The document discusses key concepts in cloud computing including cloud roles, characteristics, delivery models, and deployment models. It defines roles like cloud provider, consumer, and service owner. It describes characteristics such as on-demand access, elasticity, and multi-tenancy. Delivery models covered are IaaS, PaaS, and SaaS. Deployment models discussed are public, private, hybrid, and community clouds.
SURVEY ON KEY AGGREGATE CRYPTOSYSTEM FOR SCALABLE DATA SHARINGEditor IJMTER
Public-key cryptosystems produce constant-size cipher texts with efficient delegation
of decryption rights for any set of cipher texts. One can aggregate any set of secret keys and make
them as compact as a single key. The secret key holder can release a constant-size aggregate key for
flexible choices of cipher text set in cloud storage. In KAC, users encrypt a message not only under a
public-key, but also under an identifier of cipher text called class. That means the cipher texts are
further categorized into different classes. The key owner holds a master-secret called master-secret
key, which can be used to extract secret keys for different classes. More importantly, the extracted
key have can be an aggregate key which is as compact as a secret key for a single class, but
aggregates the power of many such keys, i.e., the decryption power for any subset of cipher text
classes. The key aggregate cryptosystem is enhanced with boundary less cipher text classes. The
system is improved with device independent key distribution mechanism. The key distribution
process is enhanced with security features to protect key leakage. The key parameter transmission
process is integrated with the cipher text download process.
“The chapter is organized into two primary sections that explore cloud delivery model issues pertaining to cloud providers and cloud consumers respectively.”
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
SURVEY ON KEY AGGREGATE CRYPTOSYSTEM FOR SCALABLE DATA SHARINGEditor IJMTER
Public-key cryptosystems produce constant-size cipher texts with efficient delegation
of decryption rights for any set of cipher texts. One can aggregate any set of secret keys and make
them as compact as a single key. The secret key holder can release a constant-size aggregate key for
flexible choices of cipher text set in cloud storage. In KAC, users encrypt a message not only under a
public-key, but also under an identifier of cipher text called class. That means the cipher texts are
further categorized into different classes. The key owner holds a master-secret called master-secret
key, which can be used to extract secret keys for different classes. More importantly, the extracted
key have can be an aggregate key which is as compact as a secret key for a single class, but
aggregates the power of many such keys, i.e., the decryption power for any subset of cipher text
classes. The key aggregate cryptosystem is enhanced with boundary less cipher text classes. The
system is improved with device independent key distribution mechanism. The key distribution
process is enhanced with security features to protect key leakage. The key parameter transmission
process is integrated with the cipher text download process.
“The chapter is organized into two primary sections that explore cloud delivery model issues pertaining to cloud providers and cloud consumers respectively.”
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Introduction of Cloud Computing & Historical Background
Cloud Service Models & Cloud Deployment Models
Benefits of Cloud Computing
Risks and Challenges
Future Trends in Cloud Computing
Edge Computing, Serverless Computing, AI & Machine Learning in Cloud, Security and
Compliance
Needs and Obstacles for Cloud Deployment
Conclusion
Cloud computing is on-demand access, via the internet, to computing resources—applications, servers (physical servers and virtual servers), data storage, development tools, networking capabilities, and more—hosted at a remote data center managed by a cloud services provider (or CSP). The CSP makes these resources available for a monthly subscription fee or bills them according to usage.
Cloud computing is very useful then also its own set of cons discourage cloud users to choose them as a best option. The multitenant architecture of cloud exposed to several threats such as improper trust management at service provider site, Storage security, Shared technology vulnerabilities, data lost/leakage during transit, unauthorized access of data. This paper studied review work on cloud steganography.
This Presentation is on a very popular technology related topic, Cloud Computing. It is in our basic daily technology need like gmail i.e. also based on Cloud Computing. And also it has also very good source of job in it. Hope it would be helpful for your School or College project.
