Cloud is not a technology but an operational model that gathers networked computing. Before, the cloud was used for removing the complexity of the connection between end-to-end data links. And now it applies to many types of server-side like any types of physical electronic devices (What Is Cloud Computing? 2019). The three major design principles and the fundamental architecture of cloud computing goes falls into SaaS, Paas, and Iaas. 1) Software as a Service (SaaS) is an on-demand service mainly for end users that are not required to install the application, but rather can access through website and interact with multiple users at the same time. Products like Google doc, Microsoft 365 are typical SaaS services that are cheap to work with and hold their computing resources managed by the vendor. The benefit is that there is no platform limitation. The users can manipulate the service from anywhere at any time, and simultaneously cooperate with others without technical barriers because the vendor takes care of it. The downside is that the most convenient functioned found in SaaS service are highly dependent on the usage of the internet. That saying, without the internet, Google Docs is like any still notes. 2) Platform as a Service (Paas) benefits the developers by providing a programmable and operational system where they can build and run their programs without worrying about the fundamental framework. Users are responsible for managing their data and app resources, the vendors will take care of the rest. 3) Infrastructure as a Service (Iaas) is designed for administrators where very editable but also complex to operate. Data storage, virtualization, servers & networking are all vendors’ responsibilities (Cloud Computing Services Models – IaaS PaaS SaaS Explained, 2017). The layers of how the cloud works: (Cloud Service Provider)-(Router)- (Network Cloud)-(Router)- (Cloud user/Host).

It is funny how some authors think the existence of a supercomputer or cloud computer makes us degenerated to the mainstream age. The invention of individual electric devices is a milestone in gaining back our power of self-control over our private data. But ever since the popularization of applying cloud computing, our data goes back to centralism (Frantsvog et al., 2021). As I was writing the topic about cloud computing monopoly, there is no better example than Google. Thinking about how many fields does Google products and services cover, and how many lawsuits on antitrust cases they have to face each year. I won’t list them all, but I will post a screenshot to make my point, and also remember that’s not all there is to it (Browse All of Google’s products & Services – Google, n.d.).

Another funny thing is, when I “googled” ‘Google cloud computing monopoly’, barely anything showed up, not even in google scholar. And as you may notice, Google is now launching its payment method and e-shopping market. All the data is collected from us before has finally been utilized. By analyzing our frequently visited locations, routings, our payment history as well as our purchasing habit and searching history, also our stored data/personal information like the password for each website, google probably knows us better than ourselves. The convenience comes along with private info intruding and leaking. There have been cases where google users lost access to their own data. By taking over 90% internet search market, Google provided the best free services, and use this free service to collect, analyze and trade data to make more profit (Vellante, 2020). Another downside of cloud computing monopoly is that government also faces difficulty accessing these data from the third party due to the current CNDA regulation (Snapp, 2021). And who gets to access these data becomes the biggest concern. And the non-transparency feature makes this open data a Blackbox where no one knows what exactly happened inside (Moss, 2020).  

References

Browse All of Google’s Products & Services – Google. (n.d.). Google. https://about.google/intl/en_us/products/#all-products

Cloud Computing Services Models – IaaS PaaS SaaS Explained. (2017, April 6). [Video]. YouTube. https://www.youtube.com/watch?v=36zducUX16w

Frantsvog, D., Seymour, T., & John, F. (2021). View of Cloud Computing. The Clute Institute. https://clutejournals.com/index.php/IJMIS/article/view/7308/7376

Moss, S. (2020, October 7). House reports on tech monopolies: Here’s what it says about Amazon Web Services. DCD. https://www.datacenterdynamics.com/en/analysis/heres-what-house-tech-antitrust-report-says-about-amazon-web-services/

Snapp, S. (2021, April 7). What To Do About the Extreme Monopoly Implications of Hyperscale Public Cloud Providers. Brightwork Research & Analysis. https://www.brightworkresearch.com/what-to-do-about-the-extreme-monopoly-implications-of-hyperscale-public-cloud-providers/

Vellante, D. (2020, November 3). Google’s antitrust play: Get your head out of your ads – and double down on cloud and edge. SiliconANGLE. https://siliconangle.com/2020/10/24/googles-antitrust-play-get-head-ads-double-cloud-edge/

What is Cloud Computing? (2019, December 1). [Video]. YouTube. https://www.youtube.com/watch?v=dH0yz-Osy54

Wikipedia contributors. (2021, May 7). Cloud computing. Wikipedia. https://en.wikipedia.org/wiki/Cloud_computing

We have previously discussed the socio-ethical impact, biases of algorithms and AI yet focusing on the companies that control it all, takes a different turn on the overall implications that cloud computing can potentially have assuming that it converted into this “unified” architecture controlled by the “big four”; Google, IBM, Microsoft, Amazon. As de Bruin and Floridi (2017) explain, Generation X and Millennials seem to “care less about” where their private information is saved or who controls it but rather care more about the efficiency with which they can use the that information, for example, a photo and send it to friends, alter it, post it or upload it, share between devices, etc. I think overall there has been a larger impact of cloud computing in our daily lives exactly because it is so easy and efficient and allows us to have all our information, documents, material, etc. stored and located in one place and accessible from multiple locations. We definitely rely on the efficiency of it all, more than what we actually question the overall concept of what is happening to our material when we do upload/save them on “the cloud”. However, there is quite a difference when the responsibility is split up among more and smaller sized companies and therefore you also get more services and products to choose from that could possibly represent your needs or preferences better. 

