Top 10 Cognitive Computing Tools

What are Cognitive Computing Tools?

Cognitive computing describes technology platforms that combine machine learning, reasoning, natural language processing, speech, vision, and human-computer interaction, that mimic the functioning of the human brain and helps to improve human decision-making. Cognitive computing applications link data analysis and adaptive page displays to adjust content for a particular type of audience. Some features that cognitive systems may express are adaptive, interactive, iterative and stateful, and contextual.

Here are the top 10 cognitive computing tools:

  1. IBM Watson
  2. Google Cloud AI
  3. Microsoft Azure Cognitive Services
  4. Amazon AI
  5. OpenAI
  6. CognitiveScale
  7. Nuance Communications
  8. Salesforce Einstein
  9. CognitiveScale
  10. H2O.ai

1. IBM Watson:

IBM Watson is a leading cognitive computing platform that provides a range of AI-powered tools and services for natural language processing, machine learning, data analysis, and more. It offers advanced capabilities for understanding, reasoning, and learning from unstructured data.

Key features:

  • Natural Language Processing (NLP): Watson’s NLP capabilities enable it to understand and analyze human language, including text and speech. It can perform tasks such as sentiment analysis, language translation, and entity recognition.
  • Machine Learning: Watson includes machine learning algorithms and tools that allow users to build and train models for various tasks, such as classification, regression, clustering, and anomaly detection. It supports both supervised and unsupervised learning techniques.
  • Image and Video Analysis: Watson has advanced capabilities for analyzing and understanding images and videos. It can perform tasks like object recognition, facial recognition, image classification, and scene understanding. This makes it useful in applications such as visual search, surveillance, and content moderation.

2. Google Cloud AI:

Google Cloud AI offers a suite of cognitive computing tools, including natural language processing, speech recognition, image recognition, and machine learning APIs. It enables developers to incorporate AI capabilities into their applications and services.

Key features:

  • Machine Learning: Google Cloud AI provides a comprehensive set of tools and services for machine learning. It includes AutoML, which enables users to build custom machine-learning models without extensive coding knowledge. It also offers TensorFlow, an open-source library for building and training machine learning models.
  • Natural Language Processing (NLP): Google Cloud AI includes powerful NLP capabilities. It provides pre-trained models for tasks like sentiment analysis, entity recognition, and language translation. Additionally, it offers tools such as Cloud Natural Language API and Dialogflow for building conversational interfaces and chatbots.
  • Vision APIs: Google Cloud AI offers computer vision capabilities through its Vision APIs. These APIs allow developers to analyze images and videos to extract information, detect objects, perform facial recognition, and more. The Vision API can also be used for OCR (optical character recognition) to extract text from images.

3. Microsoft Azure Cognitive Services:

Azure Cognitive Services provides a comprehensive set of APIs and SDKs for building intelligent applications. It offers cognitive computing capabilities such as speech recognition, image analysis, text analytics, and language understanding.

Key features:

  • Vision: Azure Cognitive Services offers vision APIs that enable developers to analyze images and videos. These APIs can perform tasks like object detection, image recognition, facial recognition, emotion detection, and content moderation.
  • Speech: Azure Cognitive Services provides speech APIs that allow developers to integrate speech recognition and synthesis capabilities into their applications. These APIs can convert speech to text, perform speaker recognition, and generate speech from text.
  • Language: Azure Cognitive Services includes language APIs for natural language processing tasks. These APIs can perform language detection, sentiment analysis, text translation, entity recognition, and key phrase extraction.

4. Amazon AI:

Amazon AI is a collection of AI services provided by Amazon Web Services (AWS). It includes tools for natural language understanding, image and video analysis, machine learning, and deep learning. These services can be used to build intelligent applications and enhance existing ones.

Key features:

  • Amazon Rekognition: Amazon Rekognition is a deep learning-based image and video analysis service. It can perform tasks like object detection, facial analysis, sentiment analysis, text detection, and content moderation. It enables developers to analyze and extract insights from visual content.
  • Amazon Polly: Amazon Polly is a text-to-speech service that uses advanced deep learning techniques to convert text into natural-sounding speech. It supports multiple languages and offers a wide range of voice options. Developers can use Polly to add speech synthesis capabilities to their applications.
  • Amazon Transcribe: Amazon Transcribe provides automatic speech recognition (ASR) capabilities. It can convert spoken language into written text, making it useful for tasks like transcription, voice commands, and real-time streaming of speech data. It supports a variety of audio formats and is designed to handle noisy environments.

5. OpenAI:

OpenAI is an organization that develops and promotes AI technologies, including cognitive computing tools. It offers language models like GPT-3 that can generate human-like text, as well as other AI technologies for various applications.

Key features:

  • GPT (Generative Pre-trained Transformer): OpenAI has developed several iterations of the GPT model, including GPT-3, which is one of the largest language models available. GPT models are capable of generating human-like text, making them valuable for tasks like language translation, content generation, and conversational agents.
  • Language Models: OpenAI focuses on building state-of-the-art language models that can understand, generate, and analyze human language. These models have been trained on vast amounts of text data and can perform tasks such as text classification, sentiment analysis, summarization, and question-answering.
  • Reinforcement Learning: OpenAI explores the field of reinforcement learning, which involves training agents to make decisions and learn from feedback in a dynamic environment. They have developed algorithms and frameworks for training AI agents using reinforcement learning techniques, enabling applications in robotics, game-playing, and autonomous systems.

6. CognitiveScale:

CognitiveScale provides a cognitive computing platform that enables businesses to develop AI-powered applications. It offers tools for natural language processing, machine learning, and data analysis, with a focus on industries such as healthcare, financial services, and retail.