“This chapter provide an overview of introductory cloud computing topics. It begins with a brief history of cloud computing along with short descriptions of its business and technology drivers. This is followed by definitions of basic concepts and terminology, in addition to explanations of the primary benefits and challenges of cloud computing adoption.”
An Integrated Cloud Computing Architectural Stack Zara Tariq
Cloud computing is the classification of those computing which are built on personal devices to handle applications.
The cloud computing architectural stack aims at facilitating communication about different cloud technologies and services.
CA NOTES ON EMERGING TECHNOLOGIES
FREE AFFIDAVITS AND NOTICES FORMATS
FREE AGREEMENTS AND CONTRACTS FORMATS
FREE LLB LAW NOTES
FREE CA ICWA NOTES
FREE LLB LAW FIRST SEM NOTES
FREE LLB LAW SECOND SEM NOTES
FREE LLB LAW THIRD SEM NOTES
FREE LLB LAW FOURTH SEM NOTES
FREE LLB LAW FIFTH SEM NOTES
FREE LLB LAW SIXTH SEM NOTES
FREE CA ICWA FOUNDATION NOTES
FREE CA ICWA INTERMEDIATE NOTES
FREE CA ICWA FINAL NOTES
KANOON KE RAKHWALE INDIA
HIRE LAWYER ONLINE
LAW FIRMS IN DELHI
CA FIRM DELHI
VISIT : https://www.kanoonkerakhwale.com/
VISIT : https://hirelawyeronline.com/
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Introduction of Cloud Computing & Historical Background
Cloud Service Models & Cloud Deployment Models
Benefits of Cloud Computing
Risks and Challenges
Future Trends in Cloud Computing
Edge Computing, Serverless Computing, AI & Machine Learning in Cloud, Security and
Compliance
Needs and Obstacles for Cloud Deployment
Conclusion
Cloud computing is on-demand access, via the internet, to computing resources—applications, servers (physical servers and virtual servers), data storage, development tools, networking capabilities, and more—hosted at a remote data center managed by a cloud services provider (or CSP). The CSP makes these resources available for a monthly subscription fee or bills them according to usage.
Cloud computing is very useful then also its own set of cons discourage cloud users to choose them as a best option. The multitenant architecture of cloud exposed to several threats such as improper trust management at service provider site, Storage security, Shared technology vulnerabilities, data lost/leakage during transit, unauthorized access of data. This paper studied review work on cloud steganography.
This Presentation is on a very popular technology related topic, Cloud Computing. It is in our basic daily technology need like gmail i.e. also based on Cloud Computing. And also it has also very good source of job in it. Hope it would be helpful for your School or College project.
“This chapter provide an overview of introductory cloud computing topics. It begins with a brief history of cloud computing along with short descriptions of its business and technology drivers. This is followed by definitions of basic concepts and terminology, in addition to explanations of the primary benefits and challenges of cloud computing adoption.”
An Integrated Cloud Computing Architectural Stack Zara Tariq
Cloud computing is the classification of those computing which are built on personal devices to handle applications.
The cloud computing architectural stack aims at facilitating communication about different cloud technologies and services.
CA NOTES ON EMERGING TECHNOLOGIES
FREE AFFIDAVITS AND NOTICES FORMATS
FREE AGREEMENTS AND CONTRACTS FORMATS
FREE LLB LAW NOTES
FREE CA ICWA NOTES
FREE LLB LAW FIRST SEM NOTES
FREE LLB LAW SECOND SEM NOTES
FREE LLB LAW THIRD SEM NOTES
FREE LLB LAW FOURTH SEM NOTES
FREE LLB LAW FIFTH SEM NOTES
FREE LLB LAW SIXTH SEM NOTES
FREE CA ICWA FOUNDATION NOTES
FREE CA ICWA INTERMEDIATE NOTES
FREE CA ICWA FINAL NOTES
KANOON KE RAKHWALE INDIA
HIRE LAWYER ONLINE
LAW FIRMS IN DELHI
CA FIRM DELHI
VISIT : https://www.kanoonkerakhwale.com/
VISIT : https://hirelawyeronline.com/
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
8. Figure 4.4 A cloud resource administrator can be with a cloud consumer organization and administer
remotely accessible IT resources that belong to the cloud consumer.
