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2023 International Conference on Computer Applications Technology, CCAT 2023

作者： Han, Boyuan Liang, Zhihong Qin, Mingming Jiang, Rongxin Dai, Wei Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Big Data and Cloud Computing Center Kunming China Yunnan Pu'er Technical School Yunnan Pu'er Technical School Kunming China

ISBN: (纸本)9798350301540

The integration of blockchain and federated learning exhibits a highly synergistic relationship. Blockchain, with its assurance of trustworthiness in computational processes and data, combined with federated learning's ability to make data available yet invisible, mutually complement each other and have shown initial advancements across various domains. Consequently, research and analysis of blockchain-based federated learning technology have garnered considerable *** blockchain enhances the federated learning process, it also poses a number of problems, such as increased communication overhead and lack of computational *** article aims to provide a comprehensive examination of the subject. Firstly, it introduces the fundamental concept of federated learning technology, along with privacy protection techniques and the existing threat landscape. Secondly, it analyzes the core principles of blockchain-based federated learning technology, delving into current research status, progress, and outlining the various research directions involved. Furthermore, it conducts an in-depth analysis and summarization of the integration between federated learning and blockchain technology, identifying numerous challenges that need to be addressed within the relevant domains. Finally, it recognizes and analyzes the future development of this combination, emphasizing the need to overcome existing *** summary, this article highlights the amalgamation of federated learning and blockchain technology. It identifies and analyzes the many challenges that require resolution within the pertinent fields, while also offering insights into the future development of this integration. © 2023 IEEE.

关键词： Blockchain

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Multi-cloud DRaaS using OpenStack Keystone Federation

Multi-cloud DRaaS using OpenStack Keystone Federation

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International Conference on Advances in computing and Communication Engineering (ICACCE)

作者： Sudheendra Harwalkar Dinkar Sitaram Shivangi Jadon Center for Cloud Computing and Big Data PES University Bangalore India

ISBN: (数字)9781728132501

ISBN: (纸本)9781728132518

Disaster recovery (DR) for the distributed workload and data in multi-cloud environments is very complex and challenging. There exist point tools for failover from one cloud to another cloud with active/passive data replications or with simple backup of private cloud data to public cloud(s). However, these point solutions are not integrated with the cloud environment. In this paper, we demonstrate how cost-effective DR process/solution can be accomplished with our Federated cloud Services Framework (middleware), which is built upon OpenStack [16], by leveraging existing OpenStack functionalities. As our solution leverages the functionality of the federated cloud environment, it generalizes easily to multi-cloud scenarios, and is better integrated with the cloud infrastructure. We also compare our solution to current DR process/solutions available in the market.

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Multicloud- auto scale with prediction and delta correction algorithm

Multicloud- auto scale with prediction and delta correction ...

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International Conference on Intelligent computing, Instrumentation and Control Technologies (ICICICT)

作者： Sudheendra Harwalkar Dinkar Sitaram Dhanaraj V Kidiyoor Milan M L Ornella D'souza Radhika Agarwal Yamini Agarwal Center for Cloud Computing and Big Data PES University Bangalore India

IT enabled enterprise organizations are embracing multi-cloud to handle their peak loads in addition to their private cloud (own data centers) and the market prediction indicates that by year 2020, about 80% of enterprise will have or working towards multi-cloud enablement for their IT needs. There are multiple products from third-party supporting multi-cloud and also some of the cloud service providers have already enabled the service using proprietary cloud management tools with custom built plug-ins. One of the major challenges in multi-cloud is efficient resource utilization and cost savings without vendor/broker lock-in. At present, multi-cloud interface standardization is still in draft. In this paper we propose handling of cloud burst at peak load with our OpenStack [9] Middleware Framework, which uses default OpenStack services. It also has built-in support for prediction, resource allocation and proactive workload migration techniques which can be integrated easily with cloud provider solutions.