Key features:

  • Augmented Intelligence: CognitiveScale’s platform enhances human decision-making by providing AI-powered insights and recommendations. It combines data from various sources, including structured and unstructured data, to generate actionable insights that can assist in decision-making processes.
  • Natural Language Processing (NLP): CognitiveScale utilizes NLP technology to understand and process human language. This enables the platform to extract meaning and context from text-based data, such as customer interactions, social media posts, and support tickets.
  • Knowledge Graphs: CognitiveScale employs knowledge graphs to organize and connect structured and unstructured data. Knowledge graphs enable the platform to represent complex relationships between different entities and provide a contextual understanding of the data.

7. Nuance Communications:

Nuance Communications specializes in speech and natural language processing technologies. Their cognitive computing tools include speech recognition, voice biometrics, virtual assistants, and healthcare-specific solutions like clinical documentation and voice-enabled clinical workflows.

Key features:

  • Speech Recognition: Nuance is known for its industry-leading speech recognition technology. Their solutions can accurately convert spoken language into written text, enabling applications such as transcription services, voice commands, and voice dictation.
  • Natural Language Understanding: Nuance leverages natural language understanding (NLU) capabilities to enable machines to comprehend and interpret human language. This allows for more sophisticated and context-aware interactions between users and AI systems.
  • Conversational AI: Nuance specializes in developing conversational AI solutions, including virtual assistants and chatbots. These AI-powered agents can engage in human-like conversations, providing assistance, answering queries, and completing tasks across various channels and devices.

8. Salesforce Einstein:

Salesforce Einstein is an AI-powered platform that brings cognitive capabilities to the Salesforce CRM ecosystem. It includes tools for predictive analytics, natural language processing, and machine learning, enabling businesses to enhance customer engagement and automate processes.

Key features:

  • Predictive Lead Scoring: Salesforce Einstein can analyze historical data and customer interactions to predict the likelihood of leads converting into customers. It assigns scores to leads based on various factors, such as demographics, behavior, and engagement, helping sales teams prioritize their efforts and focus on high-value leads.
  • Opportunity Insights: Einstein provides insights and recommendations for sales opportunities. It analyzes historical and real-time data to identify potential risks and opportunities in the sales pipeline. This helps sales teams make informed decisions, take appropriate actions, and increase their chances of closing deals.
  • Automated Email Responses: Einstein can automatically analyze and respond to customer emails using natural language processing. It understands the intent of customer inquiries and provides relevant responses, reducing the need for manual intervention and improving response times.

9. CognitiveScale:

CognitiveScale provides a cognitive computing platform that enables businesses to develop AI-powered applications. It offers tools for natural language processing, machine learning, and data analysis, with a focus on industries such as healthcare, financial services, and retail.

Key features:

  • Augmented Intelligence: CognitiveScale leverages artificial intelligence (AI) technologies to enhance human decision-making capabilities. Their platform combines machine learning, natural language processing, and advanced analytics to provide users with intelligent insights and recommendations.
  • Cognitive Process Automation: The platform enables organizations to automate complex business processes using AI and machine learning techniques. It can analyze and understand unstructured data, such as documents and images, and automate tasks that previously required human intervention.
  • Data Integration and Analytics: CognitiveScale offers robust data integration capabilities, allowing organizations to connect and aggregate data from various sources, including structured and unstructured data. Their analytics tools enable users to gain actionable insights from the data and make informed business decisions.

10. H2O.ai:

H2O.ai offers a platform for machine learning and AI, including cognitive computing capabilities. It provides tools for data analysis, predictive modeling, and automatic machine learning, allowing users to build and deploy cognitive applications.

Key features:

  • Distributed Machine Learning: H2O.ai offers a distributed computing framework that enables the parallel execution of machine learning algorithms across multiple machines. This allows for faster model training and scalability, making it suitable for handling large datasets and complex models.
  • AutoML: H2O.ai provides an automated machine learning (AutoML) capability that automates the process of model selection, hyperparameter tuning, and feature engineering. AutoML helps users quickly build and deploy machine learning models without requiring extensive expertise in data science.
  • Deep Learning: H2O.ai supports deep learning algorithms, including neural networks, for tasks such as image and text analysis. The platform provides pre-built deep learning models and tools for training and deploying them effectively.
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Top 10 High-Performance Computing Clusters

What is High-Performance Computing (HPC)?

It’s more important than ever to have HPC resources that can tackle your toughest challenges. The technology that powers today’s biggest breakthroughs — including simulation, data analytics, artificial intelligence (AI), machine learning, and more — demands robust, scalable computing power. With Altair’s industry-leading HPC tools, you can seamlessly orchestrate, visualize, analyze, and optimize your most demanding workloads.

Here are the top 10 high-performance computing clusters:

  1. Summit
  2. Sierra
  3. Sunway TaihuLight
  4. Tianhe-2A (Milky Way-2A)
  5. Frontera
  6. Piz Daint
  7. Trinity
  8. AI Bridging Cloud Infrastructure (ABCI)
  9. SuperMUC-NG
  10. Stampede2

1. Summit –

Located at Oak Ridge National Laboratory, Summit is currently the world’s most powerful supercomputer. It has a peak performance of over 200 petaflops and is used for a wide range of scientific research, including climate modeling, physics simulations, and genomics.

Key features:

  • Massive Computing Power: Summit is currently one of the world’s most powerful supercomputers. It has a peak performance of over 200 petaflops, enabling it to perform a massive number of calculations per second.
  • Heterogeneous Architecture: Summit features a heterogeneous architecture that combines traditional central processing units (CPUs) with powerful graphics processing units (GPUs). This combination allows for accelerated computing and improved performance for a wide range of applications.
  • High Memory Capacity: Summit has a substantial memory capacity, which is essential for handling large datasets and memory-intensive workloads. It features high-bandwidth memory (HBM) that provides fast data access and processing.

