Evolution of cloud computing

Distributed systems have been an active field of research for over 60 years, and has played a crucial role in computer science, enabling the invention of the Internet that underpins all facets of modern life. Through technological advancements and their changing role in society, distributed systems have undergone a perpetual evolution, with each change resulting in the formation of a new paradigm. Each new distributed system paradigm—of which modern prominence include cloud computing, Fog computing, and the Internet of Things (IoT)—allows for new forms of commercial and artistic value, yet also ushers in new research challenges that must be addressed in order to realize and enhance their operation. However, it is necessary to precisely identify what factors drive the formation and growth of a paradigm, and how unique are the research challenges within modern distributed systems in comparison to prior generations of systems. The objective of this work is to study and evaluate the key factors that have influenced and driven the evolution of distributed system paradigms, from early mainframes, inception of the global inter-network, and to present contemporary systems such as edge computing, Fog computing and IoT. Our analysis highlights assumptions that have driven distributed systems appear to be changing, including (1) an accelerated fragmentation of paradigms driven by commercial interests and physical limitations imposed by the end of Moore’s law, (2) a transition away fr...

The cloud is synonymous with the on-demand resource availability, full-service, API-driven, pay-per-use model. Over the years, as the cloud consumption model became popular and competitive, cloud infrastructure consolidation started, requiring the infrastructure to be cost-effective, power-efficient, feature-rich, simplified and scalable. The first generation cloud was built by putting discrete pieces of infrastructure together and wrapping them up with resource scheduling and automation APIs. The discrete components were typically provided by legacy hardware vendors providing network, storage, load balancers, firewalls and compute servers. The software layer was good enough to provide the abstraction needed for the consumption model. This solved the problem of operational complexity associated with undifferentiated infrastructure and offered a subscription model for resource consumption. AMD Laid The Groundwork For Big Hyperscaler AI Accelerator Play In The Future The "pre-cloud" infrastructure was a set of individual small data centers, where infrastructure was a cost center. The scale was limited, capping the cost savings that came from bulk purchasing power. The infrastructure was not highly reconfigurable and was full of heterogeneous environments. In contrast "early cloud" infrastructure was built as consolidated large data centers, where infrastructure was the business (versus a cost center). The scaling multiplier pushed for cost optimization, resulting in the mass...

Local Cloud Computing A long time ago in a galaxy far, far away, organizations deployed workloads within their local data centers, if not their own basements. This generation of applications was built from the ground up with labor-intensive code. Local data centers supported these types of deployments and also provided data storage and other IT functions. What changed? When “virtual machines” were introduced, a demand emerged for lightweight and granular IT tools, i.e., containers. Containers allow multiple mini-applications to run independently in the same operating system. If one needs to be updated, the others are not affected. Therefore, a new service was necessary to babysit the countless containers that make up modern applications. Editor Thus, Google developed Kubernetes to orchestrate containers. Developers, now often known as DevOps engineers, create container-based applications with capabilities that exceed what their older brothers were forced to sweat over only a few years before. But Kubernetes has its own complexities. It may, and frequently does, determine that physical resources must be provisioned, configured and updated. If no one “listens” and takes action, applications can get derailed. Big Data Centers: Few And Far Between The complexity of managing resource-heavy applications thus drove application owners to run deployments through third-party cloud-managed services. These services ensure that the desired states, as requested by Kubernetes, are automa...

