Data centers are large physical facilities that house servers, networking equipment, and other infrastructure to deliver computing resources and services. They provide utilities like power, cooling, security and shelter. Typical data centers range from 500-5000 square meters and consume 1-20 MW of power on average. Modern cloud-based data centers are designed with multiple regions and availability zones to provide redundancy and prevent failures across entire regions. They use software-defined infrastructure to dynamically allocate resources based on workload demands and improve utilization of servers. Managing the scale and complexity of data centers remains an ongoing challenge due to their growth and the massive amounts of data generated each day.
Data centers are large physical facilities that house computing infrastructure for enterprises. They provide utilities like power, cooling, security and shelter for servers and storage equipment. Modern data centers are designed with regions and availability zones for fault tolerance, with each zone consisting of one or more data centers within close network proximity. Key challenges for data centers include efficient cooling of equipment, improving energy proportionality as servers are often idle, optimizing resource utilization through virtualization and dynamic allocation, and managing the immense scale of infrastructure and traffic as cloud providers operate millions of servers globally.
Advanced Computer Architecture – An IntroductionDilum Bandara
Introduction to advanced computer architecture, including classes of computers,
Instruction set architecture, Trends, Technology, Power and energy
Cost
Principles of computer design
Cloud computing provides on-demand access to computing resources and infrastructure over the internet on a pay-as-you-go basis. It enables users and companies to avoid over-provisioning for peak demand and allows for applications to rapidly scale up or down based on usage. This new utility computing model is enabled by large data centers operated by companies like Amazon and Google that can provide resources much more efficiently than individual organizations. Cloud computing also has the potential to transform education by allowing students to build and deploy software applications that can continue to operate after a course ends.
Applying Cloud Techniques to Address Complexity in HPC System Integrationsinside-BigData.com
In this video from the HPC User Forum at Argonne, Arno Kolster from Providentia Worldwide presents: Applying Cloud Techniques to Address Complexity in HPC System Integrations.
"The Oak Ridge Leadership Computing Facility (OLCF) and technology consulting company Providentia Worldwide recently collaborated to develop an intelligence system that combines real-time updates from the IBM AC922 Summit supercomputer with local weather and operational data from its adjacent cooling plant, with the goal of optimizing Summit’s energy efficiency. The OLCF proposed the idea and provided facility data, and Providentia developed a scalable platform to integrate and analyze the data."
Watch the video: https://wp.me/p3RLHQ-kOg
Learn more: http://www.providentiaworldwide.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document discusses requirements and technology issues for data centers of the future. It outlines a vision for modular, proximity-based data centers with mixed compute environments, redundancy, workload migration capabilities, and automation/orchestration. Current issues include a lack of standardization in orchestration/integration, limitations on linear scaling, and "flatness" challenges from multi-tiered network designs. The data center of the future aims to address these through software-defined networking, computing, and storage orchestrated in a secure, flat design. Companies that implement these technologies gain competitive advantages around utilization and rapid expansion.
Data centers are large physical facilities that house servers, networking equipment, and other infrastructure to deliver computing resources and services. They provide utilities like power, cooling, security and shelter. Typical data centers range from 500-5000 square meters and consume 1-20 MW of power on average. Modern cloud-based data centers are designed with multiple regions and availability zones to provide redundancy and prevent failures across entire regions. They use software-defined infrastructure to dynamically allocate resources based on workload demands and improve utilization of servers. Managing the scale and complexity of data centers remains an ongoing challenge due to their growth and the massive amounts of data generated each day.
Data centers are large physical facilities that house computing infrastructure for enterprises. They provide utilities like power, cooling, security and shelter for servers and storage equipment. Modern data centers are designed with regions and availability zones for fault tolerance, with each zone consisting of one or more data centers within close network proximity. Key challenges for data centers include efficient cooling of equipment, improving energy proportionality as servers are often idle, optimizing resource utilization through virtualization and dynamic allocation, and managing the immense scale of infrastructure and traffic as cloud providers operate millions of servers globally.
