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Posted on Tuesday, November 7, 2017 2:40 PM

Sam Vanhoutte by Sam Vanhoutte

Internet of things (IoT) is hot. And it should be! But one of the major misconceptions is that IoT projects are overly focused on technology. At times I have been guilty of that myself. It appears that the gap between business and IT has reopened in this respect. The business does not understand enough about IT, of its possibilities, and IT does not know enough about the business, of what is needed.

IT is only a means. And IoT is not necessarily the solution. And I'm not just talking about IoT gadgets like the not-so-smart smartlocks, smart lighting, expensive juicers, connected refrigerators or other online, possibly automatically shopping, consumer equipment.

Even business-oriented and industrial IoT is often too much focused on the technological capabilities, rather than on business use. As a result, many IoT projects are stuck in the proof-of-concept (poc) phase and do not evolve into pilots and practical acceptance. I think the only way to get business buy-in is through the creation of a clear business case.

Past the hype

This is easier said than done. The business case is often hard to predict. Pressure can be high, partly due to the fact that IoT is now beyond its peak on the Gartner hype cycle. The top of the hype lies behind us and the downturn to 'the trough of disappointment' has set in. For those who are not easily discouraged by Gartner, there are still some genuine pitfalls.

In fact, the design of the poc-phase is one of these pitfalls. Many proof-of-concepts are set up without or with insufficient business base. This amounts to a discrepancy between the poc and business reality. Test setups for IoT solutions often put too much emphasis on quick results.

Too much time and effort are spent on matters that are less important in practice. And, perhaps even worse, too little time and effort goes into things that are much more important in practice. One example are the upcoming European Data Protection Rules GDPR.

Go for distinctiveness

A better approach to the poc phase not only increases the chance of success, it also reduces costs because time is spent in more meaningful ways. This also includes insight into what has become a commodity nowadays. Namely, that IoT is an end-to-end value chain.

There is little credit to be gained from developing components like IoT hardware, network edge capabilities, connectivity, and data intake. It is too difficult for organizations to distinguish themselves here. Instead, they should focus on intelligent clouds, data analytics, reporting and action. The latter is what brings the desired business use.

IoT is only a concept; a means to innovation and acceleration. This means can have a goal, for example an unforeseen reduction of energy consumption.

Let us look at the example of a company that stores deep-frozen food. Frozen foods are very energy intensive, but the freezing itself takes place within a specific temperature range. The low temperature does not have to be constant. Sometimes less freezing is acceptable. The company in question has an hourly contract, with a rate structure, from an energy provider. And that gives them the chance to use less energy at times when it is expensive. During cheaper hours, they can freeze harder.

On the way to greater benefits

Nevertheless, many current IoT applications involve no more than the automation of existing business processes and practices. But that is just the beginning. Next to smarter power consumption on an industrial scale, we can think of many new activities and even completely new business models.

Efficient monitoring allows for further optimization of business processes. This solves two problems at once. Because optimization requires data collection, and you can do more with more data. This extends to many departments within the organization, as they know the business very well.

Means for innovation

A good deployment of IoT can thus provide insights that allow other value-added services to be developed. This is completely in line with the shift from hardware sales to services. New services allow us to tap into other markets - through IoT, which is still a means and not the goal. IoT is hot, but no more (or less) than a good concept; a means to drive innovation and acceleration.

Note: This article was first published via Computable on 6 November 2017 (in Dutch) 

Categories: Opinions
written by: Sam Vanhoutte

Posted on Wednesday, October 25, 2017 1:44 PM

Stijn Moreels by Stijn Moreels

One of the first questions people sometimes ask me about functional programming, is the question about readability. It was a question I had myself when I started with learning functional concepts.

Readability

Now, before moving on; the term “readability” is something very subjective and yet we can define some common grounds. So, it’s not that easy to find something that everyone agrees on about readability.

Single-Letter Values and Functions

The first thing I (and maybe many before and after me) discovered was that the rules of naming conventions in an object-oriented language couldn’t be entirely used within a functional environment.

Functional programmers have this habit of giving values and functions very short names. So short that it only consists of a single letter. If we use this habit in an object-oriented language; this is almost always a bad practice (maybe not a for-loop with an aggregated index?).

So, why is this different in a functional environment?

The answer to this can be many things I guess; one of the things that comes to mind is that in a functional environment, you will very often write functions that can be used for any type (generic). Naming such values and functions can be difficult. Should we name it “value”?

In functional languages, the x is most of the time used as this “value”. Strangely, by using x I found the code a lot clearer. So, for values: x, y and z for functions: f, g and h. (Note that these letters are the same as we use in mathematics.)

When we talk about multiple values, we add and trailing 's'; like xs.

Ok, look for example at this following “bind” function for the Either Monad (Rop):

We have written the values explicitly like we would do in an imperative scenario. Now look at the following:

Most of all in the infix operator, the most important part is that we clearly see that the arguments passed in to the “bind” function are flipped. This was something we could quite see immediately in the first example.

After a while, when you see an f somewhere; you automatically understand it’s a function, just like x is some “value”.

Now, I’m not going to state anything; but in my personal opinion, the second example shows more what’s going on with less explicit names. We reach a higher level of readability by abstracting our names, rather strange at first.

Partially Applied Functions

One of the powerful concepts in functional languages that I really miss in object-oriented languages (without off course custom-made functionality like Extension Methods in C#); is the concept of Partial Application. This concept describes the idea that if you send an argument to a function, you get back a function with the remaining arguments. This concept is very strong because now we can decompose for example a three arguments function into three functions with 1, 2 and 3 arguments respectively.