2. Sierra –

Sierra is a supercomputer located at Lawrence Livermore National Laboratory. It is used for various applications, including nuclear weapons simulations, materials science research, and astrophysics. Sierra has a peak performance of over 125 petaflops.

Key features:

  • High Performance: Sierra is a high-performance supercomputer with a peak performance of over 125 petaflops. It can perform a vast number of calculations per second, making it well-suited for complex simulations and data-intensive workloads.
  • Advanced Architecture: Sierra features a hybrid architecture that combines traditional central processing units (CPUs) with graphics processing units (GPUs). This combination allows for accelerated computing and improved performance for a wide range of applications.\
  • Heterogeneous Computing: The use of GPUs in Sierra enables efficient parallel processing, making it ideal for applications that require massive parallelisms, such as physics simulations, climate modeling, and materials science research.

3. Frontera –

Frontera is a supercomputer located at the Texas Advanced Computing Center. It is designed to support a wide range of scientific and engineering applications and has a peak performance of over 23 petaflops.

Key features:

  • High Performance: Frontera is a high-performance supercomputer with a peak performance of over 23 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of computational workloads.
  • Leadership-Class Computing: Frontera is one of the National Science Foundation’s (NSF) leadership-class computing resources. It is dedicated to supporting scientific research and innovation across various disciplines.
  • Advanced Architecture: Frontera features a heterogeneous architecture that combines powerful Intel Xeon processors with NVIDIA GPUs. This combination enables accelerated computing and improved performance for diverse scientific applications.

4. Piz Daint –

Piz Daint is a supercomputer located at the Swiss National Supercomputing Centre. It is used for computational research in various fields, including climate modeling, molecular dynamics simulations, and quantum chromodynamics. Piz Daint has a peak performance of over 21 petaflops.

Key features:

  • Hybrid Architecture: Piz Daint features a hybrid architecture that combines traditional central processing units (CPUs) with powerful graphics processing units (GPUs). This hybrid configuration enables accelerated computing and improved performance for a wide range of scientific applications.
  • High Performance: Piz Daint is a high-performance supercomputer with a peak performance of over 25 petaflops. It can perform a massive number of calculations per second, making it well-suited for complex simulations, data analytics, and large-scale computations.
  • Energy Efficiency: Piz Daint is designed to be energy-efficient, incorporating power-saving technologies and techniques to optimize energy consumption. It aims to maximize computational performance while minimizing its environmental footprint.

5. Tianhe-2A –

Tianhe-2A, also known as Milky Way-2A, is a supercomputer located in China. It has a peak performance of over 13 petaflops and is used for a variety of scientific and industrial applications.

Key features:

  • High Performance: Tianhe-2A is a high-performance supercomputer with a peak performance of over 61 petaflops. It can perform an enormous number of calculations per second, making it one of the most powerful supercomputers in the world.
  • Advanced Architecture: Tianhe-2A features a hybrid architecture that combines Intel Xeon processors with custom-built Matrix-2000 co-processors. This hybrid configuration enables accelerated computing and improved performance for a wide range of applications.
  • Heterogeneous Computing: The Matrix-2000 co-processors in Tianhe-2A provide massively parallel processing capabilities, making it ideal for applications that require high degrees of parallelism, such as simulations, weather forecasting, and scientific research.

6. Stampede2 –

Stampede2 is a supercomputer located at the Texas Advanced Computing Center. It is designed to support large-scale scientific and engineering simulations and has a peak performance of over 18 petaflops.

Key features:

  • High Performance: Stampede2 is a high-performance supercomputer with a peak performance of over 18 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of scientific and computational workloads.
  • Advanced Architecture: Stampede2 features a heterogeneous architecture that combines Intel Xeon processors with NVIDIA GPUs. This hybrid configuration enables accelerated computing and improved performance for diverse scientific applications.
  • Scalability and Parallel Processing: Stampede2 is designed for scalability and parallel processing, allowing researchers to distribute and process workloads across a large number of compute nodes. This capability enables efficient handling of large-scale simulations and data-intensive tasks.

7. MareNostrum –

MareNostrum is a supercomputer located in Spain. It is used for a wide range of research areas, including weather forecasting, climate modeling, and bioinformatics. MareNostrum has a peak performance of over 11 petaflops.

Key features:

  • High Performance: MareNostrum is a high-performance supercomputer with a peak performance of over 11 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of scientific simulations and computational workloads.
  • Advanced Architecture: MareNostrum features a heterogeneous architecture that combines Intel Xeon processors with NVIDIA GPUs. This hybrid configuration enables accelerated computing and improved performance for diverse scientific applications.
  • Scalability and Parallel Processing: MareNostrum is designed for scalability and parallel processing, allowing researchers to distribute and process workloads across a large number of compute nodes. This capability enables efficient handling of large-scale simulations and data-intensive tasks.

8. Shaheen II –

Shaheen II is a supercomputer located at the King Abdullah University of Science and Technology in Saudi Arabia. It is used for scientific research in various domains, including computational fluid dynamics, molecular dynamics, and seismic imaging. Shaheen II has a peak performance of over 7 petaflops.

Key features:

  • High Performance: Shaheen II is a high-performance supercomputer with a peak performance of around 7.2 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of scientific simulations and computational workloads.
  • Advanced Architecture: Shaheen II features a heterogeneous architecture that combines Intel Xeon processors with NVIDIA GPUs. This hybrid configuration enables accelerated computing and improved performance for diverse scientific applications.
  • Scalability and Parallel Processing: Shaheen II is designed for scalability and parallel processing, allowing researchers to distribute and process workloads across a large number of compute nodes. This capability enables efficient handling of large-scale simulations and data-intensive tasks.

9. Hazel Hen –

Hazel Hen is a supercomputer located at the High-Performance Computing Center Stuttgart in Germany. It is used for simulations and data analysis in fields such as physics, engineering, and life sciences. Hazel Hen has a peak performance of over 7 petaflops.

