Software Architecture Interview Questions

What is the difference between latency and throughput?

latency

Latency is the time required to perform some action or to produce some result. Latency is measured in units of time ie hours, minutes, seconds, nanoseconds or clock periods.

Bandwidth is just one element of what a person perceives as the speed of a network. Latency is another element that contributes to network speed. The term latency refers to any of several kinds of delays typically incurred in the processing of network data. A so-called low latency network connection is one that experiences small delay times, while a high latency connection suffers from long delays.

Besides propagation delays, latency also may also involve transmission delays (properties of the physical medium) and processing delays (such as passing through proxy servers or making network hops on the Internet).

Throughput

Throughput is the number of such actions executed or results produced per unit of time. This is measured in units of whatever is being produced (cars, motorcycles, I/O samples, memory words, iterations) per unit of time. The term "memory bandwidth" is sometimes used to specify the throughput of memory systems.

Although the theoretical peak bandwidth of a network connection is fixed according to the technology used, the actual amount of data that flows over a connection (called throughput) varies over time and is affected by higher and lower latencies. Excessive latency creates bottlenecks that prevent data from filling the network pipe, thus decreasing throughput and limiting the maximum effective bandwidth of a connection. The impact of latency on network throughput can be temporary (lasting a few seconds) or persistent (constant) depending on the source of the delays.

What is the difference between bandwidth and throughput?

Bandwidth is the maximum amount of data that can move from one point to another over a given amount of time. Throughput is the amount of data that actuality moves from one point to another over a given amount of time. Many things effect throughput may include protocol, data loss, latency, and others.

An analogy for this is a highway. Cars can move down that highway (at some speed), Bandwidth tells us what the maximum number of cars that can come down the highway over a period of time. However perhaps due to roadblocks, lane closures, and car crashes we see fewer cars come down the highway, over a period of time, this is throughput.

Difference Between Grid Computing Vs. Distributed Computing?

Distributed Computing

Distributed Computing is an environment in which a group of independent and geographically dispersed computer systems take part to solve a complex problem, each by solving a part of the solution and then combining the result from all computers. These systems are loosely coupled systems coordinately working for a common goal. It can be defined as

• A computing system in which services are provided by a pool of computers collaborating over a network.
• A computing environment that may involve computers of differing architectures and data representation formats that share data and system resources.

Grid Computing

The Basic idea between Grid Computing is to utilise the ideal CPU cycles and storage of millions of computer systems across a global network function as a flexible, pervasive, and inexpensive, accessible pool that could be harnessed by anyone who needs it, similar to the way power companies and their users share the electrical grid. There are many definitions of the term: Grid computing

.

• A service for sharing computer power and data storage capacity over the Internet
• An ambitious and exciting global effort to develop an environment in which individual users can access computers, databases and experimental facilities directly and transparently, without having to consider where those facilities are located.
• Grid computing is a model for allowing companies to use a large number of computing resources on demand, no matter where they are located.

What is the difference between cloud computing and grid computing?

Both grid computing and cloud computing are network-based computing technologies that involve resource pooling, but cloud computing eliminates the complexity of buying hardware and software for building applications by allocating resources that are placed over multiple servers in clusters.

Grid Computing	Cloud Computing
It is for Application Oriented	It is for Service Oriented
The resources are distributed amoung different computing units for processing a single task	The computing resources are managed centrally and are placed over multiple servers in clusters.
Grids are generally owned and managed by an organization with in its premises	The cloud servers are owned by infrastructure providers and are placed in physically disparate locations
It operates within a corporate network	It can also be accessed through the internet
It provides a shared pool of computing resources on an as - needed basis	It involves deling with a common problem using varing number of computer resources
Its a collection of interconnected computers and networks that can be called for large scale processing tasks	More than one computer coordinates to resolve the problem together

What is edge computing?

Edge computing refers to data processing power at the edge of a network instead of holding that processing power in a cloud or a central data warehouse. There are several examples where it's advantageous to do so. For example, in the industrial Internet of Things applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimise production, and prevent product defects.

An edge device could also be an ATM (the bank wants to stop fraudulent financial transactions); a retail store that is using a beacon to push in-store incentives to a mobile app; a smartphone; a gateway device that collects data from other endpoints before sending it to the cloud, etc.

What is N-tier architecture?

An n-tier architecture is also called multi-tier architecture because the software is engineered to have the processing, data management, and presentation functions are physically and logically separated. That means that these different functions are hosted on several machines or clusters, ensuring that services are provided without resources being shared and, as such, these services are delivered at top capacity. The “N” in the name n-tier architecture refers to any number from 1.

Not only does your software gain from being able to get services at the best possible rate, but it’s also easier to manage. This is because when you work on one section, the changes you make will not affect the other functions. And if there is a problem, you can quickly pinpoint where it originates.

What is a cloud-native architecture?

Cloud-native is an approach to building and running applications that exploit the advantages of the cloud computing delivery model. Cloud-native is about how applications are created and deployed, not where. While today public cloud impacts the thinking about infrastructure investment for virtually every industry, a cloud-like delivery model isn’t exclusive to open environments. It's appropriate for both public and private clouds. Most important is the ability to offer nearly limitless computing power, on-demand, along with modern data and application services for developers. When companies build and operate applications in a cloud-native fashion, they bring new ideas to market faster and respond sooner to customer demands.

Organisations require a platform for building and operating cloud-native applications and services that automates and integrates the concepts of DevOps, continuous delivery, microservices, and containers: