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Optimized Heuristic Technique for Task Scheduling in Secure Cloud Storage Environment

Optimized Heuristic Technique for Task Scheduling in Secure ...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： S. Maheshwari J. Anitha Ruth Department of Computer Science SRMIST A.M. Jain College Vadapalani Chennai Department of Master of Computer Application Faculty of Science and Humanities Vadapalani Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Cloud computing is a highly scalable and frequently used expertise in today's rapidly evolving information and computer landscape. It runs on the payper-use, remote access, Internet-based, and on demand ideas, offering consumers a shared pool of programmable resources. However, as the number of user requests grows, efficient task scheduling and resource allocation have become critical criteria for effective workload balancing among cloud resources. It consequently improves overall cloud system performance and offers a new way to deliver services in distributed systems, paving the way for the execution of scientific applications. Scheduling and processing millions of activities using Cloud resources in a lucrative manner with minimal computing time is an attractive study area. Current scheduling algorithms ignore dependent and independent jobs. To overcome this drawback, we anticipate a new dignified technique called the Optimized heuristic Min-Min algorithm, which is stimulated by the Min-Min algorithm for task scheduling and focuses on the concern of dependent and independent tasks, as well as the parallel processing of independent tasks, which directly results in profit by minimizing computation time. This study aims to produce optimal results by minimizing completion time and maximizing resource utilization, leading to higher throughput. Kerberos authentication service is implementing for cloud data storage security. Kerberos used in this work for creating tickets and granting them to every user in order to provide high security.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms System performance parallel processing Throughput Scheduling Security Resource management Secure storage

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Substructure Discovery in Heterogeneous Multilayer Networks

Substructure Discovery in Heterogeneous Multilayer Networks

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IEEE international conference on Big Knowledge (ICBK)

作者： Kiran Bolaj Abhishek Santra Sharma Chakravarthy IT Lab and Department of Computer Science & Engineering The University of Texas at Arlington

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Graph mining analyzes graphs to find core substructures (connected subgraphs) in applications that are modeled using graphs. These identified substructures, based on frequency or some other metric, are important as they reveal an inherent feature or property in the given graph/forest. Interesting substructures that are frequent or compress the graph well offer insights into hidden regularities. The process of finding these interesting patterns using unsupervised learning is termed substructure discovery. SUBDUE is an early main-memory algorithm developed for substructure discovery. Since then, this algorithm has been extended using disk-based, database-oriented approaches, and more recently using the Map/Reduce paradigm to exploit distributed and parallel processing. Graph sizes have increased steadily, thanks to the advent of the Internet and social network applications. To model complex datasets – with multiple types of entities and relationships - multilayer networks (or MLNs) have been shown to be effective. MLNs have also been shown to be superior as compared to simple and attributed graphs for modeling complex data. MLNs are also useful for modeling different data types using distinct layers. This paper focuses on substructure discovery in heterogeneous multilayer networks (one type of MLN) using the novel decoupling-based approach. In this approach, each layer is processed independently and then the results from two or more layers are composed to identify substructures in the entire MLN. The algorithm is designed and implemented, including the composition part, using the Map/Reduce paradigm for understanding speedup. After validating accuracy of results with ground truth, we analyze speedup and response time of the proposed algorithm and approach through extensive experimental analysis on large synthetic datasets with diverse graph characteristics.

关键词： Measurement Social networking (online) Knowledge graphs parallel processing Nonhomogeneous media Data models Internet Time factors Unsupervised learning Synthetic data

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Simplifying Graph-parallel Computation in Apache Spark with GraphX

Simplifying Graph-Parallel Computation in Apache Spark with ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Lasya Vyakaranam Ramakrishnan Raman Harshal Shah Nilamadhab Mishra R. Meenakshi S. Murugan Symbiosis Law School (SLS) Pune Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX’s performance, expanding its features, and integrating it with Spark’s ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores.

关键词： Histograms Technological innovation Social networking (online) Scalability Data visualization Cluster computing Distance measurement Sparks Recommender systems Convergence

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A Review of Authentication techniques used for Security in Cloud Computing

A Review of Authentication Techniques used for Security in C...

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Mandeep Kaur Prachi Garg Department of Computer Applications Panipat Institute of Engineering and Technology Samalkha Research Scholar DCSE Maharishi Markandeshwar University Mullana India Department of CSE Maharishi Markandeshwar University Mullana India

ISBN: (纸本)9781665454025

The changes in technologies has also changed the way we compute. Computing applications provide various types of functionalities. However, a common thing is to secure the same computing system. It requires a high level of developer skills to secure a system. Generally, verifying users before access of services, encryption of data, and techniques of parallel access of information by multiple users is done to ensure only valid users can access the services. One need to verify person, device, process, or service before it access the related service(s). In this paper, we present a review of authentication techniques used in computing computing. It elaborates methods used for traditional authentication using articles, letters, people, passwords, one-time passwords, digital certificates, two-way authentication to latest behavioural, doodles, image sequence, gestures based recognition of users using biometrics, gait-based and their behavioural analytics. It also discusses key features of various methods including gaps and scope of improvement.

关键词： Cloud computing Image recognition Authentication Passwords Grid computing Image sequences Security

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Advancements in Computer Vision: A Comprehensive Review

Advancements in Computer Vision: A Comprehensive Review

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Innovations and Challenges in Emerging Technologies (ICICET), international conference on

作者： Poorva Agrawal Reyon Bose Gopal Kumar Gupta Gagandeep Kaur Samiksha Paliwal Aarya Raut Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

Computer vision is a versatile area that allows a computer to understand and analyze images from the environment. This paper focuses on a comprehensive discussion of where computer vision is today in light of its past, most recent accomplishments, and predictions of where it could go next. The evolution of computer vision over the past few decades has been marked by remarkable progress, driven by advancements in three key areas: utilize deep learning, hardware, and image-processing methods. Artificial neural networks are now commonly used in deep learning, which has improved the abilities of machines to recognize, classify, and understand visual content. Computationally speaking, parallel with deep learning, other kinds of hardware, like GPU chips, can be used to perform such functions better. This has made it easy to train and deploy complex deep learning algorithms for real-time computer vision applications with expanded reach into many facets. In addition, image processing has been dramatically improved, making it possible to process and enhance incoming visual data. Traditional and most recent techniques for Computational Photography have enabled the dependable and precise computer-vision system.

