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How to Create a Load Balancer on Google Cloud?

Creating an HTTP(s) cloud load balancer on the Google Cloud Platform (GCP)

If you're hosting your applications on Google Cloud and searching for better high-availability, then you ought to try implementing a load balancer (LB).

Google Cloud LB is sensible. It offers quite a standard one.

HTTP/2 enabled

Terminate SSL handshake

Custom SSL/TLS policies

Route traffic to the closest server

Path-based routing

Auto-scaling

and tons more…

Following, I even have two servers (one within the US and another one within the UK). Let’s create a load balancer and route traffic to both the servers. information technology degrees since Google offers auto-scaling, you've got multiple options and choose what your business requires. However, during this article, I will be able to explain the way to create a load balancer using unmanaged instance groups that don’t support auto-scaling.

Create Instance Groups

All the servers should be inside the instance groups. So this is often a pre-requisite to making an LB.

Login to GCP Console

Navigate to Compute Engine >> Instance groups

Click create an instance group

Enter the name, select zone as one, a region where your servers are, unmanaged instance group, choose the server from VM instance drop-down and click on Create

Create an HTTP(s) LB

Google offers three sorts of LB.

HTTP (s)

TCP

UDP

To manage web application traffic distribution, HTTP(s) is suitable. Let’s create that.

Navigate to Network Services >> load balancing

Click Create a load balancer

Enter the LB name

On the backend configuration tab, select the drop-down and make a backend service

Enter the name and choose backend type as instance groups

Add both instance groups (server-us and server-UK)

Adjust the port number – port number of web server or application are going to be listening on the servers

Under checkup, click create

Enter the name, select the protocol, port

A checkup is important for LB to understand which instance is down, so it stops sending traffic. Below, I'm instructing LB to hit the server IP with port 80 every 10 seconds. If a server doesn’t respond 3 times consecutively then, LB will mark that instance down. You can see the traffic from Europe is getting routed to the closest server located in London, and North America and Asia traffic are to the US server. the great thing is you don’t need to configure anything for geo traffic routing, it's the default feature. The above monitoring is out there under the backends tab.

Conclusion

Creating an LB is straightforward, and that I hope this provides you thought about it. there's tons of configuration you'll do to satisfy your application requirement like session affinity, CDN integration, SSL cert, etc. If you're exploring a choice to have a load balancer for your application, then fiddle and see how it helps.

Costing is predicated on usage so there's no monthly or annual locking. I feel the minimal usage would cost around $18 per month. If you're curious to find out about Google Cloud administration, then you'll consider taking this

5 Best Serverless Security Platform for Your Applications

Developing or developed Serverless applications but have you ever considered securing them? does one know if your application is secure?

The serverless application popularity is growing so it's a security risk. Many things can fail and be susceptible to online threats. the subsequent are a number of the main risks to be carefully mitigated.

Denial of service attacks

Business logic manipulation

Resource abuse

Data injection

Insecure authentication

Insecure storage

Vulnerable third-party API/tools integration

A serverless application requires a slightly different security approach than a standard one. it's more the securing functions. And, that’s why you would like a specialized platform for comprehensive security protection. It also requires a special sort of monitoring and debugging.

I would recommend taking a glance at this guide from PureSec, which covers 12 most crucial risks for serverless applications.

Let’s explore the subsequent solution.

Protego

Visibility, security, and control from development to production runtime.

The Protego platform offers complete visibility in 15 to twenty minutes. It continuously monitors the infrastructure to detect and mitigate risks.

There is three central platform concept.

Proact – a comprehensive view of your serverless application environment with the posture of security risks.

Observe – connect all the info points and apply machine learning techniques to detect threats and malicious code — complete visibility with root cause analysis.

Defend – prevent and mitigate risks to guard your application. Capable of blocking function level attacks in real-time.

Protego works with Google Cloud, AWS, Azure serverless platform. It also helps you to suits HIPPA, FISMA, GDPR, and PCI requirements.

PureSec

PureSec offers end-to-end security for AWS Lambda, Google Cloud Functions, IBM Cloud Functions, and Azure Functions. It integrates well with a number of the favored platform and tools.

Gitlab

Splunk

Apex

Jenkins

AWS Cloudformation

Serverless framework

PureSec’s serverless application firewall detects and stops attacks at the function event-data layer without impacting the performance. technology credit union The detection engine is capable of inspecting event trigger type as NoSQL DB, API, Cloud Storage, Pub/Sub messaging, and more. Some of the advantages of using FunctionShield are:

Data leakage prevention by monitoring outbound network traffic from functions

Prevent handler ASCII text file leakage

Child process execution control

An option to configure in an alert mode to log security events or block to prevent the execution when policy violates.

It adds but 1-millisecond latency to overall execution.

Snyk

Snyk is one of the favored open-source solutions to watch, find, and fix the vulnerabilities found within the application’s dependencies. Recently, they need to introduce the mixing with AWS Lambda and Azure Functions which permit you to attach and check if a deployed application is vulnerable or not.