AWS defines cloud computing as: “the on demand delivery of IT resources via the internet” where you can access any tech services on an as-needed basis, you can use it for data backup, disaster recovery, email services, sharing virtual desktops, big data analytics, customer-facing web applications. It can also be used for personalized treatment of patents, fraud detection for finance companies, provide online games for millions of people/players around the world and more. Basically, your private or work information is somewhere on “the cloud” and even though it might seem private on your end, as the user who could similarly be accessing let’s say a notebook or a vault, I doubt most of us read the fine prints, where more details of who has ownership and what can happen to the material and data is disclosed and explained. “Cloud computing suits the interests and values of those who adopt a deflated view of the value of ownership and an inflated view of freedom” (de Bruin & Floridi, 2017, 22). In Cloud Computing (2016), Ruparelia mentions the three things that most us look for in these services and are usually what make us choose the company/product/service we will go with whether for personal use or business purposes. Integrity and reputation, you want to be able to rely on the product, the company and the service. Which means most of us will only trust companies who not only we know because they are famous but because many others also use. It is more likely that we will trust a brand we have heard of and know can be well supported. Another important factor that goes into choosing your preferred cloud provider would be the benefits you get from cloud computing such as efficiency and promptness. Having the ability to access anything you want from anywhere in the world at any time while knowing that it is in “good hands”, safe and reliable but also in a sense never disappoints. Forgetting your hard-drive or usb stick for example can lead to major issues especially if you desperately need your data in that moment, but with cloud computing that is not something to worry about since you know whatever is on there will always be on there. Finally, pricing of each product or service plays a crucial role in the selection of a cloud computing company that would supposedly match its pricing to what it has to offer in terms of space, accessibility, security, usage, organization and more. 

Moving cloud computing into a “unified architecture” provided only by Amazon Web Services, Apple, Microsoft and IBM would be imaging a different style of data storage, accessibility, manipulation and distribution of content. Ruparelia’s (2016) given characteristics of what we look for in our cloud computing services definitely become more conscience and give you less choices to choose from. Of course this also implies the power of everything we refer to as “the cloud” to be kept among the four companies giving them more flexibility to control the data and information that is uploaded on them? What happens to those fine prints on the terms and services agreements? Where does the date ownership go or rather who does the data really belong to? What about pricing? Would it become more expensive since choices are more limited and we won’t really have more options or would they actually be more beneficial and “get-what-you-payed-for” situation? Does having all of the words cloud split among only four companies and “kept in one place”  make it riskier and more exposed to outside threats? Or does it increase security since the “big four” could potentially work together to provide a “united front” again outside threat or even cultivate healthy competition amongst them therefore creating stronger systems and walls. Floridi and de Bruin (2015) discuss the power of “interfluency” when it comes to ethically effective communication among companies but also between companies and their customers. If the big four hosting companies have the same information or share a large part of it, they should therefore  by able to “provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership” (22). The two authors also discuss  the possibility of a stricter government regulation and overall involvement in cloud computing and what that could mean in terms of restriction of the use of cloud computing, regulating what we can and cannot upload, share, distribute, etc. and what its future will look like. 

Resources

• AWS, What is Cloud Computing?
• Boudewijn de Bruin and Luciano Floridi, “The Ethics of Cloud Computing,” Science and Engineering Ethics 23, no. 1 (February 1, 2017): 21–39.
• Cloud Computing Services Models – IaaS PaaS SaaS Explained (EcoCourse)
• Derrick Roundtree and Ileana Castrillo. The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice. Amsterdam; Boston: Syngress / Elsevier, 2014.
• Nayan B. Ruparelia. (2016) Cloud Computing. Cambridge, MA: MIT Press. 

Typically, consumers in the end would stand to gain with larger cloud networks, which means more computing power for less cost. However, there remains two possible disadvantages for consumers when monopolies permeate into the cloud service industry. One disadvantage is security, and the second is unfair pricing resulting from unfair competition. A detailed overview of the advantages of cloud computing will first be discussed, followed by the disadvantages in a monopolistic environment. 