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A Fast Geometric Modeling Method for Cold Blades 1

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Asia-Pacific International Symposium on Aerospace Technology, APISAT 2018

作者： Liu, Kaicheng Wang, Jianjun Ni, Bowen School of Energy and Power Engineering Beihang University Beijing100191 China Cloud Computing and Big Data Center China Mobile Group Xinjiang Company Limited Xinjiang830018 China

ISBN: (数字)9789811333057

ISBN: (纸本)9789811333040

A geometric modeling method for cold blade is proposed based on hot-to-cold finite element (FE) transformation calculation. The main idea is to interpolate the deformation result calculated from hot-to-cold transformation to the control points of the hot blade profile. Subtract the interpolated displacement values from the coordinates of the hot control points then the coordinates of the cold control points are obtained. The resulting coordinates could be directly used for solid modeling in commercial CAD programs. Both the hot-to-cold FE transformation calculation part and the output part of the cold blade profile’s control point coordinates could be accomplished in a whole process automatically in the commercial FE program ANSYS15.0. The flow chart of the entire process is given attached with ANSYS APDL commands. Finally, a numerical example based on a blisk sector model is provided. The hot-to-cold FE transformation calculation is performed, the finite element model (FEM) is obtained and solid model is built. Additionally, the effect of mesh density on the modeling accuracy is investigated. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer aided design

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Sentiment analysis of mixed language employing Hindi-English code switching

Sentiment analysis of mixed language employing Hindi-English...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Dinkar Sitaram Savitha Murthy Debraj Ray Devansh Sharma Kashyap Dhar Center for Cloud computing and Big Data (CCBD) PES University Bangalore

Sentiment analysis has emerged as one of the prominent research branches because of its endless usages and applications. Monitoring social media, forums, blogs and other online resources for customer reviews, product competition and survey responses to understand customer insight is of significant importance in business analytics. With the proliferation of informal user generated data online, the use of mixed language has become a common phenomenon. Mixed language arises through the use of linguistic code switching (LCS) or the practice of using more than one language in a single sentence. Such mixed language has rarely been a subject of sentiment analysis before. The lack of a clear grammatical structure renders the previous approaches to sentiment analysis ineffective for such text. In this paper, we propose a strategy to determine the sentiment of sentences written in a mixed language comprising of Hindi and English lexicons. Our technique can be used to analyze the sentiment of data belonging to any one of the source languages as well as the mixed language data. Grammatical transitions which are very common in mixed language have been taken into account during the sentiment analysis. We demonstrate the effectiveness of the proposed approach via case studies on social media data sets.

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DCSim: Cooling Energy Aware VM Allocation Framework for a cloud data center

DCSim: Cooling Energy Aware VM Allocation Framework for a Cl...

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International Conference on Advances in computing and Communication Engineering (ICACCE)

作者： Priyank Bhandia Ravi S. Anupindi Pavan Yekbote Nikhil Singh H.L. Phalachandra Dinkar Sitaram Center for Cloud Computing and Big Data PES University Bangalore India

ISBN: (数字)9781728132501

ISBN: (纸本)9781728132518

Explosion of digital content has resulted in large amounts of resources being provisioned and managed for various applications in cloud data centers. Energy consumption in these large cloud data centers is a rising concern, accounting for 1.3% of the worlds electricity consumption [1]. data center cooling accounts for 40% of this energy consumption [2]. Of the various mechanisms available for studying the energy consumption in data centers, a simulation based approach is quite popular. In this paper, we propose DCSim, a configurable extension to cloudSim, a popularly used cloud infrastructure and simulation framework. cloudSim provides coarse power models to calculate total energy consumption in a data center for a given workload, but has no provision to factor in the data center topology and current cooled area into this power model. This makes building intelligent cooling energy aware allocation policies in cloudSim difficult. In DCSim, we introduce a novel data center model that addresses the shortcomings of cloudSim by encapsulating concepts of Racks, Aisles, Sectors and Zones (collectively referred to as DCObjects). We also provide the capability to model the cooling of these DCObjects. This makes the study of cooling aware resource provisioning for workloads easier. The DCObjects and the data center model presented are designed to be fully extensible to support future developments in this area. In this work we also implement a Cooling aware VM allocation policy, and demonstrate using multiple algorithms, that this VM allocation policy will effectively reduce the total data center power consumption by 18.18% over an algorithm which does not factor in the cooled DCObjects.

