A parallel digital vlsi architecture for integrated support vector machine training and classification

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A PARALLEL DIGITAL VLSI ARCHITECTURE FOR INTEGRATED
SUPPORT VECTOR MACHINE TRAINING AND CLASSIFICATION
ABSTRACT:
This paper presents a parallel digital VLSI architecture for combined support
vector machine (SVM) training and classification. For the first time, cascade SVM, a powerful
training algorithm, is leveraged to significantly improve the scalability of hardware-based SVM
training and develop an efficient parallel VLSI architecture. The presented architecture achieves
excellent scalability by spreading the training workload of a given data set over multiple SVM
processing units with minimal communication overhead. Hardware-friendly implementation of
the cascade algorithm is employed to achieve low hardware overhead and allow for training over
data sets of variable size. In the proposed parallel cascade architecture, a multilayer system bus
and multiple distributed memories are used to fully exploit parallelism. In addition, the proposed
architecture is rather flexible and can be tailored to realize hybrid use of hardware parallel
processing and temporal reuse of processing resources, leading to good tradeoffs between
throughput, silicon overhead and power dissipation. Several parallel cascade SVM processors
have been designed with a commercial 90-nm CMOS technology, which provide up to a 561×
training time speedup and a significant estimated 21 859× energy reduction compared with the
software SVM algorithm running on a 45-nm commercial general-purpose CPU.

This document presents a VLSI architecture for an image compression system using vector quantization. It discusses motivation, objectives, and provides an introduction to image compression techniques including scalar and vector quantization. It describes the LBG algorithm for generating codebooks and proposes a multistage vector quantization approach. A cost-effective VLSI architecture for multistage vector quantization is presented, including the design of individual components like the image processing unit and decompression control unit. Results show the proposed architecture can compress images with good quality while maintaining low hardware complexity.

Vector quantization

Rajani Sharma

The document discusses efficient codebook design for image compression using vector quantization. It introduces data compression techniques, including lossless compression methods like dictionary coders and entropy coding, as well as lossy compression methods like scalar and vector quantization. Vector quantization maps vectors to codewords in a codebook to compress data. The LBG algorithm is described for generating an optimal codebook by iteratively clustering vectors and updating codebook centroids.

Performance Evaluation of Server Consolidation Algorithms in Virtualized Clo...

Susheel Thakur

The document discusses server consolidation algorithms for virtualized cloud environments. It analyzes the performance of Sandpiper, Khanna's, and Entropy algorithms under constant load. Sandpiper detects hotspots using monitoring and profiling, then migrates VMs to mitigate hotspots. Khanna's algorithm sorts PMs by residual capacity and VMs by usage to migrate VMs from overloaded to underloaded PMs. Entropy formulates VM allocation as a constraint satisfaction problem and uses a constraint solver to optimize resource usage and minimize migrations. The paper evaluates these algorithms in a virtualized test environment under constant loads.

A Study on Replication and Failover Cluster to Maximize System Uptime

YogeshIJTSRD

This document summarizes a study on using replication and failover clusters to maximize system uptime for cloud services. It discusses challenges in ensuring high availability of cloud services from a provider perspective. The study aims to present a high availability solution using load balancing, elasticity, replication, and disaster recovery configuration. It reviews related literature on digital media distribution platforms, content delivery networks, auto-scaling strategies, and database replication impact. It also covers methodologies like CloudFront, state machine replication, neural networks, Markov decision processes, and sliding window protocols. The scope is to build a scalable, fault-tolerant environment with disaster recovery and ensure continuous availability. The conclusion is that data replication and failover clusters are necessary to plan data

Server Consolidation Algorithms for Virtualized Cloud Environment: A Performa...

Susheel Thakur

This document summarizes research on server consolidation algorithms for virtualized cloud environments with variable workloads. It discusses how server consolidation aims to reduce the number of physical servers through virtualization and live migration of virtual machines between servers. The document reviews several existing server consolidation algorithms and studies their impacts on performance when migrating virtual machines. It then presents an evaluation of selected algorithms under variable workloads to reduce server sprawl, optimize power consumption, and balance loads across physical machines in cloud computing environments.

Dynamic resource allocation using virtual machines for cloud computing enviro...

IEEEFINALYEARPROJECTS

JAVA 2013 IEEE CLOUDCOMPUTING PROJECT Dynamic resource allocation using virtu...

IEEEGLOBALSOFTTECHNOLOGIES

33. dynamic resource allocation using virtual machines

muhammed jassim k

At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also. We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas. MD MANIKANDAN-9037291113,04954021113 softroniics@gmail.com

SAP Sybase IQ uses a technique called distributed query processing (DQP) that can improve query performance by breaking queries into pieces and distributing the pieces across multiple SAP Sybase IQ servers. DQP provides both intra-query and inter-query parallelism. It dynamically manages resources to balance workloads and avoid saturating the system. For DQP to be effective, the storage area network must have sufficient performance to support the increased parallelism.