Cloud computing is made possible by mostly one feature, virtualization. (Ruparelia 5) Although, one important question to answer regarding the benefits of cloud computing to users is, what came before cloud computing and virtualization? Before cloud computing, there was the traditional server structure, which consisted of the hardware, software, OS, and the applications all in one location. (Ukessays 2015) The downsides of the traditional server structure was basically the converse of what makes up a cloud service, this being lack of elasticity, high maintenance costs, and also a lack of continuity (if one aspect of the server breaks down the rest follows). (Ukessays 2015)

  In contrast, a virtual server separates the software from the hardware, allowing seamless maintenance across multiple hosts, and consists of multiple servers whether these be data or email servers etc. The ability to scale down or up in a cloud service is also a key aspect, whereas in the traditional server structure, a maintainer would need to buy more physical hardware in the event of an overtaxed server, in the cloud, hosts are migrated to the more heavily trafficked servers. (Ruparelia 18) This is what makes cloud computing unique, and why the consumer benefits from larger networks. 

As for the disadvantages in the case of monopolistic competition, the security aspect is perhaps the most substantial concern. The malware threat to cloud services is unique in its scale, as cloud services are also unique in their scale, and this is referred to as “excessive access scope.” (Zalkind, 2016) Excessive access scope is when applications require credential authentication from three parties in a cloud network, these being the user, a third party entity, and the corporate environment. (Zalkind, 2016) This credential access gives the third party entity (in this case an application) access to the system even while the user is not actively using it, and through one user, the whole system is compromised. Some applications are built to be malware, while other legitimate applications are hijacked by malicious software, creating more avenues for hackers to gain access to a cloud system.  

As for the consideration of price gouging and unfair competition, a combination of both economic and technical understanding is required. Cloud services operate on economies of scale, and AWS is currently the largest cloud service provider by a substantial margin. (Gartner Magic Quadrant for Cloud, 2021.) Cloud service providers tend to offer multiple qualities of service based on computational time usage. (Kilcioglu & Rao, 2016, 1) As a result of most service providers utilizing the same hardware, the only way to adjust profit margins is by lowering the quality of service rather than charging higher prices for better performance, which is a potential setback for consumers. (Kilcioglu & Rao, 2016, 2) Additionally, in some regions, there is only one cloud provider who charges significant fees for switching, this allows for unhealthy monopolistic tendencies and creates the “locked in” customer dynamic. (Kilcioglu & Rao, 2016, 2)

References

Cloud services is not a winner-take-all market and enterprises should applaud robust competition. (2019, July 31). Diginomica. http://diginomica.com/cloud-services-not-winner-take-all-market-and-enterprises-should-applaud-robust-competition

Gartner magic quadrant for cloud: Evaluating the top six iaas providers. (n.d.). CloudBolt Software. Retrieved April 5, 2021, from https://www.cloudbolt.io/gartner-magic-quadrant-cloud-evaluating-top-six-iaas-providers/

Kilcioglu, C., & Rao, J. M. (2016). Competition on price and quality in cloud computing. Proceedings of the 25th International Conference on World Wide Web, 1123–1132. https://doi.org/10.1145/2872427.2883043

Life before cloud computing information technology essay. (2015, January 1). UKEssays.Com. https://www.ukessays.com/essays/information-technology/life-before-cloud-computing-information-technology-essay.php

Ruparelia, N. (2016). Cloud computing. The MIT Press.

Zalkind, R. (n.d.). The cloud malware threat | network computing. Retrieved April 5, 2021, from https://www.networkcomputing.com/network-security/cloud-malware-threat

“Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources” (NIST). There are leading companies- Amazon, IBM, Microsoft, and Google- providing Cloud computing services- SaaS, Paas, and IaaS. It’s really a black box for people and users without learning notions and concepts of them. When I am reading, the second writing topic reminds me of the closed ecosystem of Apple. And I think understanding the subject by comparing it with the case that I’m familiar with is a great method for me to de-blackbox it. There are millions of customers using the unified architecture- IOS, including me. For pros, it’s highly effective and convenient when all your electronic devices are Apple products. All products are highly compatible with each other, based on iCloud, Airdrop, Sidecar, etc. For example, you can store some information on your Mac and access it on other Apple devices via iCloud; the iPad is an extension of iPhone and Mac, which gives you access to all the same information. Moreover, thanks to AirDrop, iMessage, and FaceTime on macOS, functions like unlocking a Mac laptop with an Apple Watch or auto-pairing and locating missing AirPods are all available via the Apple ecosystem. For cons, since the technologies change fast, the cost will be prohibitive when one intends to move into another ecosystem for both hardware and software.

