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The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
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The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
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Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon Uni...MLconf
Fast, Cheap and Deep – Scaling Machine Learning: Distributed high throughput machine learning is both a challenge and a key enabling technology. Using a Parameter Server template we are able to distribute algorithms efficiently over multiple GPUs and in the cloud. This allows us to design very fast recommender systems, factorization machines, classifiers, and deep networks. This degree of scalability allows us to tackle computationally expensive problems efficiently, yielding excellent results e.g. in visual question answering.
Adaptive Replication for Elastic Data Stream ProcessingZbigniew Jerzak
A major challenge for cloud-based systems is to be fault tolerant so as to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss.
In state of the art data streaming systems either active replication or upstream backup are applied to ensure fault tolerance, which have a high resource overhead or a high recovery time respectively. This paper combines these two fault tolerance mechanisms in one system to minimize the number of violations of a user-defined recovery time threshold and to reduce the overall resource consumption compared to active replication. The system switches for individual operators between both replication techniques dynamically based on the current workload characteristics. Our approach is implemented as an extension of an elastic data stream processing engine, which is able to reduce the number of used hosts due to the smaller replication overhead. Based on a real-world evaluation we show that our system is able to reduce the resource usage by up to 19% compared to an active replication scheme.
PERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [(M/G/1) : (∞/GDM O...ijgca
The ever-increasing status of the cloud computing h
ypothesis and the budding concept of federated clou
d
computing have enthused research efforts towards in
tellectual cloud service selection aimed at develop
ing
techniques for enabling the cloud users to gain max
imum benefit from cloud computing by selecting
services which provide optimal performance at lowes
t possible cost. Cloud computing is a novel paradig
m
for the provision of computing infrastructure, whic
h aims to shift the location of the computing
infrastructure to the network in order to reduce th
e maintenance costs of hardware and software resour
ces.
Cloud computing systems vitally provide access to l
arge pools of resources. Resources provided by clou
d
computing systems hide a great deal of services fro
m the user through virtualization. In this paper, t
he
cloud data center is modelled as
queuing system with a single task arrivals
and a task request buffer of infinite capacity.
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
When data size grows in terms of sample count, feature count and model parameter count, things go crazy. The slideshow presents an overview of what to expect and how to handle them.
UNET is a multi-core based, high performance machine learning framework, built on top of Spark, supporting both data parallel and model parallel in massive scale.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
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Using Topic Modeling to Study Everyday "Civic Talk" and Proto-political Engag...Tuukka Ylä-Anttila
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Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
CS 301 Computer ArchitectureStudent # 1 EID 09Kingdom of .docxfaithxdunce63732
CS 301 Computer Architecture
Student # 1
E
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
Student # 2
H
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
1
1. Introduction
High-performance processor design has recently taken two distinct approaches. One approach is to increase the execution rate by increasing the clock frequency of the processor or by reducing the execution latency of the operations. While this approach is important, much of its performance gain comes as a consequence of circuit and layout improvements and is beyond the scope of this research. The other approach is to directly exploit the instruction-level parallelism (ILP) in the program and to issue and execute multiple operations concurrently. This approach requires both compiler and microarchitecture support.
Traditional processor designs that issue and execute at most one operation per cycle are often called scalar designs. Static and dynamic scheduling techniques have been used to achieve better-than scalar performance by issuing and executing more than one operation per cycle. While Johnson[7] defines a superscalar processor as a design that achieves better-than scalar performance, popular usage of this term refers exclusively to those processors that use dynamic scheduling techniques. For clarity, we use instruction-level parallel processors to refer to the general class of processors that execute more than one operation per cycle of the computer both at the personal level, or the level of a small network of computers to do not require more of these types.
The primary static scheduling technique uses the compiler to determine sets of operations that have their source operands ready and have no dependencies within the set. These operations can then be scheduled within the same instruction subject only to hardware resource limits. Since each of the operations in an instruction is guaranteed by the compiler to be independent, the hardware is able to is- sue and execute these operations directly with no dynamic analysis. These multi-operation instructions are very long in comparison with traditional single-operation instructions and processors using .
Get a clearer picture of potential cloud performance by looking beyond SPECra...Principled Technologies
When we ran various workloads on two Azure instances, the performance differences between the instances varied considerably and differed from SPECrate 2017 Integer scores
This was a talk, largely on Kamaelia & its original context given at a Free Streaming Workshop in Florence, Italy in Summer 2004. Many of the core
concepts still hold valid in Kamaelia today
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.