Key features:

  • High Performance: Hazel Hen is a high-performance supercomputer with a peak performance of over 7 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of scientific simulations and computational workloads.
  • Advanced Architecture: Hazel Hen features a heterogeneous architecture that combines Intel Xeon processors with NVIDIA GPUs. This hybrid configuration enables accelerated computing and improved performance for diverse scientific applications.
  • Scalability and Parallel Processing: Hazel Hen is designed for scalability and parallel processing, allowing researchers to distribute and process workloads across a large number of compute nodes. This capability enables efficient handling of large-scale simulations and data-intensive tasks.

10. Tsubame 3.0 –

Tsubame 3.0 is a supercomputer located at the Tokyo Institute of Technology in Japan. It is used for research in various areas, including deep learning, computational fluid dynamics, and molecular dynamics simulations. Tsubame 3.0 has a peak performance of over 4 petaflops.

Key features:

  • High Performance: Tsubame 3.0 is a high-performance supercomputer with a peak performance of over 12 petaflops. It can perform a massive number of calculations per second, making it suitable for a wide range of scientific simulations and computational workloads.
  • Advanced Architecture: Tsubame 3.0 features a heterogeneous architecture that combines Intel Xeon processors with NVIDIA GPUs. This hybrid configuration enables accelerated computing and improved performance for diverse scientific applications.
  • Scalability and Parallel Processing: Tsubame 3.0 is designed for scalability and parallel processing, allowing researchers to distribute and process workloads across a large number of compute nodes. This capability enables efficient handling of large-scale simulations and data-intensive tasks.
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Top 10 Cloud Computing Platforms

What is Cloud Computing?

Cloud computing stores and accesses data and programs over the internet instead of hard drives, physical servers, or personal computers. In its simplest terms, cloud computing uses a network of remote servers to store, manage, and process data instead of relying on local storage devices like hard drives. A cloud is essentially a group of servers that are accessible online to store and share information.

Cloud computing is used by individuals and businesses alike to store their data remotely and access it from any computer or device with an internet connection. For example, with cloud computing, you can send files back and forth while working with colleagues, access your photos on your phone or computer, or even use programs like Google Docs or Microsoft Word. Using the cloud means that the servers you’re using are not located in the exact physical location as you are; they’re accessible via the internet, making them more accessible and secure. Also, you can store your data and backup essential files in case of a disaster.

Cloud Computing Platform

The operating system and hardware of a server in an Internet-based data center are referred to as a cloud platform. It enables remote and large-scale coexistence of software and hardware devices. The distribution of various services through the Internet is what a cloud computing platform is. This is a common definition of a cloud computing platform. These resources include data storage, servers, databases, networking, and software, among other tools and applications. 

Cloud-based storage allows you to store files in a distant database rather than maintaining them on a proprietary hard drive or local storage device. As long as an electronic device has internet connectivity, it has access to the data as well as the software applications needed to run it. People and businesses are increasingly turning to cloud computing platforms for a variety of reasons, including cost savings, enhanced productivity, speed and efficiency, performance, and security.

Some popular cloud computing platforms include:

  1. Amazon Web Services (AWS)
  2. Microsoft Azure
  3. Google Cloud Platform (GCP)
  4. Alibaba Cloud
  5. IBM Cloud
  6. Oracle Cloud
  7. Salesforce
  8. VMware Cloud
  9. Tencent Cloud
  10. DigitalOcean

1. Amazon Web Services (AWS):

AWS is a comprehensive cloud platform offered by Amazon. It provides a wide range of services, including computing power, storage, databases, networking, machine learning, and analytics.

Key features:

  • Compute Services: AWS provides a range of computing services, including Amazon Elastic Compute Cloud (EC2) for scalable virtual servers, AWS Lambda for serverless computing, and AWS Batch for batch computing workloads.
  • Storage Services: AWS offers multiple storage services, such as Amazon Simple Storage Service (S3) for object storage, Amazon Elastic Block Store (EBS) for block-level storage volumes, and Amazon Glacier for long-term data archival.
  • Database Services: AWS provides a variety of database services, including Amazon Relational Database Service (RDS) for managed relational databases, Amazon DynamoDB for NoSQL databases, and Amazon Redshift for data warehousing.

2. Microsoft Azure:

Azure is Microsoft’s cloud platform that offers a broad set of services for building, deploying, and managing applications and services. It includes capabilities for virtual machines, storage, databases, AI, analytics, and more.

Key features:

  • Virtual Machines: Azure provides virtual machines (VMs) that offer scalable computing power, allowing users to run a wide range of operating systems and applications in the cloud.
  • Azure App Service: This feature allows users to build, deploy, and scale web and mobile applications easily. It supports multiple programming languages and frameworks.
  • Azure Functions: Azure Functions enables serverless computing, allowing users to run code without managing infrastructure. It automatically scales based on demand and charges only for the actual execution time.

3. Google Cloud Platform (GCP):

GCP is Google’s cloud computing platform that offers a suite of cloud services, including computing, storage, databases, machine learning, and data analytics. It also provides tools for big data processing and IoT applications.

Key features:

  • Compute Engine: Compute Engine provides virtual machines (VMs) with flexible configurations and high-performance computing options. Users can choose from predefined machine types or create custom machine types.
  • App Engine: App Engine is a fully managed platform that allows developers to build and deploy scalable web applications and APIs. It automatically scales applications based on demand and handles infrastructure management.
  • Kubernetes Engine: Kubernetes Engine is a managed container orchestration service based on Kubernetes. It simplifies the deployment, management, and scaling of containerized applications.