Aqua

Aqua offers two in one service – secure serverless container and functions, both.

It scans container images and functions for known and unknown vulnerabilities during a library, configuration, and permissions. Aqua is often integrated into the CI/CD pipeline.

Twistlock

Protect your application at every stage of the lifecycle with Twistlock.

It scans and protects all the functions within the account in real-time to stay your application vulnerable free. a number of the features are:

Supports Python, .Net, Java, and Node.js

Cloud-native firewall for continuous threat monitoring and prevention

Templates for HIPPA and PCI compliance

Integrate with TeamCity, Jenkins

Vulnerability management

Twistlock leverages machine learning to deliver automated runtime protection and policy creation.

Conclusion

Securing applications is important whether it's serverless or traditional. the great news is that they offer a FREE trial so experience yourself to ascertain what works for your application. If you're a newbie and curious about hands-on AWS Lambda and Serverless framework, then inspect this fantastic online course.

How to Find the External IP of Google Cloud VM?

You are locating external IP addresses within GCP Server.

Are you performing on a project where you would like to retrieve external (Internet/Public) IP of respective VM instance for application?

Good news – you'll quickly get them.

I am sure you'd have tried running ifconfig command. And, you'll notice the results contain only internal IP.

GCP and AWS, both have a friendly web interface where you'll see the general public IP, but if you need to urge them on a server directly, then the subsequent commands will assist you.

Getting External IP on GCP VM

There are two possible ways I'm conscious of. the primary one is employing a cloud command.

All about face recognition for Businesses

Facial Recognition isn’t confined to the realms of computing. it's solid business applications.

One of the most well-liked buzzwords of this decade is face recognition.

It’s the part of applied machine learning which will detect and identify human faces, a drag that has been notoriously difficult for computers up to now. And this has burst open an entirely new world of exciting possibilities and challenges for businesses, governments, and individuals alike.

If you’re a baron and are wondering what the fuss is all about, and whether there’s some utility during this new development, we’ve got you covered. information technology degree during this article, we’ll check out the history of face recognition, its development, current uses, controversies, deployment, and lots of more facets.

By the top of it, you’ll have a solid grasp of what face recognition technology is all about, and what its implications are for businesses.

Let’s get started!

Evolution of face recognition 

For all the hype and media coverage surrounding face recognition, the technology has been around for a few times. the primary serious algorithmic add detecting faces was the Viola-Jones Object Detection Framework published in 2001. Though a general-purpose framework for identifying objects within images, it had been quickly applied to face detection with excellent success. the most reason for this algorithm’s popularity was its speed; while the training process was excruciatingly slow, the detection process was extremely fast.

As early as 2001/2004, the typical personal computer running this algorithm was ready to process a 300px X 300px frame in 0.07 seconds (more here). The accuracy rates, though like what humans are able to do, was impressive at 90%.

However, real progress wasn’t made until the last decade of 2010-2020, when Convolutional Neural Networks emerged because the best method to perform facial detection. the rationale was the supply of raw processing power and gigantic system memories made available through cloud computing by Infrastructure-as-a-Service (IaaS) providers. For the primary time in history, computers were consistently beating humans in recognizing faces, especially when an outsized number of random faces were involved.

How does face recognition work?

Facial recognition may be a multi-step process, with several specialized sub-systems involved.

Here’s what the varied stages mean:

Detection / Tracking: This part of the preprocessing stage is liable for identifying and tracking faces within the given image or video file. Once this process is complete, we all know needless to say that there’s a face within the given input, and it is often processed further. The tracking phase is additionally liable for tracking certain parts, particular features, or expressions during a face, should that be needed.

Alignment: the matter of face recognition is compounded because faces during a given image or video don't follow any guidelines. The person could also be zoomed-in or out, peeking from behind a tree, or present during a side profile, making the matter of face detection even harder. this is often where face alignment comes in: it tells us where within the given image/video the face lines, and what are the contours for countenance.

Feature extraction: because the name suggests, during this phase of the method (we’re now within the Recognition stage), the individual features of the face, like eyes, nose, chin, lips, etc., are extracted within the form which will be employed by algorithms within the next stage. At this stage, the pc has collected enough hard data to inform a face apart uniquely.

Feature matching/classification: during this stage, the inputs received from feature extraction are matched against the given database to deduce the identity of the person. This phase is additionally referred to as classification because the algorithm could also be needed to categorize faces rather than individually identifying them.

Once this process is over, we all know needless to say whether the given face is a component of the database we compared against or not. the ultimate output can also contain tagging, the way we’re wont to seeing on Facebook.

Deployment considerations: Server-side vs. client-side

Facial recognition can work both on the server also as on the device that the user is interacting with. for instance, once you upload a photograph to Facebook, the algorithms are run on the server-side; on the opposite hand, an ID system that uses your face to unlock the device must run on the client-side. So, which one is better?