It will work in similar ways if a company runs its business on a unifying architecture provided by a specific company like Microsoft. Microsoft provides all SaaS, PaaS, and IaaS through Microsoft Azure. If a company chooses Microsoft, for SaaS, it can access all modules or applications they need through one account. Employees could communicate through Outlook, have meetings on Zoom, and work on Office 365. Through a supplier like Microsoft, the company could improve efficiency by mobilizing the workforce, and its employees can access app data everywhere. For PaaS, Microsoft offers a platform to let developers customize cloud-based applications using built-in software components and to “allow organizations to analyze and mine their data, finding insights and patterns and predicting outcomes to improve forecasting, product design decisions, investment returns, and other business decisions” (What is PaaS? Microsoft). It will improve the firm’s efficiency by cutting coding time and adding development capabilities without adding staff. For IaaS, the corporation can customize its own servers and infrastructure regarding its demands on storage, security, and data plant. It will enhance the flexibilities for the company to run its business. The cons are also similar; if a company buys all services it needs on Microsoft, the future change would be hard and expensive to achieve. Besides, the risks will be high to cooperate with a single supplier.

Reference

Cloud Computing – NIST. Retrieved April 2, 2021, fromhttps://csrc.nist.gov/projects/cloud-computing

Ruparelia, N. (2016). Cloud computing. The MIT Press.

What Is PaaS? – Microsoft Azure. Retrieved April 3, 2021, from https://azure.microsoft.com/en-us/overview/what-is-paas/#:~:text=Platform%20as%20a%20service%20(PaaS,%2C%20cloud%2Denabled%20enterprise%20applications.

In thinking about some of the consequences that could result from the convergence of the technologies on one overall “unifying” architecture from one of the “big four” companies (Google, AWS, IBM, Microsoft), my immediate thought is to consider the dangers. First, cloud computing has seen plenty of controversy in terms of data security: “2010, for instance, witnessed a huge cyber attack on the popular cloud email services of Gmail, and the sudden discontinuation of cloud services to WikiLeaks by Amazon. There followed the 2013 NSA spying scandal, the 2014 nude photo iCloud hack and the Sony hack, with hackers increasingly turning to the cloud.” If all users of an architecture system had to use technology provided by only one company, data breaches could much more widescale than they already are, and effect more people than they would if people did not have various companies to choose to use services from.

Not only would we need to worry about security in terms of data breaches, but also in terms of what we as users “own” and what the company providing the services owns: “cloud computing suits the interests and values of those who adopt a deflated view of the value of ownership and an inflated view of freedom” (De Bruin 2010). In other words,  cloud computing is designed for people “who care less about where, for instance, a certain photograph is stored and who owns it and care more about having the opportunity and freedom to do things with it.” This can be extremely dangerous, especially if we do not look into the nitty-gritty of who owns our information and where it is stored because we can be giving our work, data, and reliance on a company we may not fully trust. If we disagree with this company’s regulations for storing or accessing our information but need cloud services, we may be at a loss and left with the choice to succumb to regulations we do not align with or not having the technology to meet our specific hopes/goals.

As with all monopolies, payment becomes a huge concern. If one company controls the market, what stops them from charging any prices they choose to store our data on the cloud? Again, users are left with the hard decision to conform to absurd prices or choosing to forgo needed technology. Especially considering that many companies intentionally blackbox cloud computing services, many users will not be able to conceptualize fair pricing and could be taking advantage of users for more than just a hotdog. 

Lastly, what concerns me about one provider is what happens when we experience hiccups or outages in our systems: “ To minimize the risk of interrupted service due to power outages, datacentres are located near power plants and data are stored on various different physical locations—the greater the number of locations where your data are stored, the more you pay…even then, things may go wrong. Cloud services may face problems as a result of which they become temporarily unavailable. For the numerous companies dependent on cloud services, this means interruption of their websites, their customer services and/or their sales administrations.” If we all rely on the same services, does it mean a more substantial piece of the internet is down than what would be if we had options? And again, more issues arise with the idea of services being down and pricing: “small start-up companies are typically affected most: cloud companies require their customers to pay more to store data in more datacentres to diminish the risk, but smaller companies are less likely to be able to afford this.” Too much control for one company is never a good thing, and can have serious financial, security, and independence concerns for users.

de Bruin, B. (2016). The Ethics of Cloud Computing, Science and Engineering Ethics, volume 23, 21–39.

de Bruin, B. (2010). The liberal value of privacy. Law and Philosophy, 29(5), 505–534.

Cloud computing is the delivery of computer resources like data (cloud) storage and computing power on-demand, without direct management or deep knowledge of the user, and with pay, as we go (Wikipedia, 2021). Cloud removes the need for owning and maintaining physical data centres (Amazon Web Services, 2019).

Several companies are using the cloud for many use cases like backup, information recovery, emails, virtual desktops, big data analysis and software development (Amazon Web Services, 2019).