4. IBM Cloud:

IBM Cloud is an enterprise-grade cloud platform that offers a range of services, including compute, storage, AI, blockchain, and IoT. It focuses on hybrid cloud deployments, allowing businesses to integrate their existing infrastructure with cloud resources.

Key features:

  • Virtual Servers: IBM Cloud provides virtual server instances known as IBM Virtual Servers, offering flexible configurations and a wide range of compute options.
  • Kubernetes Service: IBM Kubernetes Service is a managed container orchestration platform based on Kubernetes. It simplifies the deployment, management, and scaling of containerized applications.
  • Cloud Object Storage: IBM Cloud Object Storage offers scalable and durable object storage for storing and retrieving unstructured data. It provides flexible storage tiers and global data availability.

5. Oracle Cloud:

Oracle Cloud provides a set of cloud services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It offers solutions for database management, application development, analytics, and more.

Key features:

  • Compute: Oracle Compute provides virtual machine instances with customizable configurations and options for both Intel and AMD processors. It offers high-performance computing capabilities.
  • Autonomous Database: Oracle Autonomous Database is a fully managed and self-driving database service. It uses AI and machine learning to automate database management tasks, such as patching, tuning, and backups.
  • Object Storage: Oracle Object Storage provides scalable and durable object storage for storing and retrieving unstructured data. It offers high durability and data protection capabilities.

6. Alibaba Cloud:

Alibaba Cloud is the cloud computing arm of Alibaba Group, one of the largest e-commerce companies. It offers a wide range of services, including computing, storage, networking, security, and big data processing. It is prevalent in China and Asia.

Key features:

  • Elastic Compute Service (ECS): Alibaba ECS offers scalable virtual server instances with flexible configurations. It provides a wide range of instance types and allows users to customize CPU, memory, storage, and networking resources.
  • Object Storage Service (OSS): Alibaba OSS provides scalable and secure object storage for storing and retrieving large amounts of unstructured data. It offers high durability, availability, and low latency.
  • ApsaraDB for RDS: ApsaraDB for RDS is a fully managed relational database service that supports various database engines, including MySQL, SQL Server, PostgreSQL, and Oracle. It offers automated backups, high availability, and scalability.

7. Salesforce:

While Salesforce is primarily known for its customer relationship management (CRM) software, it also offers a cloud computing platform known as Salesforce Platform. It allows users to build and deploy custom applications using Salesforce’s infrastructure and services.

Key features:

  • Contact and Account Management: Salesforce provides a centralized database to store and manage customer contact information, accounts, and related details. It allows businesses to track each customer’s interactions, activities, and history.
  • Sales Opportunity Management: Salesforce offers tools for managing sales opportunities, including tracking leads, managing pipelines, and forecasting sales revenue. It enables sales teams to collaborate, prioritize leads, and close deals more effectively.
  • Sales Performance and Analytics: Salesforce provides dashboards and reports to analyze sales performance, track key metrics, and gain insights into the effectiveness of sales efforts. It helps identify trends, forecast revenue, and make data-driven decisions.

8. Huawei Cloud:

Huawei Cloud is a global cloud service provider with a growing presence, offering a wide range of services across various industries.

Key features:

  • Elastic Compute Service (ECS): Huawei ECS offers scalable virtual servers with customizable configurations. It provides a wide range of instance types and allows users to easily adjust resources according to their needs.
  • Object Storage Service (OBS): Huawei OBS provides highly available and durable object storage for storing and retrieving unstructured data. It supports multiple storage classes and offers flexible data management options.
  • Database Services: Huawei Cloud offers various database services, including Relational Database Service (RDS) for MySQL, PostgreSQL, and SQL Server databases, as well as Distributed Relational Database Service (DRDS) for distributed database management.

9. VMware Cloud:

VMware Cloud is a hybrid cloud platform that enables organizations to seamlessly run, manage, and secure applications across multiple clouds and on-premises environments.

Key features:

  • VMware Cloud Foundation: VMware Cloud Foundation is an integrated platform that combines compute, storage, networking, and management services into a unified infrastructure stack. It provides a consistent operational experience across private and public clouds.
  • VMware vSphere: VMware vSphere is a virtualization platform that enables the creation and management of virtual machines. It provides high-performance compute resources, scalability, and workload mobility across on-premises and cloud environments.
  • VMware vSAN: VMware vSAN is a software-defined storage solution that is tightly integrated with vSphere. It aggregates local storage devices and provides distributed shared storage for virtual machines. It offers features like data deduplication, compression, and encryption.

10. Tencent Cloud:

Tencent Cloud is one of the leading cloud providers in China, offering a comprehensive suite of cloud services for businesses and developers.

Key features:

  • Elastic Compute Service (ECS): Tencent ECS offers scalable virtual server instances with customizable configurations. It provides a wide range of instance types and allows users to easily adjust resources according to their needs.
  • Object Storage Service (COS): Tencent COS provides highly available and durable object storage for storing and retrieving unstructured data. It offers features like automatic tiering, data archiving, and data migration.
  • Database Services: Tencent Cloud offers various database services, including TencentDB for MySQL, PostgreSQL, and MariaDB, as well as distributed databases like TDSQL. It provides options for high availability, scalability, and data management.
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Top Cloud computing and operating software

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Top Cloud computing and operating software.

OpenStack

OpenStack is a free and open-source cloud-computing software platform.[2] Users primarily deploy it as an infrastructure-as-a-service (IaaS). The technology consists of a group of interrelated projects that control pools of processing, storage, and networking resources throughout a data center—which users manage through a web-based dashboard, through command-line tools, or through a RESTful API. OpenStack.org released it under the terms of the Apache License.

CloudStack

CloudStack is an open source cloud computing software for creating, managing, and deploying infrastructure cloud services. It uses existing hypervisors such as KVM, VMware vSphere, and XenServer/XCP for virtualization. In addition to its own API, CloudStack also supports the Amazon Web Services (AWS) API and the Open Cloud Computing Interface from the Open Grid Forum.