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These days, our heads are all in the cloud. And like the clouds above us, a few formations and patterns have begun to prevail. However, unlike the cirrus and cumulus formations we see in the sky, the shapes in cloud computing trends look an awful lot like what we have seen in tech over the past decade. Seven years ago, Uber began redefining transit, especially in urban areas; next came food delivery services, which spiked in use during the pandemic; now, we are seeing this on-demand model transmute into a variety of cloud-based services. On-demand logic translates handily into the computing space. Unless you’re a billionaire, it’s unlikely that you have the resources to pay for a private driver who waits for you and/or brings you food. For the rest of us, we turn to interfaces like Uber and DoorDash to get driving or delivery services on demand. Not only is it more affordable, but it’s also just more practical and available exactly (and only) when you need it. Computing power is much the same. Do you really need it all the time? Sure, huge financial businesses run stress tests every night at midnight—yes, they need their own servers. And they can afford that. But most businesses do not need that kind of power all the time—for them, it’s better to pay for it only when it’s needed. What does this “need” look like? For rhetorical purposes, let’s define “us” as those not affiliated with the Big Three (Amazon, Microsoft, Google) or Fortune 500 companies. Most smaller and/or new...

By Alex Muchiri, an Alibaba Cloud MVP. He is the founder of With cloud computing, your entire Primarily, users can access three types of cloud computing deployment models as described below. While there are other types of classification, the following are based on the deployment model. 1) Public Cloud A public cloud is a large pool of computing infrastructure available to the general public on a subscription basis. This type of deployment is physically inaccessible to the end-user since it is located somewhere far away in the vendor's data centres. Some examples include Alibaba Cloud, Microsoft Azure, IBM SoftLayer, and Amazon Cloud. It is the cheapest among the cloud options available. 2) Private Cloud A private cloud is a pool of computing resources that hosts a single enterprise only. It is similar to public cloud computing in many ways but the difference is in the access of resources. A private cloud hosts the resources of a single customer, thus, it's more secure compared to a public cloud. It also offers better privacy since it is hosted on internal company servers using some virtualization technology. VMware is an example of a private cloud. 3) Hybrid Cloud A hybrid cloud is a combination of public and private clouds, and usually offers the best of both worlds. Sensitive data could be hosted within the private cloud while other resources are hosted in the public cloud. An organization could also revert to the public cloud to handle excessive traffic. 4) Developments...

Read more about author Over the years, cloud computing has turned from an innovative concept into a disruptive endeavor. Today, cloud computing is a This article briefly examines some of the most important events in the history of cloud computing and calls out four of the most important cloud trends developing today. A Brief History of Cloud Computing Although many think of cloud computing as a relatively recent concept, in some ways it can be traced back as far as the mid-1940s. Subsequent to Alan Turing’s 1930s work on early computing and the ultimately unprovable Entscheidungsproblem (the notion that a statement is universally provable with enough computing power), early general-purpose, Turing-complete mainframes, like the ENIAC, were introduced. This type of computing involved massive, shared, and expensive machines capable of performing computations mechanically, by manipulating components like vacuum tubes and relays. In those days, while not all programmers needed constant access to the machine, as they do today, many different people and teams would program and run the mainframe. To ensure that users had access when they needed it, “time-sharing” schedules were created. These schedules would evolve to enable users to access the mainframe from any connected stations or “dumb terminals” and use the computing power of the mainframe from a tethered screen. While this is very different from modern cloud computing, the basic premise is the same. Image Source: Pixabay A ...

"Over the last decade a new technology has begun to take hold in American business, one so new that its significance is still difficult to evaluate ... The new technology does not yet have a single established name. We shall call it information technology." Information technology has evolved and changed ever since. This article will explore that history and the meaning of IT. What is IT today? • create policies to ensure that IT systems run effectively and are aligned with an organization's strategic goals; • maintain networks and devices for maximum uptime; • automate processes to improve business efficiency; • research, implement and manage new technologies to accommodate changing business needs; and • maintain service levels, security and connectivity to ensure business continuity and longevity. In fact, today's modern hyper-connected data economy would collapse without information technology. The slow evolution of computers and computing technology Before the modern-day computer ever existed, there were precursors that helped people achieve complex tasks. The abacus is the earliest known calculating tool, in use since 2400 B.C.E. and still used in part of the world today. An abacus consists of rows of movable beads on a rod that represent numbers. But it wasn't until the 1800s that the idea of programming devices really came along. At this time the Jacquard loom was developed, enabling looms to produce fabrics with intricate woven patterns. This system used punched car...