Advanced Computer Architecture – An IntroductionDilum Bandara
Introduction to advanced computer architecture, including classes of computers,
Instruction set architecture, Trends, Technology, Power and energy
Cost
Principles of computer design
Cloud computing provides on-demand access to computing resources and infrastructure over the internet on a pay-as-you-go basis. It enables users and companies to avoid over-provisioning for peak demand and allows for applications to rapidly scale up or down based on usage. This new utility computing model is enabled by large data centers operated by companies like Amazon and Google that can provide resources much more efficiently than individual organizations. Cloud computing also has the potential to transform education by allowing students to build and deploy software applications that can continue to operate after a course ends.
Applying Cloud Techniques to Address Complexity in HPC System Integrationsinside-BigData.com
In this video from the HPC User Forum at Argonne, Arno Kolster from Providentia Worldwide presents: Applying Cloud Techniques to Address Complexity in HPC System Integrations.
"The Oak Ridge Leadership Computing Facility (OLCF) and technology consulting company Providentia Worldwide recently collaborated to develop an intelligence system that combines real-time updates from the IBM AC922 Summit supercomputer with local weather and operational data from its adjacent cooling plant, with the goal of optimizing Summit’s energy efficiency. The OLCF proposed the idea and provided facility data, and Providentia developed a scalable platform to integrate and analyze the data."
Watch the video: https://wp.me/p3RLHQ-kOg
Learn more: http://www.providentiaworldwide.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document discusses requirements and technology issues for data centers of the future. It outlines a vision for modular, proximity-based data centers with mixed compute environments, redundancy, workload migration capabilities, and automation/orchestration. Current issues include a lack of standardization in orchestration/integration, limitations on linear scaling, and "flatness" challenges from multi-tiered network designs. The data center of the future aims to address these through software-defined networking, computing, and storage orchestrated in a secure, flat design. Companies that implement these technologies gain competitive advantages around utilization and rapid expansion.
This document discusses scheduling in cloud computing environments and summarizes an experimental study comparing different task scheduling policies in virtual machines. It begins with introductions to cloud computing, architectures, and virtualization. It then presents the problem statement of improving application performance under varying resource demands through efficient scheduling. The document outlines simulations conducted using the CloudSim toolkit to evaluate scheduling algorithms like shortest job first, round robin, and a proposed algorithm incorporating machine processing speeds. It presents the implementation including a web interface and concludes that round robin scheduling distributes jobs equally but can cause fragmentation, while the proposed algorithm aims to overcome limitations of existing approaches.
Desktop to Cloud Transformation PlanningPhearin Sok
Traditional desktop delivery model is based on
a large number of distributed PCs executing operating system
and desktop applications. Managing traditional desktop
environments is incredibly challenging and costly. Tasks like
installations, conguration changes, security measures require
time-consuming procedures and dedicated deskside support. Also
these distributed desktops are typically underutilized, resulting
in low ROI for these assets. Further, this distributed computing
model for desktops also creates a security concern as sensitive
information could be compromised with stolen laptops or PCs.
Desktop virtualization, which moves computation to the data
center, allows users to access their applications and data using
stateless thin-clientdevices and therefore alleviates some of
the problems of traditional desktop computing. Enterprises can
now leverage the exibility and cost-benets of running users'
desktops on virtual machines hosted at the data center to enhance
business agility and reduce business risks, while lowering TCO.
Recent research and development of cloud computing paradigm
opens new possibilities of mass hosting of desktops and providing
them as a service.
However, transformation of legacy systems to desktop clouds
as well as proper capacity provisioning is a challenging problem.
Desktop cloud needs to be appropriately designed and provisioned
to offer low response time and good working experience
to desktop users while optimizing back-end resource usage and
therefore minimizing provider's costs. This paper presents tools
and approaches we have developed to facilitate fast and accurate
planning for desktop clouds. We present desktop workload
proling and benchmarking tools as well as desktop to cloud
transformation process enabling fast and accurate transition of
legacy systems to new cloud-based model.