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Best Cloud Computing and Operating Tools

top-cloud-computing-and-operating-software

Top Cloud computing and operating software.

OpenStack

OpenStack is a free and open-source cloud-computing software platform.[2] Users primarily deploy it as an infrastructure-as-a-service (IaaS). The technology consists of a group of interrelated projects that control pools of processing, storage, and networking resources throughout a data center—which users manage through a web-based dashboard, through command-line tools, or through a RESTful API. OpenStack.org released it under the terms of the Apache License.

CloudStack

CloudStack is an open source cloud computing software for creating, managing, and deploying infrastructure cloud services. It uses existing hypervisors such as KVM, VMware vSphere, and XenServer/XCP for virtualization. In addition to its own API, CloudStack also supports the Amazon Web Services (AWS) API and the Open Cloud Computing Interface from the Open Grid Forum.

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Cloud Computing and ROI

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Cloud Computing and ROI

Most think cloud computing is about the ability to save operational costs. That may or may not be the case, depending upon your enterprise or ecommerce problem domain. Indeed, there are many dimensions to consider here, including:

  • Ongoing operational cost reduction.
  • The value of preserving capital.
  • The value of upsizing on-demand.
  • The value of downsizing on-demand.
  • The value of shifting the risk.
  • The value of agility.

Let’s explore each:

Operational Cost Reduction

We all know that cloud computing is cheap…okay, cheaper…okay, it can be cheap. Thus it’s a good idea to figure out the actual cost reductions that cloud computing can bring to your enterprise IT. The trick here is not only to figure out how much money can be saved, but how much it will cost to save that money.

Preserving Capital

It’s money in the bank which allows the business to run. The more money we have in the bank, the more we can purchase things for the core business such as inventory that can be sold, or new plant equipment that will save the company money during production. In any event, it’s good to keep as much capital as possible on hand to invest in the business, and not into infrastructure such as data centers, hardware, and software.

Upsizing On-Demand

Core to the ability to preserve capital is the ability to upsize your IT infrastructure on demand, or simply pay more operational dollars for additional computing capacity which would traditionally require a capital expenditure. Many cloud computing providers call this being elastic, or the ability to grow or contract to accommodate the business. For example, you can call upon the cloud computing provider to support an additional user and processing load through the holiday, when considering ecommerce solutions.

Downsizing On-Demand

Like upsizing on-demand, you need to consider what it will take to reduce computing capacity and dollars paid. What does it take to scale down in case you no longer need the computing resource and want to reduce costs as well? Such is the case within many ecommerce systems with capacity requirements that are seasonal.

Shifting the Risk

Another core value of cloud computing is the ability to shift the risk from your enterprise to the cloud computing provider. This concept refers to the fact that, since it’s up to the cloud provider to handle the computing processing load and you’ll pay by use, then it’s possible to reduce the risk that you’ll run out of capacity to support your customers and core business processes. The risk functionally shifts to the cloud provider who is better suited to accept that risk.

Agility

Agility means the ability to change the IT infrastructure faster to adapt to the changing needs of the business, such as market downturns, or the introduction of a key product to capture a changing market. This, of course, provides a strategic advantage and allows the business to have a better chance of long-term survival. These days many enterprises are plagued by IT infrastructures that are so poorly planned and fragile that they hurt the business by not providing the required degree of agility.

Article Source: http://www.getelastic.com/cloud-computing-and-roi/

 

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Overcoming Cloud Computing Obstacles – Cloud Computing adoption challenges

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Overcoming Cloud Computing Obstacles

How to Make the Case to Switch to the Cloud

Companies choosing the cloud computing route often have to make the case for the switch to new technology to a board or investors. The most common obstacles raised towards adoption of cloud technology are concerns around the availability of service, security and auditability of company data and performance issues around data transfer or loading speeds.

Availability of Service

The utility computing economy is currently such that competition is growing among providers. There are a few large, corporate providers of cloud services, such as Amazon and Google, as well as a large handful of small and medium players in the market. The number of companies jumping on the cloud provision bandwagon is growing very rapidly, and because of this, there is great focus on providing a reliable and stable service. Many providers will offer their clients a Service Level Agreement (SLA), stating the acceptable levels of unplanned service downtime, as well as what amount of compensation is available should the SLA be breached. Companies signing up with a cloud provider should look for an SLA offering at least 99.9% availability, but preferably 99.99%. The best way to ensure full systems available for a company’s cloud services is to engage more than one cloud provider for the provision of the same service. This way, if something should happen to the first provider, the second one will be able to pick up the slack.

Security and Auditability of Company Data

Many cloud computing providers offer data encryption as part of their service. Small and medium sized businesses, that are not accountable to regulatory bodies can probably use the standard encryption technologies provided by most utility computing services. Companies, such as small investment firms or hedge funds, will need to invest in higher security measures for storing data in the cloud. By nature, most of the cloud computing infrastructures currently available by mainstream providers are what is known as public clouds. (Armbrust, et al) This means that computer systems are purposed for general use among all customers, and no distinction is made as to which company is using what hardware. This is generally fine for the standard SME, but those requiring data audit capabilities will need what is known as a private cloud. The private cloud is a collection of computing systems that has been walled off, both physically (in a caged area of a data center) and logically, using combinations of Virtual Private Networks (VPN), firewalls and, often, private leased line data connections which are installed to directly connect a company to its cloud service provider.