International Journal of Engineering and Science Invention (IJESI)

inventionjournals

International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online

Virtualization in Distributed System: A Brief Overview

BOHR International Journal of Intelligent Instrumentation and Computing

Virtual machines are popular because of their efficiency, ease of use and flexibility. There has been an increasing demand for deployment of a robust distributed network for maximizing the performance of such systems and minimizing the infrastructural cost. In this paper we have discussed various levels at which virtualization can be implemented for distributed computing which can contribute to increased efficiency and performance of distributed computing. The paper gives an overview of various types of virtualization techniques and their benefits. For eg: Server virtualization helps to create multiple server instances from one physical server. Such techniques will decrease the infrastructure cost, make the system more scalable and help in full utilization of available resources.

Use VMware vSAN HCI Mesh to manage your vSAN storage resources and share them...

Principled Technologies

SERVER COSOLIDATION ALGORITHMS FOR CLOUD COMPUTING: A REVIEW

Susheel Thakur

This document summarizes a research paper on server consolidation algorithms for cloud computing environments. It discusses how server consolidation aims to reduce the number of underutilized servers through virtual machine migration and load balancing techniques. It reviews different server consolidation algorithms like Sandpiper that automate monitoring for hotspots, resizing or migrating virtual machines to improve resource utilization and energy efficiency. The document provides background on server consolidation and virtualization concepts and categorizes consolidation approaches before analyzing the Sandpiper algorithm in more detail.

IEEE Cloud computing 2016 Title and Abstract

tsysglobalsolutions

Dynamic resource allocation using virtual machines for cloud computing enviro...

IEEEFINALYEARPROJECTS

JAVA 2013 IEEE PARALLELDISTRIBUTION PROJECT Dynamic resource allocation using...

IEEEGLOBALSOFTTECHNOLOGIES

The document discusses a system that uses virtualization technology to dynamically allocate data center resources based on application demands. It aims to optimize the number of servers in use to support green computing while preventing server overload. The proposed system introduces a concept of "skewness" to measure uneven resource utilization across servers and develops heuristics to minimize skewness and improve overall utilization while avoiding overload and saving energy.

International Journal of Engineering Research and Development (IJERD)

IJERD Editor

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Dynamic resource allocation using virtual machines for cloud computing enviro...

JPINFOTECH JAYAPRAKASH

This document proposes a system for dynamically allocating data center resources using virtual machines based on application demands. It aims to optimize the number of servers used through minimizing "skewness", which measures uneven resource utilization across servers. By developing algorithms to predict future resource usage and reduce placement changes, the system seeks to avoid resource overload while also enabling green computing through efficient server utilization. A trace-driven simulation and experiments showed the algorithm achieved good performance.

F1034047

IJERD Editor

This document summarizes a research paper on dynamic consolidation of virtual machines in cloud data centers to manage overloaded hosts while maintaining quality of service constraints. It proposes using a Markov chain model and control algorithm to optimally detect host overloads by maximizing the average time between VM migrations, while meeting a specified QoS goal. The algorithm handles unknown workloads using a multisize sliding window approach. Evaluation shows the algorithm efficiently solves the problem of host overload detection as part of dynamic VM consolidation in cloud computing systems.

Design of storage benchmark kit framework for supporting the file storage ret...

IJECEIAES

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.

International Journal of Engineering Research and Development (IJERD)

IJERD Editor

Consistency As A Service:Auditing Cloud Consistency

LakshmiPriya UdayaKumar

Abstract: Cloud storage services have become commercially popular due to their overwhelming advantages. To provide ubiquitous always-on access, a cloud service provider (CSP) maintains multiple replicas for each piece of data on geographically distributed servers. A key problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this paper, we first present a novel consistency as a service (CaaS) model, which consists of a large data cloud and multiple small audit clouds. In the CaaS model, a data cloud is maintained by a CSP, and a group of users that constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not. We propose a two-level auditing architecture, which only requires a loosely synchronized clock in the audit cloud. Then, we design algorithms to quantify the severity of violations with two metrics: the commonality of violations, and the staleness of the value of a read. Finally, we devise a heuristic auditing strategy (HAS) to reveal as many violations as possible. Extensive experiments were performed using a combination of simulations and real cloud deployments to validate HAS. Keywords: consistency as a service (CaaS), two-level auditing, heuristic auditing strategy (HAS), Cloud Storage. Introduction: CLOUD computing has become commercially more popular, as it swears to guarantee scalability, elasticity, and high availability and at a cost which Is low. Initiated by the trend of the everything-as-a-service model. Data storages, virtualized infrastructure, virtualized platforms, as well as applications and softwares are being provided and consumed as services in the cloud. Cloud storage services can be included as a distinctive service in cloud computing, which considers the delivery of data storage as a service. More often billed on a utility computing basis, e.g., per gigabyte per month. Some of the examples may include Amazon SimpleDB, Microsoft Azure storage, etc. By using the cloud storage services, the data stored in the cloud can be accessed anytime and anywhere, using any device, without much caring about a large amount of investments when deploying the underlying hardware infrastructures. DOMAIN KNOWLEDGE: • Cloud computing, often referred to as simply “the cloud,” is the delivery of on-demand computing resources—everything from applications to data centres—over the Internet on a pay-for-use basis. • A cloud is basically a major distributed system where each portion of data is copied on multiple globally distributed servers to attain high accessibility and high performance. Existing System:  By using the cloud storage services, the customers can access data stored in a cloud anytime and anywhere using any device, without caring about a large amount of capital investment when deploying the underlying hardware infrastructures.  The cloud service pr