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Predicting Virtual Machine Resource Consumption Based on Optimized Grey Model

Predicting Virtual Machine Resource Consumption Based on Opt...

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第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Shi Aiwu Gao Di He Kai Cloud Computing and Big Data Research Center Wuhan Textile University

In order to relatively change the computing specifications of virtual machines(VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.

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Predicting the Next Move: Determining Mobile User Location using Semantic Information

Predicting the Next Move: Determining Mobile User Location u...

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International Conference on Advances in computing, Communications and Informatics

作者： Nitin Bhyri Gautham V. Kidiyoor Varun, S. K. Subramaniam Kalambur Dinkar Sitaram Chid Kollengode Center of Cloud Computing and Big Data PES Institute of technology DataXu

ISBN: (纸本)9781479987931

The location of a mobile user is used to deliver context sensitive information like advertisements and deals. Predicting the future possible locations of a mobile user can help target specific services. Nokia provided researchers with data collected from around 200 mobile users over a period of about 2 years for the purpose of research. Previous efforts have attempted either to predict the location of the user or the semantics associated with a location. In our work, we demonstrate that using a multi-level approach by predicting the semantics of a location and then the specific location within helps improve the accuracy of the prediction. We have shown that for certain semantic locations, the prediction accuracy can be as high as 90%.

关键词： Mobile User Semantic Location

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Performance tuning analysis of spatial operations on Spatial Hadoop cluster with SSD

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Procedia Computer Science 2020年 167卷 2253-2266页

作者： Prafullata Auradkar Tejas Prashanth Suraj Aralihalli Sreeniketh Pradeep Kumar Dinkar Sitaram Center for Cloud Computing and Big Data Department of Computer Science and Engineering PES University Bangalore Karnataka India

Solid State Drives have shown promising results in improving the performance of MapReduce jobs. Previous work in the usage of SSD consisted of evaluation of its incorporation in Hadoop using benchmarking tests, such as WordCount, TeraGen, TeraSort and so on. It was empirically observed that the type of Map Reduce implementation, specifically the nature of the processing performed during the shuffle phase, has a major role in the overall outcome. This finding opens a plethora of possibilities of exploring the impact of storage media on spatial data processing in big data frameworks. Spatial big data frameworks, such as Spatial Hadoop, have been successfully used for several spatial operations, but the effect of SSD utilization in these operations is yet to be evaluated. This paper evaluates the feasibility of adopting SSD in a Spatial Hadoop cluster. The contributions presented are 1) Characterization of Spatial Hadoop jobs that would benefit from the usage of SSD. 2) Identification of the optimal mix of Solid State Drives and Hard Disk Drives for HDFS for each type of job. 3) Evaluation of cluster configuration parameters that influence the speed-up. The cluster exhibited a speed-up of up to 15% for I/O intensive spatial operations, while other compute-intensive operations could not harness a similar gain.

关键词： Spatial Hadoop Solid State Drives Performance analysis Global indexes Computational geometry

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Energy efficient data center management under availability constraints

Energy efficient data center management under availability c...

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Annual IEEE Systems Conference

作者： Dinkar Sitaram H.L. Phalachandra Gautham S Swathi H V Sagar TP Dept. CSE PES University Center for Cloud Computing and Big Data Bangalore India Center for Cloud Computing and Big Data Dept. CSE PES University Bangalore India

Due to increasing power consumption and cost, data center energy conservation has become increasingly important. As a result, a number of algorithms for optimizing compute, storage, and networking have been devised. However, these algorithms focus on job distribution based upon optimizing energy efficiency, and do not take availability into consideration. For example, if there are 3 servers, allocating workloads based only upon energy efficiency may result in consolidation of the workload into only 1 server, resulting in a single point of failure. This paper outlines the importance of availability and designs a hill climbing algorithm to prevent failure zone failure and hence also reduce the energy consumption by the datacenter.

关键词： Servers Algorithm design and analysis Resource management Redundancy Gravity Hardware Energy efficiency

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