Dr. Konstantinos Giannoutakis presents the CloudLightning simulator, a bespoke cloud simulation engine built for modelling and simulating heterogeneous resources as well as self-organising systems.
This presentation was given at the CloudLightning Conference held in conjunction with NC4 2017 in Dublin City University on 11th April 2017.
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.
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
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
Dr. Konstantinos Giannoutakis presents the CloudLightning simulator, a bespoke cloud simulation engine built for modelling and simulating heterogeneous resources as well as self-organising systems.
This presentation was given at the CloudLightning Conference held in conjunction with NC4 2017 in Dublin City University on 11th April 2017.
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.
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
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
NETWORKING guess paper for CCAT examination of C-DAC for Jun Jul 2013
For more details please visit http://cdacguru.wordpress.com or http://facebook.com/cdacguru
this is good question set for CCAT exam and alos for CCEE
for more details please visit
http://acts.cdac.in
http://cdacguru.wordpress.com
http://fb.com/cdacguru
Generalized formula for Square Numbers in Hyper DimensionsKumaran K
Generalized Formula For Consecutive Square Number’s Arithmetic Progression in Hyper Dimension or Multi Dimension
[Hyper Dimension or Multi Dimension = 2 Dimension, 3 Dimension, 4 Dimension…….Nth Dimension or infinite Dimension]
பல பரிமாணத்தில் அமைந்துள்ள இரண்டாம் அடுக்கு வர்க்க தொடர் எண்களின் கூட்டு தொடர...Kumaran K
பல பரிமாணத்தில் அமைந்துள்ள இரண்டாம் அடுக்கு வர்க்க தொடர் எண்களின் கூட்டு தொடர் காணும் பொதுவான சூத்திரம்
[இரண்டாம் பரிமாணம், மூன்றாம் பரிமாணம் , ...... ,பல பரிமாணம் அல்லது முடிவில்லா பரிமாணம்]
Samples of competitive examination questions: part IIAli I. Al-Mosawi
كتاب (نماذج أسئلة الإمتحان التنافسي/ إعداد علي إبراهيم الموسوي)
الجزء الثاني:
ماجستير علوم في هندسة البرامجيات جامعة تكنولوجيا المعلومات والإتصالات ... دبلوم عالي في تكنولوجيا مواقع الويب جامعة تكنولوجيا المعلومات والإتصالات ... ماجستير جغرافية كلية التربية للعلوم الإنسانية/ إبن رشد جامعة بغداد ... ماجستير جغرافية كلية التربية جامعة تكريت ... ماجستير هندسة مدني كلية الهندسة جامعة بابل ... دكتوراه علوم تربة وموارد مائية زراعة جامعة بغداد ... ماجستير علوم تربة وموارد مائية زراعة جامعة بغداد ... دكتوراه تأريخ حديث كلية التربية الجامعة المستنصرية ... دكتوراه تأريخ حديث كلية التربية للبنات جامعة بغداد ... ماجستير تأريخ حديث كلية التربية الجامعة المستنصرية ... ماجستير تأريخ حديث ومعاصر كلية التربية الجامعة المستنصرية ... ماجستير تأريخ إسلامي كلية التربية الجامعة المستنصرية ... ماجستير فيزياء كلية التربية الجامعة المستنصرية ... ماجستير رياضيات كلية التربية جامعة تكريت ... ماجستير رياضيات كلية علوم الرياضيات والحاسوب جامعة الكوفة ... ماجستير تأريخ كلية الآداب جامعة بغداد ... دكتوراه لغة عربية كلية الآداب جامعة بغداد ... ماجستير لغة عربية كلية الآداب جامعة بغداد ... ماجستير إنكليزي قسم اللغة الإنكليزية كلية التربية جامعة القادسية.