Performance Issues

There is often concern around the performance of data transfer within cloud applications. However, it has been shown that, generally, once data has been transferred to the cloud, the speeds of transfer between cloud servers is then much faster than it was on local drives. This is because most current cloud computing infrastructure is far more powerful than what is normally seen in SMEs. The obstacle here is the initial transfer of data onto the cloud service. This can be overcome by loading all data on portable hard drives and shipping it to the cloud service provider for the initial load. Generally, once the initial load is complete, subsequent file transfers will be much smaller in size. Exceptions to this are the data-intensive users of elastic cloud services. For these users, hard drive transfer would currently still be the most economically viable option, but there is evidence that the cost of a private leased line may decrease in future as the cost of high-end routers decreases. (Armbrust, et al)

With a careful analysis of company IT infrastructure requirements, and an appropriate plan to minimize the risks associated with the top obstacles to adoption of cloud computing, business now have the opportunity to adopt a technology which has matured over the past decade into a feasible manner in which to provide reliable and efficient corporate computing at the fraction of the cost of a full IT hardware refresh.

http://business-technology.suite101.com/article.cfm/overcoming-cloud-computing-obstacles
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Cloud Computing Trends | Cloud Adoption Analysis | Organizations

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We just finished the first decade of this century/millennium. The early part of this decade saw great worry about the Year 2000 problem. Much gloom and doom was predicted, but things passed off smoothly. No apocalyptic upheaval.

As we usher in the next decade, the biggest buzzword is “Cloud Computing”, a rapprochement of ASP, SaaS, SOA, Virtualization, Grid Computing, Enterprise 2.0, etc. All these buzzwords have been making the rounds over past few years. Finally, computing as a “utility” seems practical and doable. Amazon took the lead in introducing AWS (Amazon Web Services) way back in 2003. It then brought in Storage as a Service concept via S3 (Simple Shared Storage). It also introduced EC2 (Elastic Computing Cloud), where Infrastructure as a Service became viable.

I just read a nice summary of this written by M.R. Rangaswamy of the Sand Hill Group. While the momentum is on, MR says large enterprises are going to be slow adapters. Much cloud adoption is in the SMB arena where lower TCO and capex override any concern for security and scale. Older vendors like IBM will offer a hybrid model – In-house systems and cloud. This is a no-brainer, as there is a huge legacy of production systems in Fortune 1000 companies running in the premises. But “pure cloud” vendors like Google, Amazon, and SalesForce.com will push for “cloud-only” approach.

Another area of interest is data management, the volume of which has never been seen before. There is the NoSQL movement to deal with unstructured data and framework like Hadoop combined with the MapReduce algorithm is getting quick adoption for fast search.

This decade will see a big landscape change in the computing arena – from the model of computing to how we store and manage data for access and analytics.

Welcome to 2010.

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Understand Cloud Computing in Simple Terms – Maximumbit Inc

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Cloud Computing is an emerging computing technology that uses the internet and central remote servers to maintain data and applications. Cloud computing allows consumers and businesses to use applications without installation and access their personal files at any computer with internet access. This technology allows for much more efficient computing by centralizing storage, memory, processing and bandwidth. Cloud computing is broken down into three segments: “applications,” “platforms,” and “infrastructure.” Each segment serves a different purpose and offers different products for businesses and individuals around the world.

Cloud computing comes into focus only when you think about what IT always needs: a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. Cloud computing encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends IT’s existing capabilities.

In June 2009, a study conducted by Version One found that 41% of senior IT professionals actually don’t know what cloud computing is and two-thirds of senior finance professionals are confused by the concept, highlighting the young nature of the technology. In Sept 2009, an Aberdeen Group study found that disciplined companies achieved on average an 18% reduction in their IT budget from cloud computing and a 16% reduction in data center power costs.

Depending on who you are talking to, you will see different perceptions about what Cloud Computing actually is, from the simplest web-hosted solutions right through to virtualized processing environments with Web-Service initiated provisioning and decommissioning.

The main challenges for Cloud Computing before it is likely to enjoy wide-spread adoption are the following:

Persistence & Availability – The ability to continue working during outages or the ability to mitigate outages.
Privacy and National Security Concerns – The hosting of information outside of your country’s borders does concern Public Sector organizations. The US Patriot Act for example is a concern for some countries in adopting cloud services. It is thought that Country-silted Clouds may be able to address this.
Geo-Political Information Management Concerns – The Political risk a country takes on by housing information for another country.

Cloud Computing is all about:

1. SaaS (Software as a Service)


These type of cloud computing delivers a single application through the browser to thousands of customers using a multitenant architecture. On the customer side, it means no upfront investment in servers or software licensing; on the provider side, with just one app to maintain, costs are low compared to conventional hosting.

2. Utility computing


The idea is not new, but this form of cloud computing is getting new life from Amazon.com, Sun, IBM, and others who now offer storage and virtual servers that IT can access on demand. Early enterprise adopters mainly use utility computing for supplemental, non-mission-critical needs, but one day, they may replace parts of the datacenter. Other providers offer solutions that help IT create virtual datacenters from commodity servers, such as 3Tera’s AppLogic and Cohesive Flexible Technologies’ Elastic Server on Demand. Liquid Computing LiquidQ offers similar capabilities, enabling IT to stitch together memory, I/O, storage, and computational capacity as a virtualized resource pool available over the network.

3. Web services in the cloud


Closely related to SaaS, Web service providers offer APIs that enable developers to exploit functionality over the Internet, rather than delivering full-blown applications. They range from providers offering discrete business services to the full range of APIs and even conventional credit card processing services.

4. Platform as a service


Another SaaS variation, this form of cloud computing delivers development environments as a service. You build your own applications that run on the provider’s infrastructure and are delivered to your users via the Internet from the provider’s servers.

5. MSP (managed service providers)


One of the oldest forms of cloud computing, a managed service is basically an application exposed to IT rather than to end-users, such as a virus scanning service for e-mail or an application monitoring service (which Mercury, among others, provides). Managed security services delivered by Secure Works, IBM, and Verizon fall into this category, as do such cloud-based anti-spam services as Postini, recently acquired by Google. Other offerings include desktop management services, such as those offered by Center Beam or Ever dream.