Cloud Architecture based on the convergence with AI/ML

Convergence between AI and data is a reality, not just at the macro level but also within specific industries and technologies. Cloud computing simple architecture consists of four basic parts: the cloud service, the cloud infrastructure, the cloud platform, and the cloud storage (Databases). AI/ML systems manipulate large amounts of data, so they need infrastructures that can scale based on the computation needs. This is where cloud computes infrastructure that can be scaled horizontally and on-demand becomes important (Roe, 2019). Building AI/ML systems need large volumes of training, validation and test data. So, big data analysis tools (Hadoop, Spark, etc.) are required in order to handle these types of volumes.

Another convergence is the Internet of Things IoT in which there are equipment generating data through hundreds of connected sensors, and the enterprise is deciding on maintenance. We need to store, transport, process (using AI/ML) and decide whether the machine needs maintenance. These processes require merging different technologies such as cloud, AI/ML and big data processing to work together to deliver the final result (Jarrahi, 2018).

Virtual assistance, for example, needs a cloud system so that they can connect multiple systems and data sources through the cloud wherever they need. Cloud services that have been created by service providers such as Amazon, Microsoft, Google, and Rackspace (Dong, Xiong, Castañe, & Morrison, 2018), are divided into three layers: the infrastructure layer, the cloud management layer and the service delivery layer. 

Cloud infrastructure design balances requirements ensuring data centre scalability, maintaining server fault tolerance and minimizing costs. Traditional data centre infrastructure stands on a hierarchical structure consisting of a three-tier design, including the Access Layer, the Aggregation Layer, and the Core Layer. The access layer connects servers residing in the same rack. In contrast, the aggregation layer is a multi-purpose system connecting the access and core layers to keep various communication domains separated. The last infrastructure layer is the core layer which provides high-speed, scalable and reliable communications among the entire data centre (Dong, Xiong, Castañe, & Morrison, 2018).

Cloud management platforms follow the same design, but their implementations differ significantly. Cloud management consists of three main components: the security part (privacy, authentication and data protection), the management services (monitoring, computational management, networking resource management, storage resource management), and the user interface part. 

The last layer in the cloud computing system is the service delivery layer. There are three basic service delivery models (Dong, Xiong, Castañe, & Morrison, 2018) (Ecourse Review, 2017) known as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). IaaS gives end-users the ability to tangible infrastructures (physical servers, storage databases, network equipment). IaaS provides powerful flexibility to the end-users so that they can access their virtual machines. IaaS targets end-users who are interested in building Information Technology infrastructure. PaaS, on the other hand, reduces the configuration complexity and operational costs by providing pre-configured platforms and offer ready-to-use platforms to end-users such as operating systems (Linux, Windows), workflow engines (Apache Director Engine), Messaging frameworks (RabbitMQ, ZeroMQ), programming-language execution environment and Web application servers (e.g., Apache Tomcat, Oracle Glassfish Red Hat JBoss). SaaS gives users access to application software and databases. It is known as “on-demand software” and priced on a pay-per-use basis. SaaS providers install and operate software in the cloud so that users access them from cloud clients (Wikipedia, 2021) (Ecourse Review, 2017) (Rountree, Castrillo, & Hai Jiang, 2014). Figure1 includes the cloud design layers.

Fig.1 Cloud system layers

Another service called Mobile “backend” as a service (MBaaS) provides web application and mobile application developers with a flexible approach to link their applications to the cloud computing servers and storage. This service includes user management, integration with social network services and other push notifications (Roe, 2019).

ReferencesWeek 10

Amazon Web Services. (1 21, 2019). What is Cloud Computing? Retrieved from: Youtube: https://www.youtube.com/watch?v=dH0yz-Osy54

Dong, D., Xiong, H.. Castañe, G. G. & Morrison. J.P. (2018) Cloud Architectures and Management Approaches. Palgrave Studies in Digital Business & Enabling Technologies (p 31-61).

David Roe. (8 8, 2019). Why Big Data, IoT, AI and Cloud Are Converging in the Enterprise.  Retrieved from: CMS Wire: https://www.cmswire.com/digital-workplace/why-big-data-iot-ai-and-cloud-are-converging-in-the-enterprise/

Rountree, D., Castrillo, I.  & Jiang. H. (2014) The Basics of Cloud Computing. Waltham- USA: Elsevier.

Ecourse Review. (6 4, 2017). Cloud Computing Services Models – IaaS PaaS SaaS Explained. Retrieved from:Youtube: https://www.youtube.com/watch?v=36zducUX16w

Jarrahi, M.H. (2018) Artificial Intelligence and the Future of Work. Business Horizons، 4.