Paolo Merialdo, Cloud Computing and Virtualization: una introduzioneInnovAction Lab
Cloud computing refers to applications and services delivered over the Internet and the hardware and software in datacenters that provide those services. It provides scalable computing resources on demand in a pay-as-you-go model. This allows for infinite computing resources, eliminates upfront commitment, and allows users to pay for only what they use. Cloud computing provides advantages for both end users and service providers by allowing applications to be deployed easily and resources to be utilized efficiently.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
E source energy managers conf 4 24-13-finaljosh whitney
This document discusses best practices for improving efficiency in small server rooms and closets. It begins with an introduction on metrics to measure efficiency like PUE, CUE, and RUE. Unique challenges for small spaces are split incentives and lack of scale. Best practices discussed include improving infrastructure efficiency through techniques like hot/cold aisle containment and raising temperature setpoints. Improving IT efficiency through server refresh, consolidation, virtualization and powering off unused servers is also covered. Case studies show significant potential savings through these approaches.
Optimising Service Deployment and Infrastructure Resource ConfigurationRECAP Project
This is a presentation delivered by Alec Leckey (Intel) at the 2nd Data Centre Symposium held in conjunction with the National Conference on Cloud Computing and Commerce (http://2018.nc4.ie/) on April 10, 2018 in Dublin, Ireland.
Learn more about the RECAP project: https://recap-project.eu/
Install the Intel Landscaper: https://github.com/IntelLabsEurope/landscaper
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...DataWorks Summit
Hadoop is becoming a standard platform for building critical financial applications such as risk reporting, trading and fraud detection. These applications require high level of SLAs (service-level agreement) in terms of RPO (Recovery Point Objective) and RTO (Recovery Time Objective). To achieve these SLAs, organizations need to build a disaster recovery plan that cover several layers ranging from the infrastructure to the clients going through the platform and the applications. In this talk, we will present the different architecture blueprints for disaster recovery as well as their corresponding SLA objectives. Then, we will focus on the stretch cluster solution that Crédit Agricole CIB is using in production. We will discuss the solution’s advantages, drawbacks and the impact of this approach on the global architecture. Finally, we will explain in detail how to configure and deploy this solution and how to integrate each layer (storage layer, processing layer...) into the architecture.
In today’s world the growing demand for knowledge has made cloud computing a center of attraction. Cloud computing is providing utility based services to all the users worldwide. It enables presentation of applications from consumers, scientific and business domains. However, data centers created for cloud computing applications consume huge amounts of energy, contributing to high operational costs and a large amount of carbon dioxide emission to the environment. With enhancement of data center, the power consumption is increasing at such a rate that it has become a key concern these days because it is ultimately leading to energy shortcomings and global climatic change. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs.
This document summarizes a student report on optimizing virtual machine placement across geo-distributed data centers to minimize costs. It proposes using an optimization model to determine the optimal spare capacity allocation across data centers while considering electricity costs, demand variability, and other factors. It also describes using a heuristic algorithm to place VMs on physical machines across data centers in a way that minimizes operating costs like electricity and communication costs.
Performance Improvement of Cloud Computing Data Centers Using Energy Efficien...IJAEMSJORNAL
Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
Dr. Tamar Eilam discusses sustainable computing and AI sustainability. Deep learning requires a lot of computation and energy to train large models. The demand for AI is growing exponentially, as are the sizes of language models. Foundation models are becoming more common, where a broad pre-trained model is adapted for specific tasks. However, continuously training larger models risks increasing energy consumption significantly. Sustainable AI research aims to dynamically track energy and carbon usage, while helping data scientists determine optimal model training strategies based on transparency around computational costs and model performance.
This document discusses how to make software more green and environmentally friendly. It defines green software as software that is carbon efficient, energy efficient, hardware efficient, and carbon aware. It provides recommendations for various roles within an organization on driving green initiatives, including focusing on efficiency for CxOs, architects, infrastructure engineers, and developers. Examples include optimizing resource usage, using public clouds effectively, prioritizing equipment standardization, and developing applications that can run more efficiently.
I presented "Cloudsim & Green Cloud" in First National Workshop of Cloud Computing at Amirkabir University on 31st October and 1st November, 2012.
Enjoy it!
Taking High Performance Computing to the Cloud: Windows HPC and Saptak Sen
High Performance Computing (HPC) is expected to be the single largest workload on Windows Azure. This session discusses how Windows HPC Server 2008 R2 SP2 enables our customers to easily run their HPC applications on Windows Azure. It covers different usage scenarios (“bursting” to Windows Azure vs. running everything in Windows Azure),differences between running HPC applications on-premise vs. in Azure,best practices,limitations,etc. Real-world customers and their scenarios are highlighted. The key points are illustrated with live demos of HPC applications running in Windows Azure. This session is a must for everyone who wants to know about HPC and Windows Azure.
