Cloud & Edge Computing

Cloud & Edge Computing

The emergence of new technologies such as IoT, & Autonomous Vehicles/Connected Cars (V2X, M2M, Automotive Telematics), XR (VR/AR/MR), fueled and enabled by the high data speeds promised by 5G is expected to generate BIG DATA that needs to be processed at HYPERSCALERS such Google, Amazon, Facebook, Microsoft, Tencents, Alibaba & Baidu. Harvesting this metadata can be best achieved through distributed applications & scaleout workloads (as opposed to conventional scale-up, performance-driven processing - traditionally supported by Intel Xeon chips). This generates the need  of  Cloud Computing (SaaS, PaaS, IaaS) at Datacenters.

In addition to the hyperscalars, Enterprises - that have traditionally relied on their IT infrastructure being on-premises, have started moving some of their assets to the cloud in adoption of a Hybrid Cloud environment - thereby converting to a pay-as-you-go software & services model from being locked into infrastructural IT hardware - as a means to offset capex concerns into opex overheads. This has also helped fuel growth of the cloud - and led to an alignment of the protocols and paradigms, between On-Premises and Cloud computing models.

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https://azure.microsoft.com/en-us/overview/what-is-cloud-computing/

Simply put, cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. You typically pay only for cloud services you use, helping you lower your operating costs, run your infrastructure more efficiently, and scale as your business needs change.

Top benefits of cloud computing

Cloud computing is a big shift from the traditional way businesses think about IT resources. Here are seven common reasons organizations are turning to cloud computing services:

Cost: Cloud computing eliminates the capital expense of buying hardware and software and setting up and running on-site datacenters—the racks of servers, the round-the-clock electricity for power and cooling, and the IT experts for managing the infrastructure. It adds up fast.

Speed: Most cloud computing services are provided self service and on demand, so even vast amounts of computing resources can be provisioned in minutes, typically with just a few mouse clicks, giving businesses a lot of flexibility and taking the pressure off capacity planning.

Economies of scale: The benefits of cloud computing services include the ability to scale elastically. In cloud speak, that means delivering the right amount of IT resources—for example, more or less computing power, storage, bandwidth—right when they’re needed, and from the right geographic location.

Productivity: On-site datacenters typically require a lot of “racking and stacking”—hardware setup, software patching, and other time-consuming IT management chores. Cloud computing removes the need for many of these tasks, so IT teams can spend time on achieving more important business goals.

Performance: The biggest cloud computing services run on a worldwide network of secure datacenters, which are regularly upgraded to the latest generation of fast and efficient computing hardware. This offers several benefits over a single corporate datacenter, including reduced network latency for applications and greater economies of scale.

Reliability: Cloud computing makes data backup, disaster recovery, and business continuity easier and less expensive because data can be mirrored at multiple redundant sites on the cloud provider’s network.

Security: Many cloud providers offer a broad set of policies, technologies, and controls that strengthen your security posture overall, helping protect your data, apps, and infrastructure from potential threats.

Types of cloud computing

Not all clouds are the same and not one type of cloud computing is right for everyone. Several different models, types, and services have evolved to help offer the right solution for your needs.
First, you need to determine the type of cloud deployment, or cloud computing architecture, that your cloud services will be implemented on. There are three different ways to deploy cloud services: on a public cloud, private cloud, or hybrid cloud.

Public clouds are owned and operated by a third-party cloud service providers, which deliver their computing resources, like servers and storage, over the Internet. Microsoft Azure is an example of a public cloud. With a public cloud, all hardware, software, and other supporting infrastructure is owned and managed by the cloud provider. You access these services and manage your account using a web browser.

A private cloud refers to cloud computing resources used exclusively by a single business or organization. A private cloud can be physically located on the company’s on-site datacenter. Some companies also pay third-party service providers to host their private cloud. A private cloud is one in which the services and infrastructure are maintained on a private network.

Hybrid clouds combine public and private clouds, bound together by technology that allows data and applications to be shared between them. By allowing data and applications to move between private and public clouds, a hybrid cloud gives your business greater flexibility, more deployment options, and helps optimize your existing infrastructure, security, and compliance.

Types of cloud services: IaaS, PaaS, serverless, and SaaS

Most cloud computing services fall into four broad categories: infrastructure as a service (IaaS), platform as a service (PaaS), serverless, and software as a service (SaaS). These are sometimes called the cloud computing "stack" because they build on top of one another. Knowing what they are and how they’re different makes it easier to accomplish your business goals.