Wikipedia. (, 2021). Cloud_computing. Retrieved from: Wikipedia: https://en.wikipedia.org/wiki/Cloud_computing

In our daily life, cloud services are everywhere but not obvious. In many situations, we use products or services which use cloud computing but we just contribute them to the Internet. Email services provided by different company, translation services and Virtual Assistants are all the examples. We have the network, so the products can work well. (In fact, the networking is only the base or tool of one of the five characteristics of cloud, ubiquitous access. But outside the black box, it looks like the network finishes all the things.) While on the other hand, in terms of the business, many company claim that they use cloud computing to support their work to show that they are the high-tech company. The separation of daily experience and business claiming about the cloud computing may label the cloud as a high-tech matter, irrelevant to daily life and exacerbate the black box of cloud service and the cloud computing. What is the cloud or cloud computing? According to Rountree & Castrillo, “cloud is actually a service or group of services”. There are 3 basic cloud service models, SaaS, PaaS and IaaS which “can be viewed in terms of increased levels of abstraction”(Ruparelia, 2016). Take an example of Amazon translate.

Amazon translate is one of the service of the Amazon Web Services (AWS). AWS can provide different service models for users based on user needs. For example, if an individual want to translate a text from English to Chinese, he can just use the console to translate, select the language and input the text, then an real-time translation results output. This is an SaaS example. But when comes to the business, it would be different. For example, the Hotels.com needs to translate customer reviews in 41 languages so that the users can understand the reviews and have more information about the hotels (Amazon Translate Customers, n.d.). In this case, Hotel.com should not only input the review data to the cloud but also manage its website and how to collect its data and show the results to the users, like write the CSS and HTML to create the Web page, a function of code to calls Amazon Translate to work and so on. This is an PaaS example. In fact, the Amazon Translate provide limited code language to use it, like the JAVA and python (in fact, translate a web page requires AWS SDK for java). And it specifies how to input data and how to upload data. So for the translate function, AWS only provide the PaaS or higher abstract level of cloud service model. But AWS itself provide the IaaS. As IaaS, It only provides computing infrastructure and some basic function like data storage, virtualization, networking and resources above vendors, Users can handle the data, middleware and the system.

As mentioned before, cloud is a group of services. For the translate itself, the input data will just calls the Amazon Translate model, then the encoder of the model reads the word of text one by one and construct a semantic representation. then the decoder use the representation to generate the translation also one word at a time (What Is Amazon Translate? – Amazon Translate, n.d.). While as a cloud services, we usually use many services of the cloud instead of one. AWS encapsulates many cells (services). Translate is one of them. Most of the time, we will use different cells to get the final output. For example, use Amazon Polly to read the Amazon Translate results. In this case, before we get the translate results, inside the cloud, the data will also input to the Amazon Polly model and return results to the users.

In my opinion, the cloud services is an AI version of professionals do professional things. like the video How AWS Is Changing Businesses Using Artificial Intelligence said “AI and machine learning is hard to implement alone and can be a complex undertaking”. Take Hotel.com as an example, it’s difficult for them to train a AI on its own to translate the reviews in different languages, let alone it needs 41 languages. But with AWS, all the Hotel.com need is pack the data and input it through the APIs provided by AWS.

In short, with the cloud services, companies do not have to store, manage and process all the data on the local server, but can use to outsource all these to the cloud platform, thereby saving costs and improving work efficiency. The transformation of this model is like changing from building a house to building blocks.

Reference

Amazon Translate Customers. (n.d.). Amazon Web Services, Inc. Retrieved April 5, 2021, from https://aws.amazon.com/translate/customers/

edureka! (2018, July 13). Cloud Computing Service Models |  IaaS PaaS SaaS Explained | Cloud Masters Program | Edureka. https://www.youtube.com/watch?v=n7B4icXvs74

Rountree, D., & Castrillo, I. (2014). The basics of cloud computing: Understanding the fundamentals of cloud computing in theory and practice. Elsevier/Syngress.

Ruparelia, N. (2016). Cloud computing. The MIT Press.

What Is Amazon Translate? – Amazon Translate. (n.d.). Retrieved April 3, 2021, from https://docs.aws.amazon.com/translate/latest/dg/what-is.html

Question

What’s the difference between cloud services and cloud computing? Take AWS as an example, are the products provided by AWS cloud services? and are the services based on the cloud computing?

What the readings bring up is both this great unifying force of technology and its reach into everything we do. I wanted to highlight this effect specifically for Google as it’s a prime example of a company making integration easy but at the same time centralizing a lot of work around the Google platform.

It makes sense for Google to be a cloud computing company as its primary services require online services. Still, it is a company that continues to integrate into different online markets as a consequence of its massive infrastructure and first-mover advantage.

This makes it easy to access things such as files easily, using Google drive, access information quickly, or even run complex ML/AI models for businesses. There a couple concerns to be had when great monoliths are created. First, unless the company is constantly incentivized to innovate, the economies of scale effect are achieved, which reduces cost (which always wonderful) but also stifles innovation which may be more incremental as the cost/benefit will never reach the same level as these large tech companies. This is seen more the effects of Amazon and Walmart, but as Google is able to out-compete smaller companies or easily buy them out, this causing services to continue to feed into these growing tech giants. This isn’t all bad as some other companies now may be able to scale up as a function of the lowered prices and added integrated services, leaving companies better able to perform and reallocate resources elsewhere, which may be more beneficial.