NETWORKING guess paper for CCAT examination of C-DAC for Jun Jul 2013
For more details please visit http://cdacguru.wordpress.com or http://facebook.com/cdacguru
this is good question set for CCAT exam and alos for CCEE
for more details please visit
http://acts.cdac.in
http://cdacguru.wordpress.com
http://fb.com/cdacguru
Generalized formula for Square Numbers in Hyper DimensionsKumaran K
Generalized Formula For Consecutive Square Number’s Arithmetic Progression in Hyper Dimension or Multi Dimension
[Hyper Dimension or Multi Dimension = 2 Dimension, 3 Dimension, 4 Dimension…….Nth Dimension or infinite Dimension]
பல பரிமாணத்தில் அமைந்துள்ள இரண்டாம் அடுக்கு வர்க்க தொடர் எண்களின் கூட்டு தொடர...Kumaran K
பல பரிமாணத்தில் அமைந்துள்ள இரண்டாம் அடுக்கு வர்க்க தொடர் எண்களின் கூட்டு தொடர் காணும் பொதுவான சூத்திரம்
[இரண்டாம் பரிமாணம், மூன்றாம் பரிமாணம் , ...... ,பல பரிமாணம் அல்லது முடிவில்லா பரிமாணம்]
Samples of competitive examination questions: part IIAli I. Al-Mosawi
كتاب (نماذج أسئلة الإمتحان التنافسي/ إعداد علي إبراهيم الموسوي)
الجزء الثاني:
ماجستير علوم في هندسة البرامجيات جامعة تكنولوجيا المعلومات والإتصالات ... دبلوم عالي في تكنولوجيا مواقع الويب جامعة تكنولوجيا المعلومات والإتصالات ... ماجستير جغرافية كلية التربية للعلوم الإنسانية/ إبن رشد جامعة بغداد ... ماجستير جغرافية كلية التربية جامعة تكريت ... ماجستير هندسة مدني كلية الهندسة جامعة بابل ... دكتوراه علوم تربة وموارد مائية زراعة جامعة بغداد ... ماجستير علوم تربة وموارد مائية زراعة جامعة بغداد ... دكتوراه تأريخ حديث كلية التربية الجامعة المستنصرية ... دكتوراه تأريخ حديث كلية التربية للبنات جامعة بغداد ... ماجستير تأريخ حديث كلية التربية الجامعة المستنصرية ... ماجستير تأريخ حديث ومعاصر كلية التربية الجامعة المستنصرية ... ماجستير تأريخ إسلامي كلية التربية الجامعة المستنصرية ... ماجستير فيزياء كلية التربية الجامعة المستنصرية ... ماجستير رياضيات كلية التربية جامعة تكريت ... ماجستير رياضيات كلية علوم الرياضيات والحاسوب جامعة الكوفة ... ماجستير تأريخ كلية الآداب جامعة بغداد ... دكتوراه لغة عربية كلية الآداب جامعة بغداد ... ماجستير لغة عربية كلية الآداب جامعة بغداد ... ماجستير إنكليزي قسم اللغة الإنكليزية كلية التربية جامعة القادسية.
Machine Learning for Big Data Analytics: Scaling In with Containers while Sc...Ian Lumb
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
Armed with nothing more than an Apache Spark toting laptop, you have all the trappings required to prototype the application of Machine Learning against your data-science needs. From programmability in Scala, Java or Python, to built-in support for Machine Learning via MLlib, Spark is an exceedingly effective enabler that allows you to rapidly produce results.
Of course, as soon as your prototyping proves successful, you'll want to scale out to embrace the volume, variety and velocity that characterizes today's Big Data demands... in production. Because Spark is as comfortable on an isolated laptop as it is in a distributed-computing environment, addressing Big Data requirements in production boils down to effectively and efficiently embracing containers and clusters for Big Data Analytics.
And this is where offerings from Univa shine - i.e., in making the transition from prototype to production completely seamless. For some use cases, it makes sense to scale-in Spark based applications within Docker containers via Univa Grid Engine Container Edition or Navops by Univa; whereas in others, Spark is interfaced (as a Mesos-compliant framework) with Univa Universal Resource Broker, to permit scaling out on a cluster. In both scenarios, your production Spark applications are scheduled alongside other classes of workload - without a need for dedicated resources.