Honestly, it’s not about which one is best. Both server-side and client-side deployments have their strengths, and in practice, businesses deploy a hybrid system. The recommended practice is to coach your models on the server-side, where training data and processing resources are without limit. Once the models are trained, these are often packaged and deployed on the client-side, which improves the speed of the system also maintains the user’s privacy.

Sending everything to the server introduces a delay, which may be bad or unacceptable in certain cases. At an equivalent time, keeping everything on the client-side will end in weaker models.

How accurate is Facial Recognition?

Accuracy isn't a really well-defined term in face recognition. the most reason is that it’s a fuzzy problem with all kinds of tousled inputs (low light, face partially covered by hair, camera-quality, etc.) and even deceptive inputs (more on this later!). As a result, the neural networks involved in face recognition got to be tweaked for the matter at hand, limiting their scope. So, while an industrial face recognition system might boast of 100% accuracy (which is usually the case), an equivalent system might not be even 20% accurate when asked to spot faces during a crowded photo.

In one research, a specific sort of face recognition algorithm was ready to achieve 98.52% accuracy, above the human accuracy of 97.53% achieved within the same test. In another study conducted in forensics, the mixture of human judgment and algorithms yielded the simplest leads to some cases.

Bottom line — for focused, well-defined applications, face recognition is that the best tool we've.

Where is face recognition being used?

Even within the short period that viable algorithms are developed, face recognition has found incredibly useful and exciting applications. a number of these are conspicuous, but some are so subtly and fundamentally woven into the lifestyle that we hardly pause to believe what’s underneath.

Facebook is probably the foremost common example of recent face recognition systems at work. As soon as you upload a photograph, the social network is in a position to detect faces. While a while ago you were asked to tag friends, now Facebook is in a position to try to do so on its own.

A cool new application by Facebook is that the feature of informing users when photos containing their face are uploaded by someone, albeit they’ve not been tagged in those photos.

Snapchat makes heavy use of face detection and recognition for several of its features, most notably, the funny filters that are such a rage.

For these filters to figure, the contours and features of the subject’s face got to be detected perfectly, otherwise, the overlays won’t look realistic. The same goes for Face Swap, another popular feature in Snapchat. just in case you’re curious about diving deeper into Snapchat’s capabilities in face recognition, see here.

Uber has been battling privacy and safety concerns for a short time now, and therefore the newest weapon within the company’s arsenal is face recognition. the corporate has unrolled a replacement feature where its driver-partners’ identity is verified by using their faces. the corporate says on its blog that after testing several face recognition tech vendors, they settled on Microsoft Face API for its top quality. Interestingly, this real-time ID check works well in low-light conditions and is in a position to detect glasses.

With face recognition proving successful within the wild, it’s easy to predict that it'd soon replace other identification methods at educational institutions, hospitals, libraries, etc.

Retail crime prevention may be a natural extension of the appliance of face recognition. The retail industry loses an estimated $45 billion per annum to shoplifters and other retail crimes, with little or no to counter it. Now, companies like FaceFirst are helping retailers use face recognition to detect previous offenders and alert security officers.

While this newfound superpower within the hands of the police has sparked heated public debates over individual privacy, the police believe it'll help them restrict wrongdoers better. As Richard Lewis, Deputy Chief Constable of South Wales police told the Financial Times:

If you identify someone who has committed an offense [previously], you basically say: we all know you’re here, please behave yourself.

Healthcare recently had an unexpected application, where face recognition helped detect a rare genetic disease called DiGeorge Syndrome.

The DiGeorge Syndrome appears in about 1 in 6,000 children and leads to deformities in several parts of the body. The healthcare problem, during this case, is more severe for poorer countries, which don’t have the resources to travel for expensive diagnosis methods. As such, face recognition, with an astounding accuracy of 96.6%, offers new hope for victims of DiGeorge Syndrome.

n the airline industry, face recognition adoption is learning, and it’ll soon replace the traditional boarding passes. Currently, there are limited but promising leads to helping identify passengers as they leave the country. In fact, the Transport Security Administration (TSA) of the US has laid out an idea for the widespread use of facial recognition-based biometrics.

Controversial uses of face recognition 

Technology empowers us, though its good or bad use is up to us. No doubt, then, that something as potent and radical as face recognition is being put to use in a way that raises concern about fundamental human rights and ethics.

The most prominent example of controversial uses of face recognition is China’s enormous closed-circuit television that employs an estimated 200 million cameras to stay an eye fixed on its 1.4 billion citizens.

The system tracks people and evaluates their actions, constantly updating a metric called citizen score. While there’s some value in having a strong state-controlled closed-circuit television (tracking debt-defaulters, for example), most see it because of the arrival of the dystopian future Orwell imagined. It’s a future where governments have unlimited power over the individual, and privacy is non-existent.

The second example of debatable use of face recognition also comes (unsurprisingly?) from China. At this point, it’s the varsity system adopting face recognition to form sure students are “attentive” during classes. The new face recognition system, though not widespread yet, replaces ID cards, library cards, attendance systems, etc., using the student’s face for identification.