dai, Haipeng Liu, Bowen Dou, Wanchun Chen, Guihai Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. Prior studies mainly focus on tasks with strict deadlines. However, in the real world, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much, and still be timely. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT, a novel offloading approach that handles tasks with mixtures of hard deadlines and soft deadlines. Specifically, we first formulate the problem as an integer linear programming and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers to maximize the task completion ratio and an algorithm that adjusts the task execution order to maximize the task completion revenue. Moreover, to balance the fairness among tasks and system revenue elastically, we extend CONFECT by using a tunable fairness knob. Finally, extensive experiments show that CONFECT outperforms five baseline algorithms in terms of task completion ratio and completion revenue.

关键词： Computation offloading Deadline-aware tasks Edge computing online algorithm Fairness

来源：评论

学校读者我要写书评

暂无评论

Time and energy driven online scheduling problem in EV charging

引用

THEORETICAL COMPUTER SCIENCE 2025年 1044卷

作者： Guo, Xinru Dai, Sijia Han, Xinxin Xu, Yicheng Zhang, Yong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shenzhen Polytech Univ Sch Artificial Intelligence Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China

Electric vehicles (EVs) become more and more popular, along with the higher and higher efficiency demand for charging scheduling. This paper studies the online scheduling problem of EV charging that minimizes the maximum flow time, solving the problem of queuing congestion at charging stations during peak hours. Based on the characteristics of the charging power curve of electric vehicles, we design an online scheduling algorithm for EV charging and prove that the ratio of this algorithm to the optimal offline algorithm is gamma = 3.4528 . (1 + epsilon). We analyse and find that the proposed algorithm can adjust the charging time proportionally, allowing the electric vehicle EV Ito gradually reach the final State of Charge (SOC), which is 0.7313 + 0.2687 . I-int, from its initial charging state (I-int). According to the battery overheating protection mechanism, charging will stop when the battery level starts to decrease. This method effectively balances the trade-off between time cost and energy intake, significantly improving the queuing situation for charging electric vehicles during peak hours.

关键词： Scheduling Flow-time online algorithm Competitive analysis EV charging

来源：评论

学校读者我要写书评

暂无评论

Learning-Augmented Decentralized online Convex Optimization in Networks

引用

PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS 2024年第3期8卷 1-42页

作者： Li, Pengfei Yang, Jianyi Wierman, Adam Ren, Shaolei Univ Calif Riverside Riverside CA 92521 USA Univ Houston Houston TX USA CALTECH Pasadena CA USA

This paper studies learning-augmented decentralized online convex optimization in a networked multi-agent system, a challenging setting that has remained under-explored. We first consider a linear learning-augmented decentralized online algorithm (LADO-Lin) that combines a machine learning (ML) policy with a baseline expert policy in a linear manner. We show that, while LADO-Lin can exploit the potential of ML predictions to improve the average cost performance, it cannot have guaranteed worst-case performance. To address this limitation, we propose a novel online algorithm (LADO) that adaptively combines the ML policy and expert policy to safeguard the ML predictions to achieve strong competitiveness guarantees. We also prove the average cost bound for LADO, revealing the tradeoff between average performance and worst-case robustness and demonstrating the advantage of training the ML policy by explicitly considering the robustness requirement. Finally, we run an experiment on decentralized battery management. Our results highlight the potential of ML augmentation to improve the average performance as well as the guaranteed worst-case performance of LADO.

关键词： decentralized optimization learning to optimize online algorithm

来源：评论

学校读者我要写书评

暂无评论

SOAR: Smart online Aggregated Reservation for Mobile Edge Computing Brokerage Services

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第1期22卷 527-540页

作者： Zang, Shizhe Bao, Wei Yeoh, Phee Lep Vucetic, Branka Li, Yonghui Univ Sydney Fac Engn & Informat Technol Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ Sydney Fac Engn & Informat Technol Sch Comp Sci Sydney NSW 2006 Australia

With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users' demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40 percent of cost for users through the brokerage service.

关键词： Mobile edge computing resource reservation resource brokerage competitive analysis online algorithm

来源：评论

学校读者我要写书评

暂无评论

online influence maximization under continuous independent cascade model with node-edge-level feedback

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 1091-1110页

作者： Liu, Chao Xu, Haichao Liu, Xiaoyang Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China

We study the online influence maximization problem in social networks. We concentrate on solving two challenges in this paper. First, we work with continuousindependentcascademodel instead of independentcascademodel. In the independent cascade model, the influence diffusion is limited and imprecise because an activated node can only attempt to impact a neighbor through a directed edge for once. Therefore, we propose a continuous influence set and combine this set with the independent cascade model to build a continuous independent cascade model, which realizes multiple activation and can be more customized to attract more targeted users. Second, we devise node-edge-levelfeedback instead of node-levelfeedback. In the node-level feedback, though the combined influence is relatively easy to observe, the exact edge which cause the influence seldom reveals. Therefore, we use node-edge-level feedback to generate the source nodes which activate the active node. We improve the IMFB algorithm and propose the CIC_IMFB-NE algorithm. The CIC_IMFB-NE algorithm is more efficient than the existing online influence maximization algorithm. Our experiments demonstrate the excellence of CIC_IMFB-NE in terms of regret bound in real life.

关键词： Social network Influence maximization Independent cascade model online algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：