Agenda:
• Overview of Apache Spark as a platform for Deep Learning - from Python-based Jupyter Notebooks to Spark's Machine Learning library MLlib
• Overview of prototyping Machine Learning via Apache Spark on a laptop - without and within Docker containers
• Introductions to Univa Grid Engine Container Edition and Univa Universal Resource Broker plus Navops by Univa
• Overview of production Big Data Analytics platforms for Machine Learning
• Docker-containerized Apache Spark and Univa Grid Engine Container Edition
• Docker-containerized Apache Spark and Navops by Univa
• Apache Spark plus Univa Universal Resource Broker
• Introducing support for GPUs without and within Docker containers
• Use case example - using Machine Learning to classify data from Twitter without and within Docker containers
• Summary and next steps
Watch On Demand Anytime via http://www.univa.com/resources/webinar-machine-learning.php
SA_IT241_5
• Compromise between access lists and capability lists. • Each object has list of unique bit patterns, called locks. • Each domain as list of unique bit patterns called keys.
An investigation into Cluster CPU load balancing in the JVMCalum Beck
This was my dissertation piece for my Software Engineering Degree.
The JVM CPU Cluster Balancer is a scalable, proof of concept system designed to distribute processes over a network to perform multiple tasks at once, in a language of high abstraction. Once distributed, workers return results to an access server, all while monitoring their respective CPUs for computational stress in terms of CPU usage. CPU’s incurring set stress then have their respective processes moved to a less intensive area in the cluster, balancing work overall.
Stream and Batch Processing in the Cloud with Data Microservicesmarius_bogoevici
The future of scalable data processing is microservices! Building on the ease of development and deployment provided by Spring Boot and the cloud native capabilities of Spring Cloud, the Spring Cloud Stream and Spring Cloud Task projects provide a simple and powerful framework for creating microservices for stream and batch processing. They make it easy to develop data-processing Spring Boot applications that build upon the capabilities of Spring Integration and Spring Batch, respectively. At a higher level of abstraction, Spring Cloud Data Flow is an integrated orchestration layer that provides a highly productive experience for deploying and managing sophisticated data pipelines consisting of standalone microservices. Streams and tasks are defined using a DSL abstraction and can be managed via shell and a web UI. Furthermore, a pluggable runtime SPI allows Spring Cloud Data Flow to coordinate these applications across a variety of distributed runtime platforms such as Apache YARN, Cloud Foundry, or Apache Mesos. This session will provide an overview of these projects, including how they evolved out of Spring XD. Both streaming and batch-oriented applications will be deployed in live demos on different platforms ranging from local cluster to a remote Cloud to show the simplicity of the developer experience.
Watch this Tech Talk: https://do.co/video_pgupta
An introduction into the world of containers and the orchestration ecosystem, and how Kubernetes can help software developers and cloud infrastructure engineers be more agile, efficient, and productive.
Containers and Kubernetes have changed the infra world for good, bringing agility, efficiency, and more productivity. Still thinking about how to get started with Kubernetes? This talk is designed to give you an introduction into the world of containers and the orchestration ecosystem.
What You'll Learn
- Introduction to containers and microservices
- Introduction to Kubernetes and how it can help
- Essential Kubernetes building blocks (“primitives”) for getting started
About the Presenter
Peeyush Gupta is a cloud enthusiast with 5+ years of experience in developing cloud platforms and helping customers migrate their legacy applications to cloud. He has also been a speaker at multiple meetups and serves the developer community as part of Kubernetes contributor experience group. He is currently working with DigitalOcean as a Senior Developer Advocate.
