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Dynamic Pagerank Frequent Subgraph Mining by GraphX in the distributed System

Dynamic Pagerank Frequent Subgraph Mining by GraphX in the D...

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： Sadhana Priyadarshini Sireesha Rodda Department of CSE GIT GITAM Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781665460859

Graph Mining has been the most demanding research area for the last few decades in different fields, such as biological networks, the world wide web, mobile applications, sensors, online, social networks, etc. Frequent Subgraph Mining (FSM) plays a vital role in Graph Mining to exercise, study and generate interesting patterns from graph data. Basically, FSM techniques are classified into two types such as an apriori-based method, and a pattern growth-based method. This technique faces the problems such as the generation of the duplicate frequent subgraph, having no proper technique to rank during candidate generation, and how to map the threshold values. In this proposed system, a Dynamic PageRank GraphX- based Frequent Subgraph Mining (DPRGFSM) model that is able to extract interesting patterns from the distributed system by eliminating duplicates by ranking them to the proper level. In addition, we also use load balancing, pre-punning, and optimization techniques to improve its performance in both memory requirements and time complexity. The potency of methods defined in this paper is evaluated rigorously with different threshold values and comparative studies with different parameters with existing Spark- based Single Graph Mining (SSIGRAM) and A Ranked Frequent pattern Growth Framework (A- RAFF) and found drastic improvement with all four datasets. The proposed methodology is 1.6 times faster than the Spark-based Single Graph Mining (SSIGRAM) model and 50 times faster than the A Ranked Frequent pattern Growth Framework (A- RAFF) for recurrent subgraph extraction.

关键词： Renewable energy sources Social networking (online) Memory management Sensors Mobile applications Data mining Web sites

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Optimizing the energy envelope in the Internet of Things using predictive cubic splines 17

Optimizing the energy envelope in the Internet of Things usi...

引用

17th Annual IEEE international conference on Sensing, Communication and networking (IEEE SECON)

作者： Ghosh, Saibal Agrawal, Dharma P. Univ Cincinnati Ctr Distributed & Mobile Comp Cincinnati OH 45221 USA

ISBN: (纸本)9781728110622

The paradigm of Internet of Things (IoT) is the result of rapid advances in the development of small low powered computing devices with wireless connectivity. This has given rise to a plethora of new applications, including, but not limited to monitoring, sensing, intrusion detection and others. Due to the nature of these applications, IoT devices are often burdened with receiving and transmitting a large volume of data that is time and delay sensitive. Furthermore, since most of these devices are battery powered, a data deluge often renders parts of the network depleted of energy, causing that part to go dark. In this work, we propose an energy optimization framework based on predictive cubic splines that anticipate a sudden increase in the bandwidth and minimize energy consumed by these devices while still maintaining an optimum degree of availability and reducing bottlenecks in the data flow.

关键词： internet of things energy optimization predictive analytics cubic splines

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A Taxonomy on distributed Controllers in Software Defined networking

A Taxonomy on Distributed Controllers in Software Defined Ne...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Manisha Kumari Rajoriya Chandra Prakash Gupta Rajasthan Technical University Kota India

Software defined networking (SDN) is an ongoing emerging network model that simplifies network management tasks. This review deals with the idea of distributed controllers in large scale software defined networking (LSSDN). Numerous studies have been available considering single controller based classical network. A single controller is insufficient to control the entire network, so a distributed controller or multi-controller is needed for easy flow processing. The details about single controller based SDN are published in the previous literature. So here, author's only focuses on multi controller based software defined network which provides several advantages over classical network in form of features, configuration, performance and innovation. Furthermore, 6 recent distributed controllers are considered and compared on the basis of 26 different criteria's. Also the placement problem in distributed SDN controller is explored. This paper investigates that how to choose the best possible SDN controller which in result will support scalability, less software and hardware needs, centralized visibility and high availability of network and less technical resources requirements.

关键词： Technological innovation Scalability Taxonomy Process control Software Hardware Software defined networking

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A Light in the Dark Web: Linking Dark Web Aliases to Real Internet Identities 40

A Light in the Dark Web: Linking Dark Web Aliases to Real In...