Infrastructure as a service (IaaS)

The most basic category of cloud computing services. With IaaS, you rent IT infrastructure—servers and virtual machines (VMs), storage, networks, operating systems—from a cloud provider on a pay-as-you-go basis.

Platform as a service (PaaS)

Platform as a service refers to cloud computing services that supply an on-demand environment for developing, testing, delivering, and managing software applications. PaaS is designed to make it easier for developers to quickly create web or mobile apps, without worrying about setting up or managing the underlying infrastructure of servers, storage, network, and databases needed for development.

Serverless computing

Overlapping with PaaS, serverless computing focuses on building app functionality without spending time continually managing the servers and infrastructure required to do so. The cloud provider handles the setup, capacity planning, and server management for you. Serverless architectures are highly scalable and event-driven, only using resources when a specific function or trigger occurs.

Software as a service (SaaS)

Software as a service is a method for delivering software applications over the Internet, on demand and typically on a subscription basis. With SaaS, cloud providers host and manage the software application and underlying infrastructure, and handle any maintenance, like software upgrades and security patching. Users connect to the application over the Internet, usually with a web browser on their phone, tablet, or PC.




https://azure.microsoft.com/en-us/overview/what-is-iaas/

IaaS : Renting a datacenter (compute, storage, networking infrastructure), use on a pay-as-you-go basis
PaaS: Providing OS & development tools for app hosting & development
SaaS: Providing application software to end users over the web

https://medium.com/@mbondar/5-ways-serverless-can-revolutionize-software-development-2d31ed2a6266

Who manages what in the cloud:

IaaS:  [CSP] HW/FW + [You] Everything Else

PaaS: [CSP] HW/FW + OS/MW + [You] Apps

FaaS: [CSP] HW/FW + OS/MW + App Backend + [You] Apps Client

SaaS: [CSP] HW/FW + OS/MW + Apps

Uses of cloud computing

You’re probably using cloud computing right now, even if you don’t realize it. If you use an online service to send email, edit documents, watch movies or TV, listen to music, play games, or store pictures and other files, it’s likely that cloud computing is making it all possible behind the scenes.

You’re probably using cloud computing right now, even if you don’t realize it. If you use an online service to send email, edit documents, watch movies or TV, listen to music, play games, or store pictures and other files, it’s likely that cloud computing is making it all possible behind the scenes.

• Develop, test, run, manage cloud-native apps
• Data storage & backup
• Digital content (audio/video) streaming
• Cloud gaming
• Deliver application software on demand (SaaS)
• Data analytics & business intelligence (using ML/AI models) for informed decisions

 https://azure.microsoft.com/en-us/overview/what-is-cloud-computing
https://azure.microsoft.com/en-us/overview/cloud-computing-dictionary/

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However, under certain circumstances, such as those for low latency applications, it may not be practical to move data back and forth from its point of generation to/from the CLOUD for processing. In other cases, this may be cost prohibitive since bandwidth is limited and data transfer spectrum is shared. It may be faster and cheaper to process the data as close as possible to the endpoints and away from the cloud. This gives rise to a new paradigm of distributed computing architecture termed as Edge Computing.

Edge computing could be compute/storage/network/connectivity  resources decentralized from the cloud and closer to the endpoints. Examples inlcude infrastructural nodes (such as small-cells, routers, controllers & switches for telecom equipment), embedded systems, VM/containers for virtualization based systems, database & storage, etc.

Computing at the cloud or edge is assisted by neural network based algorithms  that are collectively grouped as Artificial Intelligence / Machine Learning (AI/ML).

The cloud is more suited to running Training based AI/ML workloads, that require heavier processing & computing needs - while the edge is more appropriate to run Inference based AI/ML applications.

While a CPU is general purpose chip that excels at running a wide variety of applications based on deterministic, high-precision computations with finite and exact outcomes, AI/ML based workloads - particularly those at edge, work on probabilistic, lower precision calculations based on predictive pattern recognition across large data sets.  In addition, a recent trend in microprocessor computing / HPC, has been to speed up processing done in a CPU by offloading tasks to co-processors & accelerators, primarily GPU's and FPGA's. However, using GPU/FPGA as accelerators requires high power consumption, that cannot be afforded particularly at the edge. This is resulting in  a new drive to develop ASIC's and custom SoC as AI Processors / Accelerators for edge-based inference type AI/ML applications.