Cloud Computing Roles: Cloud Resource Administrator -1/2
10. Cloud Computing Roles: Cloud Auditor
• A third-party (often accredited) that conducts independent
unbiased assessment of cloud environments to help strengthen
the trust relationship between cloud consumers and cloud
providers.
• Cloud Auditor is responsible for the evaluation of
– security controls
– privacy impacts
– performance
11. Cloud Computing Roles: Cloud Broker & Cloud Carrier
• Cloud Broker: Responsible of managing and negotiating the usage
of cloud services between cloud consumers and cloud providers.
• Cloud Carrier: Responsible for providing the wire-level connectivity
between cloud consumers and cloud providers
13. Cloud Characteristics
13
• On-demand usage
– ability of a cloud consumer to self-provision and use necessary cloud-based services without requiring
cloud provider interaction
• Ubiquitous access
– support for a range of devices, transport protocols, interfaces, and security technologies
• Multi-tenancy (and resource pooling)
– ability of an instance of the program to serve different consumers
• Elasticity
– ability of a cloud to transparently scale IT resources
• Measured usage
– ability to keep track of the usage of its IT resources
• Resiliency
– Failover through redundant implementations of IT resources across physical locations
23. Cloud Delivery Models: Infrastructure as a Service (IaaS): Building -1/4
• The two most fundamental IT resources that are delivered:
– virtual server
– cloud storage device
• Standardized configurations properties:
– Operating system
– Primary memory capacity
– Processing capacity
– Virtualized storage capacity
24. Cloud Delivery Models: Infrastructure as a Service (IaaS): Building -2/4
• IaaS offerings are preemptively assembled by cloud providers via virtual
server images that capture the pre-defined configurations
• May offer cloud consumers direct administrative access to physical IT
resources
• Snapshots can be taken of a virtual server to record its current state,
memory, and configuration
• Horizontal and vertical scaling
– duplicate a virtual server
– backup and replication purposes
– import and export options for custom-built virtual server
25. Cloud Delivery Models: Infrastructure as a Service (IaaS): Building -3/4
• Multiple geographically-diverse data centers
– Can be linked together for increased resiliency
– Connected through high-speed communications networks with low latency
– Can perform load balancing
– IT resource backup and replication
– Increase storage capacity
– Improving availability and reliability
28. Cloud Delivery Models: Platform as a Service (PaaS): Equipping
• Equipped with a selection of application development and deployment platforms in order to
accommodate different programming models, languages, and frameworks
• A separate ready-made environment is usually created for each programming stack that
contains the necessary software to run applications specifically developed for the platform
• Consumers can create and control customized virtual server images with ready-made
environments
• Also provides features, such as managing deployed applications and configuring
multitenancy
• The PaaS environment, by default, usually relies on the cloud security mechanisms
provisioned for IaaS environments
30. Cloud Delivery Models: Software as a Service (SaaS): Optimizing -1/6
Diversity of Functionality
• Collaborative authoring and information-sharing (Wikipedia, Blogger)
• Collaborative management (Zimbra, Google Apps)
• Conferencing services for instant messaging, audio/video
communications (Skype,Google Talk)
• Enterprise management systems (ERP, CRM)
• Video/File-sharing and content distribution (YouTube, Dropbox)
• Industry-specific software (engineering, bioinformatics)
• Messaging systems (e-mail, voicemail)
• Mobile application marketplaces (Android Play Store, Apple App Store)
• Office productivity software suites (Microsoft Office, Adobe Creative Cloud)
• Search engines (Google, Yahoo)
• Social networking media (Instagram, Twitter, LinkedIn)
31. Cloud Delivery Models: Software as a Service (SaaS): Optimizing -2/6
Diversity of Implementation Mediums
• Mobile application
• REST service (Representational State Transfer)
• Web service (e.g. SOAP)
• Each of these SaaS implementation mediums provide Web-based APIs
for interfacing by cloud consumers.