Public, private, and hybrid clouds have different characteristics in terms of technology leverage and ownership, management of provisioned resources, workload distribution methods, security and data privacy enforcement, and example platforms. Infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) are the three main service models for cloud computing, with each offering a different level of service and control over the infrastructure. Ensuring security across these cloud service models presents challenges that providers aim to address through techniques like trust overlays, data watermarking, and access control measures.
The document summarizes research done at the Barcelona Supercomputing Center on evaluating Hadoop platforms as a service (PaaS) compared to infrastructure as a service (IaaS). Key findings include:
- Provider (Azure HDInsight, Rackspace CBD, etc.) did not significantly impact performance of wordcount and terasort benchmarks.
- Data size and number of datanodes were more important factors, with diminishing returns on performance from adding more nodes.
- PaaS can save on maintenance costs compared to IaaS but may be more expensive depending on workload and VM size needed. Tuning may still be required with PaaS.
Introduction to Machine Learning
Association Analysis
Supervised (inductive) learning
Training data includes desired outputs
Classification
Regression/Prediction
Unsupervised learning
Training data does not include desired outputs
Semi-supervised learning
Training data includes a few desired outputs
Reinforcement learning
Rewards from sequence of actions
Time Series Analysis and Forecasting in PracticeDilum Bandara
This document discusses time series analysis and forecasting. It covers the components of time series including trends, seasonality, cyclical patterns and irregular components. It then describes several approaches to forecasting including qualitative judgmental methods, statistical time series models and explanatory causal models. Specific statistical time series forecasting techniques are explained such as simple and exponential smoothing, linear regression models, and Holt-Winters seasonal models. The importance of evaluating forecast accuracy is also highlighted.
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This document discusses scheduling in cloud computing environments and summarizes an experimental study comparing different task scheduling policies in virtual machines. It begins with introductions to cloud computing, architectures, and virtualization. It then presents the problem statement of improving application performance under varying resource demands through efficient scheduling. The document outlines simulations conducted using the CloudSim toolkit to evaluate scheduling algorithms like shortest job first, round robin, and a proposed algorithm incorporating machine processing speeds. It presents the implementation including a web interface and concludes that round robin scheduling distributes jobs equally but can cause fragmentation, while the proposed algorithm aims to overcome limitations of existing approaches.
Desktop to Cloud Transformation PlanningPhearin Sok
Traditional desktop delivery model is based on
a large number of distributed PCs executing operating system
and desktop applications. Managing traditional desktop
environments is incredibly challenging and costly. Tasks like
installations, conguration changes, security measures require
time-consuming procedures and dedicated deskside support. Also
these distributed desktops are typically underutilized, resulting
in low ROI for these assets. Further, this distributed computing
model for desktops also creates a security concern as sensitive
information could be compromised with stolen laptops or PCs.
Desktop virtualization, which moves computation to the data
center, allows users to access their applications and data using
stateless thin-clientdevices and therefore alleviates some of
the problems of traditional desktop computing. Enterprises can
now leverage the exibility and cost-benets of running users'
desktops on virtual machines hosted at the data center to enhance
business agility and reduce business risks, while lowering TCO.
Recent research and development of cloud computing paradigm
opens new possibilities of mass hosting of desktops and providing
them as a service.
However, transformation of legacy systems to desktop clouds
as well as proper capacity provisioning is a challenging problem.
Desktop cloud needs to be appropriately designed and provisioned
to offer low response time and good working experience
to desktop users while optimizing back-end resource usage and
therefore minimizing provider's costs. This paper presents tools
and approaches we have developed to facilitate fast and accurate
planning for desktop clouds. We present desktop workload
proling and benchmarking tools as well as desktop to cloud
transformation process enabling fast and accurate transition of
legacy systems to new cloud-based model.
