会议名称：
22nd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)
会议时间：
DEC 17-19, 2021
会议地点：
Sun Yat Sen Univ, Guangzhou, PEOPLES R CHINA
会议主办单位：
Sun Yat Sen Univ
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Communication distance;Data locality;Executor allocation;Spark
摘要：
Data locality is a key factor influencing the performance of Spark systems. As the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation in reduce stage for Spark framework. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, an approximation algorithm is proposed and the approximate factor is proved to be 2. Finally, we evaluate the performance of our algorithm in a practical Spark cluster by using several representative benchmarks: sort, pageRank and LDA. Experimental results show that the proposed algorithm can help to improve the data locality and application/job performance obviously.
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.
作者机构：
[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin; Liu, Qifei; Li, Jing; Geng, Yuqing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.
会议名称：
IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT)
会议时间：
DEC 06-09, 2022
会议地点：
Vancouver, WA
会议主办单位：
[Geng, Yuqing;Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin;Liu, Qifei;Li, Jing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA 02114 USA.
关键词：
Cloud Storage;Resemblance Detection;Context-Aware;Deduplication Ratio Prediction
摘要：
With the prevalence of cloud storage, people prefer to outsource their data to the cloud for flexibility and reliability. Undoubtedly, there are lots of redundancy among these data. However, high-end storage with deduplication costs heavy computation and increases the data management complexity. Potential customers need the redundancy proportion information of their outsourced data to decide whether high-end storage with deduplication is worthwhile. Thus, many researchers have previously attempted to predict the redundant ratio. However, existing mechanisms ignore the redundancy proportion among similar chunks containing many duplicate data. Although resemblance detection, detecting the duplicate parts among similar data, has become a hot issue, it is hardly applied to the conventional deduplication ratio estimation because of unacceptable calculation cost. Therefore, we analyze the limitations and challenges of deduplication ratio prediction in prediction scope and response time and further propose a novel prediction scheme. By leveraging the context-aware resemblance detection, and confidence interval theory, our method can achieve faster estimation speed with higher accuracy in deduplication ratio compared with the state-of-the-art work. Finally, the results show that our method can efficiently and effectively estimate the proportion of duplicate chunks and redundant data among similar chunks by conducting experiments on real workloads.
期刊：
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS,2022年519:28-36 ISSN：0168-583X
通讯作者：
Fei Mao
作者机构：
[Li, Shi-Ming; Mao, Fei; Zhao, Xu-Dong] Univ South China, Sch Nucl Sci & Technol, Hengyang 421001, Peoples R China.;[Cheng, Guo-Dong] Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.;[Li, Bing-Sheng] Southwest Univ Sci & Technol, State Key Lab Environm Friendly Energy Mat, Mianyang 621010, Sichuan, Peoples R China.;[Mao, Hong] Hunan Inst Sci & Technol, Coll Mech Engn, Yueyang 414006, Hunan, Peoples R China.;[Wang, Feng] Beijing Inst Technol, Sch Phys, Beijing 100081, Peoples R China.
通讯机构：
[Fei Mao] S;School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
关键词：
Electron transfer;Electronic excitation;Excitation threshold;Stopping power
摘要：
The electronic stopping power of Zn for energetic protons is studied by using a nonequilibrium approach based on real-time time-dependent density-functional theory combined with molecular dynamics simulations. We calculated the electronic stopping power of protons traveling along two channeling trajectories depending on the impact parameter and off-channeling trajectories, and revealed the mechanism for d-electron excitation. In the low-velocity range, we reproduced not only the velocity proportionality of the stopping power of Zn for protons, but also the smooth transition between two velocity proportionality regimes, which is ascribed to the gradually increasing efficiency for d-electron excitation. The off-channeling electronic stopping power is in a good agreement with experimental data in the low and middle-velocity regimes. Our results showed that the contribution of d-electron excitation to the electronic stopping is remarkable in the high-velocity regime.
摘要：
Spark Streaming is an extension of the core Spark engine that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It treats stream as a series of deterministic batches and handles them as regular jobs. However, for a stream job responsible for a batch, data skew (i.e., the imbalance in the amount of data allocated to each reduce task), can degrade the job performance significantly because of load imbalance. In this paper, we propose an improved range partitioner (ImRP) to alleviate the reduce skew for stream jobs in Spark Streaming. Unlike previous work, ImRP does not require any pre-run sampling of input data and generates the data partition scheme based on the intermediate data distribution estimated by the previous batch processing, in which a prediction model EWMA (Exponentially Weighted Moving Average) is adopted. To lighten the data skew, ImRP presents a novel method of calculating the partition borders optimally, and a mechanism of splitting the border key clusters when the semantics of shuffle operators permit. Besides, ImRP considers the integrated partition size and heterogeneity of computing environments when balancing the load among reduce tasks appropriately. We implement ImRP in Spark-3.0 and evaluate its performance on four representative benchmarks: wordCount, sort, pageRank, and LDA. The results show that by mitigating the data skew, ImRP can decrease the execution time of stream jobs substantially compared with some other partition strategies, especially when the skew degree of input batch is serious.
关键词：
Oblivious RAM;Accountability;Group signature;Blockchain;Access control
摘要：
Recently, oblivious random access machine (ORAM) has been widely used to prevent privacy leakage from user's access pattern. However, in multi-user scenarios, the obliviousness property of ORAM facilitates the malicious data modification by unauthorized users, which brings a new security challenge of user accountability to ORAM applications. Moreover, based on our observations, existing user accountability schemes for multi-user ORAM induce the extremely unacceptable overhead in both time and storage. What is worse, it is still inherent the traditional cloud accountability problem that the untrusted cloud server may have misbehavior on storing the outsourced data. In this paper, we focus on the issue that how to do accountability for both malicious users and untrusted cloud server without the independent trusted third party server. To address the above problem, we design and implement a Traceable Oblivious RAM, or T-ORAM for short, a cryptographic system that protects the privacy of users and the integrity of outsourced data based on group signatures. It can detect malicious users quickly by utilizing the traceability property of group signatures, and cost less storage overhead comparing with the existing solutions. Then, we further propose a more secure solution of Blockchain-based Traceable Oblivious RAM (BT-ORAM). Specifically, by introducing the blockchain technology, BT-ORAM can detect the malicious behavior from both malicious users and untrusted cloud server. BT-ORAM is the first accountability work for multi-user ORAM that deal with both malicious users and the untrusted cloud server. Finally, security analysis and experimental results show that our method outperforms the state-of-the-art accountability work for oblivious RAM, S-GORAM, in both security and performance. (C) 2019 Elsevier Inc. All rights reserved.
摘要：
microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.
摘要：
Qutrit is the natural extension of qubit in quantum information processing and has quite a few advantages that outperform qubit. In this paper, we investigate the feasibility of teleportation of an unknown qubit state, as well as an unknown qutrit state using a two-qutrit entangled pair. We show that by carefully constructing the measurement bases, both the qubit and the qutrit can be faithfully teleported from Alice to Bob with a two-qutrit maximally entangled state.
期刊：
Journal of Parallel and Distributed Computing,2020年146:1-14 ISSN：0743-7315
通讯作者：
Ruixuan Li
作者机构：
[Dongdong Yue; Ruixuan Li] School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;[Yan Zhang 0003] School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia;[Wenlong Tian] School of Computer Science and Technology, University of South China, Hengyang, China;[Yongfeng Huang] Department Of Electronic Engineering, Tsinghua University, Beijing 100084, China
通讯机构：
[Ruixuan Li] S;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