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Arabnezhad, Ehsan La Morgia, Massimo Mei, Alessandro Nemmi, Eugenio Nerio Stefa, Julinda Univ Sapienza Dept Comp Sci Rome Italy

ISBN: (纸本)9781728170022

Most users have several Internet names. On Facebook or LinkedIn, for example, people usually appear with the real one. On other standard websites, like forums, people often use aliases to protect their real identities with respect to the other users, with no real privacy against the web site and the authorities. Aliases in the Dark Web are different: users expect strong identity protection. In this paper, we show that using both "open" aliases (aliases used in the standard Web) and Dark Web aliases can be dangerous per se. Indeed, we develop tools to link Dark Web to open aliases. For the first time, we perform a massive scale experiment on real scenarios. First between two Dark Web forums, then between the Dark Web forums and the standard forums. Due to a large number of possible pairs, we first reduce the search space cutting down the number of potential matches to a small set of candidates, and then on the selection of the correct alias among these candidates. We show that our methodology has excellent precision, from 87% to 94%, and recall around 80%.

关键词： Social networking (online)

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Detection of DoS, DDoS Attacks in Software-Defined networking 1

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international conference on Research in Intelligent and computing in Engineering, RICE 2020

作者： Truong, Dinh-Tu Tran, Khanh-Dang Nguyen, Quoc-Binh Tran, Dac-Tot Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City University of Food Industry Ho Chi Minh City Viet Nam

ISBN: (数字)9789811575273

ISBN: (纸本)9789811575266

In the traditional network system, control plane (CP) and data plane (DP) are both located on the same network device. If that device fails, the whole system will stop working and the traffics cannot be managed by the administrator. In distributed denial of service (DDoS) or denial of service (DoS), the attackers usually use botnets to generate a large or medium size traffic flow toward the server where the service is active. Nowadays, many techniques have been proposed to detect DoS, DDoS attacks but they are not very effective. However, the separation of CP and DP in software-defined networking (SDN) provides a good foundation for attack detection and prevention no matter whether the attackers attack some parts of the SDN or all of them. In this research, we use entropy to calculate the randomness level of the size of the packet to CP, the system’s brain. With this method, the controller can handle faster and we can get more correct output compared with the machine learning method in the SDN. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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Deinsum: practically I/O optimal multi-linear algebra 22

Deinsum: practically I/O optimal multi-linear algebra

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Proceedings of the international conference on High Performance computing, networking, Storage and Analysis

作者： Alexandros Nikolaos Ziogas Grzegorz Kwasniewski Tal Ben-Nun Timo Schneider Torsten Hoefler ETH Zurich Zurich Switzerland NextSilicon Tel Aviv Israel

Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for programs with many high-dimensional inputs is a notoriously hard problem. State-of-the-art libraries rely on heuristics and often fall back to suboptimal tensor folding and BLAS calls. We present Deinsum, an automated framework for distributed multilinear algebra computations expressed in Einstein notation, based on rigorous mathematical tools to address this problem. Our framework automatically derives data movement-optimal tiling and generates corresponding distributed schedules, further optimizing the performance of local computations by increasing their arithmetic intensity. To show the benefits of our approach, we test it on two important tensor kernel classes: Matricized Tensor Times Khatri-Rao Products and Tensor Times Matrix chains. We show performance results and scaling on the Piz Daint supercomputer, with up to 19x speedup over state-of-the-art solutions on 512 nodes.

关键词： performance analysis hardware acceleration distributed computing tensors linear algebra automatic programming

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distributed Hierarchical File Systems strike back in the Cloud 40

Distributed Hierarchical File Systems strike back in the Clo...

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Ismail, Mahmoud Niazi, Salman Sundell, Mauritz Ronstrom, Mikael Haridi, Seif Dowling, Jim KTH Royal Inst Technol Stockholm Sweden Log Clocks AB Stockholm Sweden Oracle AB Santa Clara CA USA

ISBN: (纸本)9781728170022

Cloud service providers have aligned on availability zones as an important unit of failure and replication for storage systems. An availability zone (AZ) has independent power, networking, and cooling systems and consists of one or more data centers. Multiple AZs in close geographic proximity form a region that can support replicated low latency storage services that can survive the failure of one or more AZs. Recent reductions in inter-AZ latency have made synchronous replication protocols increasingly viable, instead of traditional quorum-based replication protocols. We introduce HopsFS-CL, a distributed hierarchical file system with support for high-availability (HA) across AZs, backed by AZ-aware synchronously replicated metadata and AZ-aware block replication. HopsFS-CL is a redesign of HopsFS, a version of HDFS with distributed metadata, and its design involved making replication protocols and block placement protocols AZ-aware at all layers of its stack: the metadata serving, the metadata storage, and block storage layers. In experiments on a real-world workload from Spotify, we show that HopsFS-CL, deployed in HA mode over 3 AZs, reaches 1.66 million ops/s, and has similar performance to HopsFS when deployed in a single AZ, while preserving the same semantics.