关键词： Deep learning Computer vision Visualization Technological innovation Reviews Image processing Transforms

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A Systematic Review on Personal Route Prediction techniques based on Trajectory data

A Systematic Review on Personal Route Prediction Techniques ...

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Dipti Goel Neelam Duhan Komal Kumar Bhatia Department of Computer Engineering J.C. Bose University of Science & Technology YMCA Faridabad India

ISBN: (纸本)9781665454025

With the advancement of location-acquisition and mobile computing devices in today’s life, the trajectory data obtained from Global Positioning System (GPS) is becoming large which makes route prediction a very challenging task. The route prediction can be done in two modes-offline route prediction and online route prediction in terms of acquiring performance and accuracy. There are many different techniques of route prediction which mainly consists of three consecutive tasks- i) Route abstraction ii) Frequent route pattern mining iii) Route Prediction applied in the context of different application domains. Route prediction plays an important role in many location-based applications including vehicular ad-hoc networks, route navigation, traffic control and congestion estimation, Place recommendation and many more. Trajectory data have different useful information hiding into it like background data and contextual information. So, Privacy is a big concern while sharing this personal trajectory data with the server predicting the best optimal route. Many techniques have been proposed by researchers for ensuring privacy of user data along with maintaining availability and usability of the data. Rather than doing all pre-processing computations on a single machine, many algorithms have been proposed to make system scalable. In this paper, a comparative analysis of various existing techniques of route prediction along with privacy preservation and scalability issues is discussed with pros and cons. The main aim of this paper is to give insights of the different algorithms of the route prediction with the directions of future research.

关键词： Data privacy Systematics Scalability Traffic control Prediction algorithms Trajectory Servers

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Gluon-Async: A Bulk-Asynchronous System for distributed and Heterogeneous Graph Analytics 19

Gluon-Async: A Bulk-Asynchronous System for Distributed and ...

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Proceedings of the international conference on parallel Architectures and Compilation techniques

作者： Roshan Dathathri Gurbinder Gill Loc Hoang Vishwesh Jatala Keshav Pingali V. Krishna Nandivada Hoang-Vu Dang Marc Snir University of Texas at Austin IIT Madras University of Illinois at Urbana-Champaign

ISBN: (纸本)9781728136134

distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP) programming and execution model. BSP permits bulk-communication and uses large messages which are supported efficiently by current message transport layers, but bulk-synchronization can exacerbate the performance impact of load imbalance because a round cannot be completed until every host has completed that round. Asynchronous distributed graph analytics systems circumvent this problem by permitting hosts to make progress at their own pace, but existing systems either use global locks and send small messages or send large messages but do not support general partitioning policies such as vertex-cuts. Consequently, they perform substantially worse than bulk-synchronous systems. Moreover, none of their programming or execution models can be easily adapted for heterogeneous devices like *** this paper, we design and implement a lock-free, non-blocking, bulk-asynchronous runtime called Gluon-Async for distributed and heterogeneous graph analytics. The runtime supports any partitioning policy and uses bulk-communication. We present the bulk-asynchronous parallel (BASP) model which allows the programmer to utilize the runtime by specifying only the abstract communication required. applications written in this model are compared with the BSP programs written using (1) D-Galois and D-IrGL, the state-of-the-art distributed graph analytics systems (which are bulk-synchronous) for CPUs and GPUs, respectively, and (2) Lux, another (bulk-synchronous) distributed GPU graph analytical system. Our evaluation shows that programs written using BASP-style execution are on average ~ 1.5× faster than those in D-Galois and D-IrGL on real-world large-diameter graphs at scale. They are also on average ~ 12× faster than Lux. To the best of our knowledge, Gluon-Async is the first asynchronous distributed GPU graph analytics system.

关键词： BSP model

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The parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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international conference on Data Engineering

作者： Feng Cheng Jiang Xiao Cunyang Liu Shijie Zhang Yifan Zhou Bo Li Baochun Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics - silent assets - and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions.

关键词： Sharding Directed acyclic graph distributed ledger Avatars Scalability Receivers Throughput

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Clustering techniques in Vehicular Adhoc Networks- A Survey

Clustering Techniques in Vehicular Adhoc Networks- A Survey

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Gurtej Kaur Meenu Khurana Amandeep Kaur Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665454025

The vehicular adhoc networks (VANETs) is an integral part of the intelligent transportation system required for delivering safety and non-safety messages to vehicles in real-time via vehicle to vehicle and vehicle to everything communication modes. The purpose of VANETs is to provide efficient routing, message security, congestion-free traffic, and other services related to entertainment and comfortable journey. All these VANETs services, at the core, are related to efficient and secure data dissemination. In this paper, we have focused on clustering in VANETs for efficient data dissemination and resource sharing tasks. Considering the importance of fog computing for fast computing and processing, we have focused on clustering techniques in Fog-based VANET as well that can enhance the range of transmission and expand the number of available resources. This survey paper discusses the existing clustering techniques in VANETs scenario, cluster head selection parameters, the context-based techniques, and cluster performance parameters in VANETs.

关键词： Data dissemination Vehicular ad hoc networks Transportation Routing Real-time systems Safety Security

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