{"title":"A Gird-Based Joint Routing and Energy Replenish Scheme for Rechargeable Wireless Sensor Networks","authors":"Zhansheng Chen, Hong Shen","doi":"10.1109/PDCAT46702.2019.00097","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00097","url":null,"abstract":"To maintain the durability of data collection and improve charging efficiency for a mobile wireless charging vehicle (WCV) in Wireless Rechargeable Sensor Networks (WRSNs), a grid-based joint routing and energy replenish scheme (GRER) is proposed in this paper, aiming to achieve energy balance and maximize recharging benefit. Based on WCV charging service, network is firstly divided into several virtual grids with the help of minimum coverage area idea and a simple concurrent multi-hop chain-based routing protocol is proposed for forming data transmission links. Then, WCV visits and charges nodes using an angle expansion-based breadth-first strategy (AEBF) within the limited battery capacity. Simulation results demonstrate that GRER outperforms FCFS, NJNP, LADP and RCSS schemes in regards to network lifetime, energy balance and charging efficiency.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Privacy Preservation for Network Traffic Classification","authors":"Yue Lu, Hui Tian, Jingjing Yu","doi":"10.1109/PDCAT46702.2019.00027","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00027","url":null,"abstract":"With the rapid development of Internet technology and massive demands of data sharing, data privacy issues have attracted more and more attention in recent years. The paper analyzes the network traffic classification methods and designs the features subset selection algorithm based on information entropy. The proposed privacy preserving algorithm is based on data perturbation. By applying the algorithm on the real network traffic data set, it is shown that the network traffic data protected by the algorithm can effectively ensure data security while maintaining data utility, which contributes to balance the contradiction between them in existing algorithms. It effectively solves the privacy leakage problem of network traffic in the process of data mining.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128844297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Parallel Tracking and Reconstruction of States in Heuristic Optimization Systems on GPUs","authors":"M. Köster, J. Groß, A. Krüger","doi":"10.1109/PDCAT46702.2019.00016","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00016","url":null,"abstract":"Modern heuristic optimization systems leverage the parallel processing power of Graphics Processing Units (GPUs). Many states are maintained and evaluated in parallel to improve runtime by orders of magnitudes in comparison to purely CPUbased approaches. A well known example is the parallel Monte Carlo tree search, which is often used in combination with more advanced machine-learning methods these days. However, all approaches require different optimization states in memory to update or manipulate variables and observe their behavior over time. Large real-world problems often require a large number of states that are typically limited by the amount of available memory. This is particularly challenging in cases in which older states (that are not currently being evaluated) are still required for backtracking purposes. In this paper, we propose a new general high-level approach to track and reconstruct states in the scope of heuristic optimization systems on GPUs. Our method has a considerably lower memory consumption compared to traditional approaches and scales well with the complexity of the optimization problem.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127270307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Acceleration of Genetic Algorithm on GPU CUDA Platform","authors":"D. Janssen, Alan Wee-Chung Liew","doi":"10.1109/PDCAT46702.2019.00047","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00047","url":null,"abstract":"When a deterministic search approach is too costly, such as for non-deterministic polynomial-hard problems, finding near-optimal solutions with approximation algorithms, such as the genetic algorithm, is the only practical approach to reduce the execution time. In this paper, we exploit the capability of graphics processing units (GPU), specifically Nvidia's CUDA platform, to accelerate the genetic algorithm by modifying the evolutionary operations to fit the hardware architecture. This has allowed us to achieve significant computational speedups compared to the non-GPU counterparts.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131288587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenting Wei, Kun Wang, Kexin Wang, Shengjun Guo, Huaxi Gu
{"title":"A Virtual Machine Placement Algorithm Combining NSGA-II and Bin-Packing Heuristic","authors":"Wenting Wei, Kun Wang, Kexin Wang, Shengjun Guo, Huaxi Gu","doi":"10.1109/PDCAT46702.2019.00044","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00044","url":null,"abstract":"The servers in the data center networks have multi-dimensional physical resources, and there is a lot of diversity in resource consumption among tasks. When virtual machines carrying different user requests are deployed on the same server at the same time, it is very likely that there is an imbalanced usage of multi-dimensional resources, resulting in the waste of physical resources. In this paper, we focus on virtual machine placement in data centers aiming to balance multi-dimensional resource usage and maximize the service rate. To solve such a bi-objective optimization problem, we present a joint bin-packing heuristic and genetic algorithm to reduce the time complexity while obtaining an approximate optimal solution.