Paolo Merialdo, Cloud Computing and Virtualization: una introduzioneInnovAction Lab
Cloud computing refers to applications and services delivered over the Internet and the hardware and software in datacenters that provide those services. It provides scalable computing resources on demand in a pay-as-you-go model. This allows for infinite computing resources, eliminates upfront commitment, and allows users to pay for only what they use. Cloud computing provides advantages for both end users and service providers by allowing applications to be deployed easily and resources to be utilized efficiently.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
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This document discusses best practices for improving efficiency in small server rooms and closets. It begins with an introduction on metrics to measure efficiency like PUE, CUE, and RUE. Unique challenges for small spaces are split incentives and lack of scale. Best practices discussed include improving infrastructure efficiency through techniques like hot/cold aisle containment and raising temperature setpoints. Improving IT efficiency through server refresh, consolidation, virtualization and powering off unused servers is also covered. Case studies show significant potential savings through these approaches.
Optimising Service Deployment and Infrastructure Resource ConfigurationRECAP Project
This is a presentation delivered by Alec Leckey (Intel) at the 2nd Data Centre Symposium held in conjunction with the National Conference on Cloud Computing and Commerce (http://2018.nc4.ie/) on April 10, 2018 in Dublin, Ireland.
Learn more about the RECAP project: https://recap-project.eu/
Install the Intel Landscaper: https://github.com/IntelLabsEurope/landscaper
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...DataWorks Summit
Hadoop is becoming a standard platform for building critical financial applications such as risk reporting, trading and fraud detection. These applications require high level of SLAs (service-level agreement) in terms of RPO (Recovery Point Objective) and RTO (Recovery Time Objective). To achieve these SLAs, organizations need to build a disaster recovery plan that cover several layers ranging from the infrastructure to the clients going through the platform and the applications. In this talk, we will present the different architecture blueprints for disaster recovery as well as their corresponding SLA objectives. Then, we will focus on the stretch cluster solution that Crédit Agricole CIB is using in production. We will discuss the solution’s advantages, drawbacks and the impact of this approach on the global architecture. Finally, we will explain in detail how to configure and deploy this solution and how to integrate each layer (storage layer, processing layer...) into the architecture.
In today’s world the growing demand for knowledge has made cloud computing a center of attraction. Cloud computing is providing utility based services to all the users worldwide. It enables presentation of applications from consumers, scientific and business domains. However, data centers created for cloud computing applications consume huge amounts of energy, contributing to high operational costs and a large amount of carbon dioxide emission to the environment. With enhancement of data center, the power consumption is increasing at such a rate that it has become a key concern these days because it is ultimately leading to energy shortcomings and global climatic change. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs.
This document summarizes a student report on optimizing virtual machine placement across geo-distributed data centers to minimize costs. It proposes using an optimization model to determine the optimal spare capacity allocation across data centers while considering electricity costs, demand variability, and other factors. It also describes using a heuristic algorithm to place VMs on physical machines across data centers in a way that minimizes operating costs like electricity and communication costs.
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Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better usage of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved.
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Enjoy it!
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Full-RAG: A modern architecture for hyper-personalization
Introduction to Warehouse-Scale Computers
1. Warehouse-Scale
Computers
CS4342 Advanced Computer Architecture
Dilum Bandara
Dilum.Bandara@uom.lk
Slides adapted from “Computer Architecture, A Quantitative Approach” by John L.
Hennessy and David A. Patterson, 5th Edition, 2012, MK Publishers and
The Datacenter as a Computer:An Introduction to the Design of Warehouse-Scale
Machines by Luiz André Barroso & Urs Hölzle
7. Warehouse-Scale Computer (WSC)
Provides Internet services
Search, social networking, online maps, video sharing,
online shopping, email, cloud computing, etc.
Differences with HPC clusters
Clusters use higher performance processors & network
Clusters emphasize thread-level parallelism, WSCs
emphasize request/task-level parallelism
Differences with datacenters
Datacenters consolidate different machines & software
into a single location
Datacenters emphasize virtual machines & hardware
heterogeneity to serve varied customers 7
8. Design Factors for WSC
Cost-performance
Small savings add up
Energy efficiency
Affects power distribution & cooling
Work per joule
Operational costs count
Power consumption is a primary constraint when
designing a system
Dependability via redundancy
Many low-cost components
8
9. Design Factors (Cont.)
Network I/O
Interactive & batch processing workloads
Web search – interactive
Web indexing – batch
Ample computational parallelism isn’t important
Most jobs are totally independent, “Request-level
parallelism”
Scale – Its opportunities & problems
Can afford to build customized systems as WSC
require volume purchase
Frequent failures
9
10. Failure Example
Consider a WSC with 50,000 nodes. MTTF of a node is 5
years. How many failures be there for a day?