This also places a large emphasis on the ability for one company to be financially successful and secure. For success, imagine if Google declared bankruptcy tomorrow; how would the economy be affected by this news. Now, this may be an unfair scenario as Google is one of the most successful companies in the world but imagine how many services would be at risk of going offline. How many years would it take for things to return to the levels before the news? Would the US government have to bail them out? These are all major concerns for these businesses as they are integrated into so much of our digital infrastructure. This also puts an emphasis on security since their is so much private and critical data handled by Google, the moment they have a data breach (like Facebook just did), the amount of information that is now out in the open would be astronomical. This puts a huge amount of pressure on companies to do things correctly the first time and constantly be vigilant to outside threats. Both of these are good as Google is a stable company with a very secure infrastructure. Still, the more Google does and the more integrated into Google services, the more important it is for Google to continue to be successful.

The final thing I want to mention is the question of efficiency. As companies grow to scale, their ability to take care of work also increases per employee. So one employee working for a company that uses Google’s services may be able to do the job of 1.2 employees elsewhere. This is great because people can do more and would open it up for employees to do other work but the major problem being that companies looking to reduce costs will not need to continue to recruit personnel. This is one of the existential crises with the rise of these integrative cloud services and its effect on productivity that the economy is not creating enough jobs to replace the jobs destroyed by the rise in productivity and ML/AI. With so much integration with Google, less needs to be done, which saves hours of time and costs but at the cost of a different kind.

Though the topics I brought up tend to bend to the more negative, it’s only because these the major questions we need to consider before jumping headlong into total integration for these services. Do the benefits outway the cost? What do you do after creating a growing tech Colossus?

When it comes to cloud computing, most of the readings for this week refer to the fact that there is an uncertainty in the definition of this term. This uncertainty is intentional as Professor Irvine mentioned in his presentation Introduction: Topics and Key Concept of the Course. Cloud is based on an old engineering metaphor and means “a blackbox of connections in a network.” Our readings for this week indicate that the National Institute of Standards and Technology (NIST) defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.” When cloud computing is combined with AI/ML and data systems, the outcome of the three computing trends will not only be a gigantic network that is able to learn, improve and store enormous amounts of data but also a cost effective and environmentally friendly one. If we think of the way these three trends work in combination, it seems to be a complex mechanism. In this assignment, I will try to deblackbox how AI/ML and Data systems are implemented in the Cloud architecture by revealing the key design principles and main architecture of cloud computing system and list some points of its convergence with AI/ML and data systems. 

In his book, Cloud Computing, Ruparelia said that the cloud computing model “promotes availability and is composed of the following:

1. Five essential characteristics (ubiquitous access, on demand availability, pooling of resources, rapid elasticity and measured service usage).
2. Three service models (Infrastructure as a service; IaaS, platform as a service; PaaS and software as a service; SaaS) 
3. Four deployment models (public cloud, private cloud, community cloud and hybrid cloud).

This structure of characteristics, deployments and services is what makes Cloud computing a beneficial network, as it provides agility, elasticity, cost saving and fast global deployment. Cloud computing relies on two basic virtualization technologies; server virtualization and application virtualization. This virtualization enables everything we can do in computing to be virtual and scalable. 

At first glance, AI/ML, data systems and cloud computing look like working separately but in fact, they are proactively linked to each other. While AI/ML and data systems work together in an inseparable way, the vast amount of rich output data needs the scalability and extensibility offered by cloud computing, in the shape of cheap extensible storage memories. On the other hand, blending AI/ML solutions as a service with cloud computing, improves the already existing cloud solutions and takes it to another level of efficiency. This unique combination of the three computing trends encourages organizations of every type, size, and industry to shift to cloud computing for a wide variety of use cases. This is due to fact that this combination “offers huge advantages in terms of installation, configuration, updating, compatibility, costs and computational power.” The best example, I can think of, to demonstrate the convergence between AI/ML, data systems and cloud computing is Amazon Web Services (AWS). This tool is currently the leading platform in the world (according to AWS website). AWS’s ML service provides the broadest and deepest set of machine learning services in one cloud platform. AWS enables data scientists and developers to “create faster solutions and add intelligence to applications without needing ML expertise,” as the platform facilitates using pre-trained data through AI services to many applications such as creating more intelligent contact centers, improving demand forecasting, detecting fraud, personalizing consumer experience and more.” The following diagram illustrates how AWS’s machine learning is used to build, train, and deploy models faster with less effort and at a lower cost.