• Examples of online SaaS-based cloud services with Web-based APIs include:
– Electronic payment services (PayPal)
– Mapping and routing services (Google Maps)
– Publishing tools (WordPress)
32. Cloud Delivery Models: Software as a Service (SaaS): Optimizing -3/6
Specialized Processing Requirements
• Service Load Balancing – for workload distribution across redundant SaaS-based cloud service
implementations
• Dynamic Failure Detection and Recovery – to establish a system that can automatically resolve
some failure conditions without disruption in service to the SaaS implementation
• Storage Maintenance Window – to allow for planned maintenance outages that do not impact
SaaS implementation availability
• Elastic Resource Capacity/Elastic Network Capacity – to establish inherent elasticity within the
SaaS-based cloud service architecture that enables it to automatically accommodate a range of
runtime scalability requirements
• Cloud Balancing – to instill broad resiliency within the SaaS implementation, which can be
especially important for cloud services subjected to extreme concurrent usage volumes
33. Cloud Delivery Models: Software as a Service (SaaS): Optimizing -4/6
• Tenant Subscription Period – This metric is used by pay-per-use monitors to record and track
application usage for time-based billing. This type of monitoring usually incorporates
application licensing and regular assessments of leasing periods that extend beyond the
hourly periods of IaaS and PaaS environments
• Application Usage – This metric, based on user or security groups, is used with pay-per-use
monitors to record and track application usage for billing purposes
• Tenant Application Functional Module – This metric is used by pay-per-use monitors for
function-based billing. Cloud services can have different functionality tiers according to
whether the cloud consumer is free-tier or a paid subscriber
• Similar in IaaS and PaaS implementations, SaaS environments are also commonly monitored
for data storage, network traffic, failure conditions, and event triggers.
40. Cloud Deployment Models
• A cloud deployment model represents a specific type of cloud
environment, primarily distinguished by
• Ownership
• Size
• Access
• Four common cloud deployment models:
• Public cloud
• Community cloud
• Private cloud
• Hybrid cloud
41. Cloud Deployment Models: Public Cloud
• A public cloud is a publicly accessible cloud environment owned by a
third-party cloud provider
• The IT resources on public clouds are usually provisioned via the
previously described cloud delivery models and are generally offered to
cloud consumers at a cost or are commercialized via other avenues
(such as advertisement)
• The cloud provider is responsible for the creation and on-going
maintenance of the public cloud and its IT resources
43. Cloud Deployment Models: Community Cloud
• A community cloud is similar to a public cloud except that its
access is limited to a specific community of cloud consumers
• The community cloud may be jointly owned by the community
members or by a third-party cloud provider that provisions a public
cloud with limited access
• The member cloud consumers of the community typically share
the responsibility for defining and evolving the community cloud.
45. Cloud Deployment Models: Private Cloud
• A private cloud is owned by a single organization
• Private clouds enable an organization to use cloud computing
technology as a means of centralizing access to IT resources by
different parts, locations, or departments of the organization
47. Cloud Deployment Models: Hybrid Cloud
• A hybrid cloud is a cloud environment comprised of two or more
different cloud deployment models
• For example, a cloud consumer may choose to deploy cloud
services processing sensitive data to a private cloud and other,
less sensitive cloud services to a public cloud. The result of this
combination is a hybrid deployment model
49. Cloud Deployment Models: Other
• Virtual Private Cloud: Also known as a "dedicated cloud" or "hosted
cloud," this model results in a self-contained cloud environment
hosted and managed by a public cloud provider and made available
to a cloud consumer.
• Inter-Cloud: This model is based on an architecture comprised of two or
more inter-connected clouds .