Spatial Cloud Computing: A Practical Approach

Cloud computing is the delivery of numerous services over the Internet, such as data storage, servers, databases, networking, and software. You can save files to a distant database and retrieve them anytime you need them with the hybrid cloud storage.

 

In the twenty-first century, the geo-spatial sciences face four major information technology (IT) challenges: data intensity, computing intensity, concurrent access intensity, and spatiotemporal intensity. 

 

What is Cloud Computing?

 

It is a computing architecture that allows for ubiquitous, easy, and on-demand network access to a shared pool of configurable computing resources (e.g., servers, storage, applications, and services) that can be instantly supplied and released with minimal management effort or service provider involvement.

 

Data backup, disaster recovery, email, virtual desktops, software development and testing, big data analytics, and customer-facing web apps are just a few of the use cases that organizations of all types, sizes, and industries are embracing the cloud for. 

 

Google Cloud, for example, is a set of public cloud services provided by Google. 

 

(Related Blog:- Introduction to Distributed Cloud)

What is Spatial Cloud?

 

Spatial cloud computing is a cloud computing variety of innovation based on geospatial sciences and optimized utilizing spatiotemporal principles to allow geospatial scientific discoveries as well as cloud computing in a distributed computing environment.

 

It’s Important to also know about IaaS, Platform as a Service (PaaS), and Software as a Service (SaaS). 

 

IaaS (Infrastructure as a Service):- 

 

The most widely used cloud service, IaaS provides on-demand computing power, storage, networks, and other basic computing resources to cloud users, allowing them to deploy and execute any software. 

 

IaaS allows users to access, control, and manage a cloud server as if it were a local server.

 

 

PaaS (Platform as a Service):- 

 

PaaS is a cloud service that enables application creation and deployment on cloud infrastructure using a set of programming languages, libraries, services, and tools that the provider has defined as a solution. 

 

PaaS is a term that refers to a service that covers the full application development lifecycle, including coding, testing, deployment, runtime, hosting, and delivery.

 

 

SaaS (Software as a Service):-

 

The most widely utilized cloud service, SaaS provides end customers with a variety of sophisticated applications that are often provided via Web browser.

 

 

Concept Behind Spatial Cloud

 

Without a doubt, geospatial scientists would gain more from cloud computing than they would from present support for geospatial science research and applications, such as parallel computing technology or grid computing technology, which solely provide processing power. 

 

However, effectively utilizing cloud computing to support geospatial research communities remains a big problem, mostly due to the fact that geospatial applications are distinct from standard IT applications (e.g., accounting) and have unique cloud computing platform requirements. 

 

Geospatial science challenges, in particular, are characterized by time and geographical limitations and principles.

 

To better exploit and optimize cloud computing infrastructure and services for geospatial science applications, cloud computing platforms should take such spatial principles and limits into account. 

 

As a matter of fact, Yang et al. (2011a) defines spatial cloud computing as "the cloud computing paradigm that is driven by geospatial sciences and optimized by spatiotemporal principles for enabling geospatial science discoveries and cloud computing inside a distributed computing environment for enabling geospatial science discoveries and cloud computing inside a distributed computing environment."

 

(Related Blog:- What is Edge Computing? Working and Benefits)

 

Optimization:-

 

Spatial cloud computing improves the cloud data center selection, schedules computing jobs while minimizing delay and expense, and maximizes computing task performance. Several geographical and spatiotemporal patterns must be examined and combined in order to utilize the flexibility, scalability, and high-end processing capabilities afforded by cloud computing for a geospatial application: 

 

1. The  physical placement of computing resources 

2. Data distribution 

3. Dynamic user access at various locations and times

4. Application's research area 

 

Harnessing these spatiotemporal patterns is a critical strategy for making big geographical data applications perform successfully by considering location, time, computational capabilities, data, and user characteristics. 

 

A location-aware application, for example, beats non-location-aware applications by a factor of 3-11 in terms of performance.

 

Architecture:-

 

The design and development of a geographic cloud platform should examine three areas to handle the computing problem posed by geospatial science models, as well as the large data challenges posed by observations and model output. 

 

Computational and geographic functions that are independent of domain applications 

Application-level functions and interfaces that are directly accessible by users.