With the increasing number of cloud computing platforms users in the last few years, a number of risks surfaced. These risks hold some users back from adopting Cloud computing service. The risks include but are not limited to ambiguity of what cloud is, concerns over maturity to meet organization’s needs, security issues caused by lack of direct control over systems and data, and corporate policies permit moving to the cloud, and flexibility in choosing a suitable provider. Given the fact that there are“big four” cloud computing providers, the question, raised by Professor Irvine here, is what are the consequences of merging these big four in one provider? An answer to such an important question requires a separate study. However, I can say that there will be a maximization in both advantages and disadvantages of Cloud computing. The four bodies will grow into one incredible network that is able to gain access to massive economies of scale. On the other hand, this gigantic network will be monopolized by one provider that will control the access of millions if not billions of global users to services by one provider.

References

Amazon Web Services (AWS) [main site]: browse services and Machine Learning products.

AWS Machine Learning.

Boudewijn de Bruin and Luciano Floridi, “The Ethics of Cloud Computing,” Science and Engineering Ethics 23, no. 1 (February 1, 2017).

Derrick Roundtree and Ileana Castrillo. The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice.

Nayan B. Ruparelia, Cloud Computing (Cambridge, MA: MIT Press, 2016).

Professor Martin Irvine Irvine, Introduction: Topics and Key Concepts of the Course.

To understand how AI/ML and data systems are implemented in cloud architecture we first have to discuss what the “Cloud” is. Cloud computing defined by the National Institute of Standards and Technology is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models. In brief the five essential characteristics are On-demand self-service, Broad network access, Resource pooling, Rapid elasticity, and Measured service. The three service models are Information as a Service, Platform as a Service, and Software as a Service. Finally, the four deployment models are public, private, community, hybrid. The readings provided more detail specifically Rountree’s Basics of Cloud Computing regarding the basic definitions of cloud followed by more in-depth detail of the abstractions (service models) and deployment models in Ruparelia’s Cloud Computing. For me though I understand cloud as the ability to manipulate data with on-demand availability of computer system resources from hosting companies (Amazon, Google, IBM, Microsoft). Cloud takes away the need for physical infrastructure by virtualizing everything from physical hardware to applications.  

Now how that applies to AI/ML is that the main questions so let’s now go to IBM’s Virtual Assistant Watson. Most of the case studies were based on business interactions and this virtual assistant was built to help companies “provide customers with fast consistent and accurate answers across any application, device or channel.” I took this to mean a business version of Apple’s Siri working for companies customer service operations. IBM states that Watson utilizes what IBM calls AutoML with “meta-learning” techniques, what I conjure as a form of blackboxing. I think Watson is another neural network that uses the similar techniques in the virtual assistant’s discussion in week 8, in which virtual assistants receive an input and run that input through various hidden layers in a neural network to produce an output. However, Watson does this in a form of conversational user interfaces i.e. advance chatboxes. IBM argues that they are the leader in this technology for companies due to advance in their natural language processing capabilities and new natural language understanding. Since all my information from them is from their own publishing I would argue that it may be bias.

Anyway, how this combines with cloud computing is that businesses can pay for hosting companies computer services like Watson to collect, organize, and analyze their data and provide appropriate responses without investing in their own physical and virtual resources to do the same. This thereby produces a new source of efficiency and innovation. Companies like Siemens have used this tool and created CARL an HR interactive chatbot for employees. CARL which stands for Cognitive Assistant for Interactive User Relationship and Continuous Learning runs on IBM Cloud and is powered by IBM Watson. This is a good way of viewing the interaction between Cloud and AI/ML. I like to look at it in the sense the Watson provided the adaptability behind the process i.e. algorithms, NLP, and AI/ML to answer users inquiries while the cloud provided the scalability to accommodate users, languages and topics through IBM’s vast databases, storage centers, and computation services.

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Now the ethics behind this are interesting. As opposed to other emerging technology that can be used for nefarious reasons like AI/ML, cloud computing to me is just an extension of computing services on a different platform. So, the risk I see is in the inherent design of the system due to its reliance on the internet namely the vulnerabilities to attacks and security and privacy. Regarding vulnerabilities to attacks, I mostly agree with Floridi’s argument in Ethics of Cloud Computing we have to look more at the users and impose proscriptive measures preventing uses of certain businesses with private and personal information to use these systems, for now. I believe that companies should continue to advance their software and find resolutions to the inherent vulnerabilities in cloud computing, an until then certain businesses should avoid using cloud computing. Regarding security and privacy, if only four companies own and operate cloud services which are slowly becoming the go to “unifying” architecture I instantly think of security and privacy violations of user’s data. In this sense I disagree with Floridi and argue that something beyond technological neutral regulations should be thought of that can both limit big tech’s monopoly of this service while also sustaining innovation. There is a middle ground we just need more dialogue and de-blackboxing these concepts for the general public.