MTTF in days = 5 x 365 = 1,825
Failure rate = 1/1,825 per day
No of failures per day = 50,000/1,825 = 27.4
Consider a WSC with 50,000 nodes & each node with 4
hard disks. Suppose a annual failure rate of a disk is 4%.
What is the time for a disk failure?
No of disks = 50,000 x 4 = 200,000
No of failures per year = 200,000 x 0.04 = 8,000
Time for failure = 365 x 24 / 8,000 = 1.095 hours/failure 10
11. Programming Models & Workloads
Batch processing framework
– MapReduce
Map
Applies a programmer-
supplied function to each
logical input record
Runs on thousands of
computers
Provides new set of (key,
value) pairs as intermediate
values
Reduce
Collapses values using
another function 11
http://www.cbsolution.net/techniques/ontarget/mapredu
ce_vs_data_warehouse
14. Programming Models & Workloads
(Cont.)
MapReduce runtime environment schedules
map & reduce task to WSC nodes
Availability
Use replicas of data across different servers
Use relaxed consistency
No need for all replicas to always agree
Workload demands
Often vary considerably
14
15. Computer Architecture of WSC
Often uses a hierarchy of networks for
interconnection
Each 19” rack holds 48 1U servers connected to
a rack switch
Rack switches are uplinked to a switch(es)
higher in hierarchy
Uplink has 48/n times lower bandwidth –
Oversubscription
n – No of uplink ports
Goal is to maximize locality of communication relative
to the rack
15
19. Infrastructure & Costs
Location
Proximity to Internet backbones, electricity cost, property tax rates,
low risk from earthquakes, floods, & hurricanes
Power distribution
19
20. Power Usage
20
U.S. EPA Report 2007 – 1.5% of total U.S.
power consumption used by data centers
which has more than doubled since 2000 &
costs $4.5 billion
21. How Many Nodes can a WSC Support?
Each node
“Nameplate power rating” gives maximum power
consumption
To get actual, measure power under actual workloads
Oversubscribe cumulative nodes power by 40%,
but monitor power closely
21
23. Cooling (Cont.)
23
Cooling system also uses water (evaporation & spills)
e.g. 70,000 to 200,000 gallons per day for an 8 MW facility
24. Efficiency
Power Utilization Effectiveness (PUE)
= Total facility power / IT equipment power
≥ 1
Median PUE on 2006 study was 1.69
24
Source: http://hightech.lbl.gov/benchmarking-guides/data-a1.html
25. Performance
Latency is important metric because it is seen by
users
Bing study
Users will use search less as response time
increases
Service Level Objectives (SLOs) & Service Level
Agreements (SLAs)
Typically given at application level
e.g., 99% of requests be below 100 ms
In clouds typically given only for static resources
CPU speed, no of cores, & memory
25
26. Cost
Capital expenditures (CAPEX)
Cost to build a WSC
Hardware cost dominates
Operational expenditures (OPEX)
Cost to operate a WSC
Power for nodes & cooling dominates
26
28. Cloud Computing (Cont.)
WSCs offer economies of scale that can’t be
achieved with a datacenter
5.7 times reduction in storage costs
7.1 times reduction in administrative costs
7.3 times reduction in networking costs
This has given rise to cloud services such as Amazon
Web Services
“Utility Computing”
Based on using open source virtual machine & operating
system software
28
29. Amazon Web Services
Virtual machines
XEN
Very low cost
$ 0.10 per hour per instance
Primary rely on open source software
No (initial) service guarantees
No contract required
Amazon S3
Simple Storage Service
Amazon EC2
Elastic Computer Cloud 29
30. Amazon Web Services – Example
30
http://www.ryhug.com/free-art-available-on-amazon-amazon-web-services-that-is/
Notas del editor
1U - A rack unit (abbreviated U or RU) is a unit of measure defined as 44.50 mm (1.75 in)
computer room air conditioning (CRAC)
DCiE = 1/PUE
S3 - Simple Storage Service
EC2 - Elastic Compute Cloud