 

Step 1

 

Initially, the computing infrastructure can combine traditional high-performance cluster technology with scalable cloud resources that can be deployed from either a private cloud platform or a public cloud platform. 

 

The spatial cloud computing platform can scale up automatically to run scientific models and handle massive spatiotemporal data management, access, processing, analysis, and visualization for various domain science applications by leveraging cloud resources as the underlying computing infrastructure. 

 

Furthermore, the popularity of cloud computing has resulted in a plethora of cloud providers and cloud computing platforms, each with its own set of advantages and disadvantages.

Whereas all major public and private cloud resources can contribute to the creation of a large-scale, flexible, and dynamic computing pool, cloud platforms and solutions differ drastically, making cloud infrastructure selection and design a big problem. 

 

Every platform, for example, may use different IT technologies and have varying processing capacities, scalability, pricing regulations, security methods, dependability, degree of customization, usability, and geological distribution of cloud regions.

 

Step 2

 

Moreover, a crucial component of a spatial cloud computing platform should provide both computational and geospatial services to enable the integration of data, processing, and model resources inside a cloud-based cyberinfrastructure environment. 

 

The above feature is frequently referred to as spatial cloud computing middleware as it shields end users from the complexity of computation and data processing. 

 

Computing service includes features such as computing task scheduling, computing resource communication and coordination, accomplishing interoperability between local IT infrastructure and different clouds, cloud resource operation and manipulation, cloud security control, user authentication and authorization, and more to handle and leverage underlying multi-sourced computer resources.

 

Step 3

 

Lastly, a user-friendly spatial cloud gateway is frequently used to access the underlying processing power and services. 

 

This portal acts as a web-based spatial gateway for supporting various domain applications by using the underlying computing architecture and services, which are concealed from cloud users. 

 

Although the application level functions vary depending on the scientific problem (e.g., air quality, water), common features include data access, data visualization  model configuration and model-run tracking analysis, and data dissemination to aid model runs and scientific discovery, as well as results sharing among the geospatial science communities. 

 

While cloud users (i.e., consumers) can use geographical cloud portals to access cloud services, only local users and administrators can use the computing resource management interface, or command line interface, to access private physical servers directly.

 

 

What are the Spatial Cloud Service Models?

 

In relation to the three cloud services there are other several cloud services. Such as Data as a Service (DaaS), Model as a Service (MaaS), Geoprocessing as a Service (GaaS), and Workflow as a Service (WaaS), were specifically conceptualised and developed in geospatial science fields, and are essential to geospatial applications (WaaS).

 

• DaaS:- 

 

Data as a Service (DaaS) addresses challenges such as data discoverability, accessibility, utilizability, service quality, cost, and security. 

 

DaaS arose to support data storage, discovery, access, and use, as well as supply data and data processing on demand to end users without geographical or scalability limits, since data has become the enabling technology for many advancements. 

 

Also, DaaS allows data-oriented innovations.

 

• MaaS:-

 

 MaaS creates and distributes a variety of geospatial models as services that may be accessible via an online interactive interface. 

 

A MaaS prototype was created using a global climate change model to show how MaaS automates the procedures of building up a computing environment, configuring and running models, and managing model results.

 

• GaaS:- 

 

GaaS provides geospatial customers with scalable, on-demand, and cost-effective geoprocessing services, as well as addressing big data concerns in the geospatial science sector.  

 

Handling large data necessitates high-performance data processing techniques that allow information to be extracted from massive amounts of data.

 

• WaaS:-

 

 WaaS enables geoscientists to create a model or service workflow and submit it to the cloud for processing. On the fly, the process gathers the models required for a scientific application or activity. 

 

Previously, different researchers and scientists had to create a significant number of complex algorithms, models, and apps that were specialized to their individual study.

 

 

Conclusion

 

The "spatial computing" at the center of this image is the next phase in the physical and digital worlds' ongoing confluence. 

 

It performs all of the functions of virtual-reality and augmented-reality apps, including digitizing things that connect via the cloud, allowing sensors and motors to interact, and digitally representing the real environment. 

 

The system then combines these capabilities with high-fidelity spatial mapping to allow a computer "coordinator" to track and regulate object movements and interactions as a person navigates around the digital or physical world. 

 

In many areas of life, such as industry, health care, transportation, and the home, spatial computing will soon bring human-machine and machine-machine interactions to new levels of efficiency.