关键词： Metadata

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Logit Calibration for Non-IID and Long-Tailed Data in Federated Learning

Logit Calibration for Non-IID and Long-Tailed Data in Federa...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Huan Wang Lijuan Wang Jun Shen School of Cyber Engineering Xidian University Xi'an China School of Computing & Information Technology University of Wollongong Wollongong Australia

Federated learning (FL) strives to enable collaborative training of deep models on the distributed clients of different data without centrally aggregating raw data and hence improving data privacy. Nevertheless, a central challenge in training classification models in the federated system is learning with non-IID data. Most of the existing work is dedicated to eliminating the heterogeneous influence of non-IID data in a federated system. However, in many real-world FL applications, the co-occurrence of data heterogeneity and long-tailed distribution is unavoidable. The universal class distribution is long-tailed, causing them to become easily biased towards head classes, which severely harms the global model performance. In this work, we also discovered an intriguing fact that the classifier logit vector (i.e., pre-softmax output) introduces a heterogeneity drift during the learning process of local training and global optimization, which harms the convergence as well as model performance. Therefore, motivated by the above finding, we propose a novel logit calibration FL method to solve the joint problem of non-IID and long-tailed data in federated learning, called Federated Learning with Logit Calibration (FedLC). First, we presented a method to mitigate the local update drift by calculating the Wasserstein distance among adjacent client logits and then aggregating similar clients to regulate local training. Second, based on the model ensemble, a new distillation method with logit calibration and class weighting was proposed by exploiting the diversity of local models trained on heterogeneous data, which effectively alleviates the global drift problem under long-tailed distribution. Finally, we evaluated FedLC using a highly non-IID and long-tailed experimental setting, comprehensive experiments on several benchmark datasets demonstrated that FedLC achieved superior performance compared with state-of-the-art FL methods, which fully illustrated the effectiveness of

关键词： Training Data privacy Federated learning distributed databases Collaboration Data models Calibration

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Decentralized Data Privacy Protection and Cloud Auditing Security Management

Decentralized Data Privacy Protection and Cloud Auditing Sec...

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international conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： M. Mageshwari R. Naresh Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India

ISBN: (纸本)9781665462013

Decentralized data protection is becoming more important as more firms employ Cloud resources. Securing, protecting, and processing user data are primary issues that Cloud computing faces. This study’s primary goal is to detect and address attack protection in Cloud. Our objectives are to comprehend the present state of cloud computing security concerns and methods, identify future cloud computing security concerns to offer countermeasures for potential. Preventative measures to employing cloud services include data security and privacy concerns. The challenge is to create cloud platforms for integrating decentralized information systems to ensure the user’s privacy. For management units of distributed systems, the requirements analysis yielded a model for organizational levels of cloud systems. By bypassing the single-point-of-failure of TPA, our suggested approach may prevent service disruption and privacy-preserving concerns. The extensive security analysis proves that our suggested solution meets all the needed security criteria. We also implement our suggested method with PSO-Homomorphic Algorithm (PSO-HA) and evaluate its efficiency in real-world situations, demonstrating its efficacy, high efficiency, and portability. Our results show that our methodology for simultaneously-integrated recovery can be implemented in polynomial time and is thriving.

关键词： Privacy Cloud computing security Security management Data security Computational modeling Data protection Object recognition

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Proceedings of MCHPC 2019: Workshop on Memory Centric High Performance computing - Held in conjunction with SC 2019: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of MCHPC 2019: Workshop on Memory Centric High P...

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2019 IEEE/ACM Workshop on Memory Centric High Performance computing, MCHPC 2019

ISBN: (纸本)9781728160078

The proceedings contain 11 papers. The topics discussed include: performance evaluation of advanced features in CUDA uni?ed memory;generalized sparse matrix-matrix multiplication for vector engines and graph applications;a distributed deep memory hierarchy system for content-based image retrieval of big whole slide image datasets;performance evaluation of the intel optane dc memory with scientific benchmarks;UMap : enabling application-driven optimizations for page management;extending OpenMP map clause to bridge storage and device memory;and optimizing data layouts for irregular applications on a migratory thread architecture.

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