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126883341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Outlier Removal for the Reliable Condition Monitoring of Telecommunication Services","authors":"Günter Fahrnberger","doi":"10.1109/PDCAT46702.2019.00052","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00052","url":null,"abstract":"Customer contentment plays an essential (if not the most important) role for the continuation and development of business relations. In this context, the functioning of goods and services acts as the crucial influencing factor. In the case of services, their providers willingly deploy CMSs (Condition Monitoring Systems) for the continuous CM (Condition Monitoring) of the service availability by means of various KPIs (Key Performance Indicators). A CMS must red-flag an abnormal condition. This happens if the recent value(s) of a KPI exceed(s) a predetermined threshold for a certain period. The pertinent literature contains a multiplicity of ways for automatic threshold computation, including a particular one for telecommunication services with time-varying load characteristic. Regrettably, the latter lacks in (distribution-independent) outlier extinction. Thus, this scholarly piece bridges this gap by applying an outlier detection algorithm based upon Walsh's nonparametric tests to a KPI history, removing the identified outliers, and comparing Pukelsheim's three sigma rule and the minimum or maximum value of the outlier-free array for threshold evaluation. The result of a corresponding field test assesses the reliability of the suggested methodology.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"14 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129264693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling Data Transmission in Mobile Ad-hoc Networks for Characterizing Black-Hole Attacks","authors":"Mnar Saeed Alnaghes, Hong Shen","doi":"10.1109/PDCAT46702.2019.00026","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00026","url":null,"abstract":"The vulnerability of mobile ad-hoc networks (MANETs) to various kinds of attacks such as active route interfering and denial of service has brought increasing concerns in the deployment of MANETs. The black-hole Attack is a notorious attack that absorbs data packets by the malicious node and results in denial of service in the network. This paper proposes a Markov chain based stochastic model to simulate data transmission in MANETs using the RTS/CTS handshaking mechanism for characterizing the effect of black-hole attacks. Through simulation evaluation, we analyze the effect of black-hole attacks on major network performance metrics for the multi-hop routing protocol applying the proposed data transmission model.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132416907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine Learning Based Performance Analysis and Prediction of Jobs on a HPC Cluster","authors":"Zhengxiong Hou, Shuxin Zhao, Chao Yin, Yunlan Wang, Jianhua Gu, Xingshe Zhou","doi":"10.1109/PDCAT46702.2019.00053","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00053","url":null,"abstract":"There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121917200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Fault-Tolerant Syndrome Measurement of Quantum Error-Correcting Codes Based on \"Flag\"","authors":"QiFei Wei, Dongxiao Quan, Jing Liu, Changxing Pei","doi":"10.1109/PDCAT46702.2019.00041","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00041","url":null,"abstract":"Fault-tolerant syndrome measurement plays an important role in the process of quantum error correction, and considerable effort had been taken for reducing the physical overhead of syndrome measurement which including the ancilla qubits, the CNOT gates and the time-slots. The two extra qubits syndrome measurement technique, known as \"flag\"-style syndrome measurement, cuts down the number of extra qubits to the utmost. However, it works slowly because it measures only one syndrome at a time. We extend the technique to extract all syndromes of the distance-3 quantum error-correcting code at once. We propose a new method that increases the parallelism of the syndrome measurement circuit and reduces the time overhead by allocating and adjusting the order of CNOT gates for measuring data block reasonably, which we call dynamic time-slot allocation scheme, and which is applicable to both Hamming codes and color codes. For a CSS quantum error-correcting code with the number of stabilizers m and the maximum weight w, we only need 2m extra qubits and 2 × (w+2) time-slots for one-shot measurement of all syndromes.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114423051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Plaintext Recovery Attacks and Their Mitigation in an Application-Specific SHE Scheme","authors":"Tikaram Sanyashi, Anasuya Acharya, B. Menezes","doi":"10.1109/PDCAT46702.2019.00024","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00024","url":null,"abstract":"In addition to storage and computing power, cloud providers ensure confidentiality of user data through the use of various encryption technologies. The need to decrypt the data before it can be operated upon exposes a possible security hole which could be exploited by untrustworthy system administrators. Homomorphic encryption allows operations on encrypted data without the need to first decrypt it making it attractive for cloud computing. However, it incurs significant overhead of storage and computation and is therefore infeasible in practice. Somewhat homomorphic schemes have been proposed to handle specific applications - one such scheme, the Zhou Wornell Scheme, operates on vectors of integers. We demonstrate that this scheme is vulnerable to plaintext recovery attacks for a range of vector sizes. We explore the trade-offs between plaintext vector length, public key size and security. Increasing vector length increases security but at the cost of greatly increased public key size. We suggest a way of reducing the size of the public key by up to 90%. Finally, we propose a variant of this scheme which is secure against plaintext recovery attacks.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123612219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}