6. Service commerce platforms


A hybrid of SaaS and MSP, this cloud computing service offers a service hub that users interact with. They’re most common in trading environments, such as expense management systems that allow users to order travel or secretarial services from a common platform that then coordinates the service delivery and pricing within the specifications set by the user. Think of it as an automated service bureau. Well-known examples include Rearden Commerce and Ariba.

7. Internet integration


The integration of cloud-based services is in its early days. OpSource, which mainly concerns itself with serving SaaS providers, recently introduced the OpSource Services Bus, which employs in-the-cloud integration technology from a little startup called Boomi. SaaS provider Workday recently acquired another player in this space, CapeClear, an ESB (enterprise service bus) provider that was edging toward b-to-b integration. Way ahead of its time, Grand Central — which wanted to be a universal “bus in the cloud” to connect SaaS providers and provide integrated solutions to customers — flamed out in 2005.

 

Citrix Cloud Center

C3 is designed to give cloud providers a complete set of service delivery infrastructure building blocks for hosting, managing and delivering cloud-based computing services. C3 includes a reference architecture that combines the individual capabilities of several Citrix product lines to offer a powerful, dynamic, secure and highly available service-based infrastructure ideally suited to large-scale, on-demand delivery of both IT infrastructure and application services. This architecture consists of four key components:

Platform – Powered by Citrix XenServerTM Cloud Edition:  The new XenServer Cloud Edition is a powerful virtual infrastructure solution optimized for service provider environments. It combines the cloud-proven scalability of the Xen® hypervisor which powers most of the world’s largest clouds, with all the virtualization management and dynamic workload provisioning capabilities of the full Citrix XenServer product line enabling cloud providers to host and manage any combination of Windows® and Linux environments. XenServer Cloud Edition also features an innovative consumption based pricing model to meet the needs of service providers that charge their customers based on metered resource use.

Delivery – Powered by Citrix® NetScaler’s®:  Through its rich policy-based AppExpert engine, Citrix NetScaler’s delivers cloud-based resources to users over the Web, continually optimizing user application performance and security by dynamically scaling the number of virtual machines (VMs) or servers available in response to changing workload demands and infrastructure availability. This allows cloud providers to balance workloads across large distributed cloud environments and transparently redirect traffic to alternate capacity on or off premise in the event of network failures or datacenter outages.  NetScaler’s can also dramatically reduce server requirements in large cloud centers by offloading protocol and transaction processing from backend server pools. NetScaler’s proven architecture is designed for highly scalable, multi-tenant Web applications and delivers Web services to an estimated 75 percent of all Internet users each day.

Bridge – Powered by Citrix WANScaler:  As larger enterprises begin experimenting with cloud-based services for parts of their own infrastructure and application hosting strategy, cloud providers will also need reliable and secure ways to provide a seamless bridge between hosted cloud services and premise-based enterprise services. Over time, C3 will incorporate a set of open interfaces that allow customers to easily move virtual machines and application resources into a cloud-based datacenter and back again as needed. WANScaler technology will play a critical role in this enterprise bridge by accelerating and optimizing application traffic between the cloud and the enterprise datacenter, even over long distances.

Orchestration – Powered by Citrix Workflow Studio TM: Tying it all together, Citrix Workflow Studio provides a powerful orchestration and workflow capability that allows the products in the C3 portfolio to be dynamically controlled and automated, and integrated with customer business and IT policy. Workflow Studio allows customers to control their infrastructure dynamically–integrating previously disconnected processes and products into a single powerful, orchestrated and cohesive system. This unique capability will make it easier for cloud providers to enable highly efficient burst able clouds that automatically scale resources up and down based on demand, shifting hardware resources to where they are most needed and powering them down for maximum power savings when not needed.

Today, with such cloud-based interconnection seldom in evidence, cloud computing might be more accurately described as “sky computing,” with many isolated clouds of services which IT customers must plug into individually. On the other hand, as virtualization and SOA permeate the enterprise, the idea of loosely coupled services running on an agile, scalable infrastructure should eventually make every enterprise a node in the cloud. It’s a long-running trend with a far-out horizon. But among big megatrends, cloud computing is the hardest one to argue with in the long term.

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Cloud Computing: The Computer is out the Window!

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Debates have been heating up about Cloud Computing (CC). Biggest challenge is security and bigger bigger challenge is ‘control’ of a company’s tech assets. The only limitation so far has been internet bandwidth, reason why it took CC a while to become mainstream. Futurists such as Nicolas Negroponte saw it coming a while back and evangelized about it repeatedly in his book ‘being digital’ (a masterpiece). Entrepreneurs like Marc Andreessen saw the opportunities early and started Loud Cloud back in 1999 (now Opsware) and Amazon today generates millions in revenue because of Amazon Web Services (Amazon launched its Elastic Compute cloud (EC2) for companies to use back 2006: yes, commercially). What really triggered CC is none other than Web 2.0: all them browser-based enterprise applications! In Summary: we’ve all contributed to Cloud Computing, without realizing it. You’ve been using Cloud Computing.

Cloud Computing is fantastic for emerging economies and their speed in adopting ‘affordable’ new technology. Look what’s happening in Africa, where mobile internet and new telecom infrastructures are making it possible to leap into internet adoption. So why a computer in the first place. Computers are becoming more of a luxury item vs. a need?

Conclusion: Cloud Computing is not a trend, but a major shift in how we ’smartly’ manage technology. For those who are still in denial and resisting change, they’re already lagging and need to catch up fast, cuz that computer is out of the Window!

Great reference here on the history of CC and how far it dates back (60’s) thanks to Computer Weekly http://tinyurl.com/yj7rln3

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