New to DigitalOcean? Get US $100 in credit when you sign up: https://do.co/deploytoday
To learn more about DigitalOcean: https://www.digitalocean.com/
Follow us on Twitter: https://twitter.com/digitalocean
Like us on Facebook: https://www.facebook.com/DigitalOcean
Follow us on Instagram: https://www.instagram.com/thedigitalocean/
We're hiring: http://do.co/careers
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
advanced computer architecture unit 1 notes. topics covered are Parallel Computer Models : The state of computing, Multiprocessors and Multi-computers, Multi vector and SIMD Computers, PRAM and VLSI Models, Architectural Development Tracks
Accessible hpc for everyone with docker and containersDocker, Inc.
This session will introduce High Performance Computing and outline the challenges when trying to fit those workloads into containers. Afterwards the community solutions are touched on before an approach based on proper Docker is shown. The talk will wrap-up with an outlook how containers can foster scientific discoveries by allowing HPC to be used by everyone.
some old CCPP aptitude paper aptitude might help you in future CCPP
for more info please visit
http://cdac.in
or http://acts.cdac.in
or http://cdacguru.wordpress.com
this is a java paper you can use it as a reference in interview for java
for more info please visit
http://acts.cdac.in
or
http://cdac.in
or
http://cdacguru.wordpress.com
Diploma in Advanced Software Development Methodologies (DASDM)prabhatjon
CDAC New Course Diploma in Advanced Software Development Methodologies (DASDM) launched
for more details visit http://facebook.com/cdacguru or http://cdacguru.wordpress.com
C-DAC
ACTS will start operating from its new premise
with effect from August 20, 2013
for more details please visit http://acts.cdac.in
or http://cdacguru.wordpress.com
or http://www.facebook.com/cdacguru
CDAC run large no or courses , having various centers across India, Bytes Softech is CDAC ATC in New Delhi that also run CDAC affiliated courses this PDF give a simple gimps
for more info please visit http://bytessoftech.com
or http://www.facebook.com/bytesedu
or call Tel No: 011-46579380, 40503451
1. CCEE GUESS PAPER FOR OS MODULE
(SOLVED)
A. Mutual-exclusion when n jobs
Q1- The removal of process from
active contention of CPU and
sharing a resource
reintroduce them into
B. Multiple clients and servers
accomplishing a computation
memory later is known as…
C. The need for synchronization in
systems where many processes
share a resource
A. Interrupt
B. Swapping
C. Signal
D. None of the above
D. Multiprogramming
Q2- The Purpose of Cooperating
processes is …
Q5- In multithreaded programs,
the kernel informs an application
about certain events
A. Information Sharing
using a procedure known as …… .
B. Convenience
A. Signal
C. Computation Speed-Up
B. Upcall
D. All of the above
C. Event handler
D. Pool
Q3The
Multiprogramming
by…
degree
of
is controlled
A. CPU scheduler
B. Context switching
C. Long-term scheduler
D. Medium-term scheduler
Q4The producer-consumer
problem refers to:
Q6- LWPis…
A. placed between system and
kernel threads.
B. placed between user and
kernel threads.
C.
common
in
systems
implementing
one-to-one
multithreading models.
D. none of the above.
2. CCEE GUESS PAPER FOR OS MODULE
(SOLVED)
Q9- Thread-specific datais data
Q7CPU burst distribution is
that…
generally characterized as
A. is not associated with any
process.
A. constant
B. linear
C. polynomial
D.
exponential
exponential
or
hyper-
Q8The primary difference
between user-level threads and
kernel threads is …
A. User level threads do not use OS
services via system calls, where
kernel threads require system calls
.
B. User level threads are
independent of each other,
whereas kernel threads can
write into each other’s memory
space .
C.
User level threads require
memory management where kernel
threads do not .
D. None of the above.
B. has been modified by the thread
but not yet updatedto the parent
process.
C. is generated by the thread
independent of the thread's process
.
D. is copied and not shared
with the parent process.
Q10- The purpose of dual modeof
operation is to…
A.
Protect the OS from user
programs.
B. Protect the user programs from
an OS.
C.
Protect one program from
another user program.
D.
Protect the computer
hardware.