Benchmarking Scalability and Elasticity of DistributedDataba.docx

jasoninnes20

Benchmarking Scalability and Elasticity of Distributed Database Systems Jörn Kuhlenkamp Technische Universität Berlin Information Systems Engineering Group Berlin, Germany [email protected] Markus Klems Technische Universität Berlin Information Systems Engineering Group Berlin, Germany [email protected] Oliver Röss Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany [email protected] ABSTRACT Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and com- pare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling. 1. INTRODUCTION Modern distributed database systems, such as HBase, Cas- sandra, MongoDB, Redis, Riak, etc. have become popular choices for solving a variety of data management challenges. Since these systems are optimized for different types of workloads, decision makers rely on performance benchmarks to select the right data management solution for their problems. Furthermore, for many applications, it is not sufficient to only evaluate performance of one particular system setup; scalability and elasticity must also be taken into considera- tion. Scalability measures how much performance increases when resource capacity is added to a system, or how much performance decreases when resource capacity is removed, respectively. Elasticity measures how efficient a system can be scaled at runtime, in terms of scaling speed and performance impact on the concurrent workloads. Experiment reproduction. In section 4, we reproduce performance and scalability benchmarking experiments that were originally conducted by Rabl, et al. [14] for evaluating distributed database systems in the context of Enterprise Application Performance Management (APM) on virtualized infrastructure. In section 5, we discuss the problem of This work is licensed under the Creative Commons Attribution- NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain per- mission prior to any use beyond those covered by the license. Contact copyright holder by emailing [email protected] Articles from this volume were invited to present their results at the 40th International Conference on Very Large Data Bases, September 1st - 5th 2014, Hangzhou, China. Proceedings of the VLDB Endowment, Vol. 7, No. 13 Copyright 2014 VLDB Endowment 2150-8097/14/08. selec ...

Effective cost reduction for elastic clouds under spot instance pricing throu...

ieeepondy

Efficient fault tolerant cost optimized approach for scientific workflow via ...

IAESIJAI

Cloud computing is one of the dispersed and effective computing models, which offers tremendous opportunity to address scientific issues with big scale characteristics. Despite having such a dynamic computing paradigm, it faces several difficulties and falls short of meeting the necessary quality of services (QoS) standards. For sustainable cloud computing workflow, QoS is very much required and need to be addressed. Recent studies looked on quantitative fault-tolerant programming to reduce the number of copies while still achieving the reliability necessity of a process on the heterogeneous infrastructure as a service (IaaS) cloud. In this study, we create an optimal replication technique (ORT) about fault tolerance as well as cost-driven mechanism and this is known as optimal replication technique with fault tolerance and cost minimization (ORT-FTC). Here ORT-FTC employs an iterative-based method that chooses the virtual machine and its copies that have the shortest makespan in the situation of specific tasks. By creating test cases, ORT-FTC is tested while taking into account scientific workflows like CyberShake, laser interferometer gravitational-wave observatory (LIGO), montage, and sipht. Additionally, ORT-FTC is shown to be only slightly improved over the current model in all cases.

EFFICIENT TRUSTED CLOUD STORAGE USING PARALLEL CLOUD COMPUTING

International Journal of Technical Research & Application

- In last few years, rapidly increasing businesses and their capabilities & capacities in terms of computing has grown in very large scale. To manage business requirements High performance computing with very large scale resources is required. Businesses do not want to invest & concentrate on managing these computing issues rather than their core business. Thus, they move to service providers. Service providers such as data centers serve their clients by sharing resources for computing, storage etc. and maintaining all those.

You are the CIO of a medium size company tasked with modernizing the.pdf

fcaindore

Why might you want to look at a path other than the courts? In negotiation and mediation a middle ground is attempted to be found. Arbitration is more like private court with both parties pleading the case before a neutral third party. Please explain which form you would want to use in each dispute listed below and why. 1. Contract dispute between 2 parties. 2. Insurance claim between one person and their insurance company. 3. Insurance claim between you and someone else\'s insurance company. 4. A claim between you and a company over liability for injuries sustained in their store. Solution Answer: We might want to look at a path other than court, to have speedy resolution of the problem by direct negotiation and mediation with the involved parties..

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A parallel digital vlsi architecture for integrated support vector machine training and classification