In practice, this concept can really be of help when declaring problems. In my previous post; I solved the Coin Change Kata. In one the properties, I needed to describe the overall core-functionality. In the assertion, I needed to assert on the sum all the coin values:

The “( )” around the operators “+” and “=” makes sure I get a function back with the remaining argument. I could have written the assertion as the following expression:

And I can understand that in the beginning this will maybe be more understandable for you than the previous example. But please note that, with this anonymous function explicitly specified, we have written a “lot” of code just for addition and equality verification.

I personally think that every functional programmer will refactor this to the first example. Not only because it can be written in fewer characters, but it also expresses more what we’re trying to solve. In imperative languages, we typically assign a value to a variable, and that can be used to another variable, … and without you knowing you’ve created a pipeline. I like this concept very much. I don’t have to assign the next result to a value anymore but can just pass it along to the next function.

“For the Change”
“We need to have each value”
“So we can sum all the values”
“And sum it with the remaining value”
“This should be the same as we expected”

Notice that we always use the verb in the front of the sentence. The action we’re trying to express in code now in front of the line by partially applying one of the arguments and not at the end.
Also note that, when we specify the function explicitly, you can’t read the expression in the second example from top to bottom without moving you’re eyes to the right to see the addition or the equality verification; which is actually the most important part of the line.

This is also one of the reasons I like this form of programming.

Yes, I know that it takes some time to get used to this way of writing functions; but I can assure you. Once you have mastered this technique, you would want this in your favorite object-oriented language as well. (That's one of the reasons I implemented them in the form of Extension Methods).

Infix Operators

One thing that I can’t find a common approach about yet, is the feature of defining your own operators and where to use it. Infix operators can make your code a lot cleaner and more readable; but it can also harm your readability; that’s why it’s probably difficult to define a common approach.

There are already many operators available and by specifying your own operators that looks similar; we can guess what the operator does.

The (|>) pipe operator already exists, and by defining and operators like (||>) or (|>>), we can guess that it has something to do with piping more than one argument, or has something to do with piping and composing.

I didn’t find a global rule to this approach, but I guess it’s something that must be used carefully. If we would define for every function an operator, the code would be less readable.

The (>>=) operator is used for the binding, and so it’s reasonable to define them instead of writing “bind” over-and-over again; because we're actually more interested in WHAT you're trying to bind. The same can be said about the (<*>) operator for the Applicative Bind or the (<!>, <$>) operator for the Mapping. When you see the (<|>) you know it has something to do with Conditional Piping since it pipes in two directions ("if then?"). Some operators are well known and so, are probably never questionable to define.

FsCheck defines (.&.) and (.|.) operators to define the AND and OR of Properties. We already know the boolean operators without the dots leading and trailing them, that’s why it’s easier to know what the infix operator does.

The tricky part is when we use too much operators. I would like to use those operators when we’re changing the data flow in such a way that we can reuse it somewhere else. In those cases it’s probably a good approach to define an infix operator.

Conclusion

This small blog post was for me a small reminder of why I write lesser characters and still make more Declarative Code. It was strange at first to think about it. Most of the time in Object-Oriented Languages; when you talk about smaller names, short-handed operators, … you’re quickly end up with an “anti-pattern” or a bad practice while in Functional Programming this is the right way to do it.

Both imperative and functional programmers are right in my opinion. It’s just a way the language allows us to write clear/clean readable code, because that is really what we want to do.

Categories: Technology
written by: Stijn Moreels

Posted on Monday, October 23, 2017 11:18 AM

Glenn Colpaert by Glenn Colpaert

In this blog post, I will go deeper into detail on why IoT is more than just collecting some data from devices and explain you why it's important to engage business into your IoT Solution next to your perfectly built architecture.

Simplifying IoT, one Azure service at a time!

The Internet of Things (IoT) isn't a technology revolution, it is a business revolution enabled by technology. By 2020, there will be 26 Billion connected 'things' and IoT will be good for a 12$ Trillion market share. These connected 'things' will range from more consumer-driven IoT ranging from wearables and home automation to intelligent industrial scenarios like smart buildings and intelligent machine infrastructures.

In this blog post, I will go deeper into detail on why IoT is more than just collecting some data from devices and explain you why it's important to engage business into your IoT Solution next to your perfectly built architecture. I will talk about some of the more complex things you need to think about when building and designing your solution. Some of them might be scary or sound very complex to tackle, but remember that some of these solutions are just one Azure service away...

A simple view on IoT

When creating a simplified overview of an IoT project or solution, it can be drilled down to the following 4 key components.
An IoT project always comes down to securely connecting your devices to the cloud and start flowing your local data streams into the cloud. Once your device data is stored in the cloud you can start creating insights on it. Based on that, you can leverage the business intelligence towards business and allow them to act upon actions or events raised on that data and trigger additional workflows.

IoT projects can be complex!

However, when taking a closer look on IoT Projects there is more to say than the above 4 key components, especially when moving from a POC setup to a full-blown production ready solution with potentially thousands of devices in the field. As IoT is a business-driven revolution, the most important action there is that you need business to be involved from the very start, as they are the key-drivers from your IoT project. The risk of not involving the business into IoT projects is that you potentially get stuck in POC limbo and your IoT solutions will never see the break of day. Once you get business on board, things are getting easier... or not. Some of the most important technical questions or decisions are listed below, all of them are just a small part of your entire solution. 

How to connect things that are hard to connect?

Getting your IP enabled devices connected to the cloud is one thing, but how will you connect your existing devices, that don't speak IP, to the cloud. What if your devices are not capable of change or the risk of changing them is too high? Or what if your devices aren't even allowed to talk to the cloud, due to security reasons. When this is the case you might need to look at other possibilities to connect your devices with the cloud, like for example introducing a gateway that will be responsible for acting as a 'bridge' between your devices and cloud platform.

Device Management/lifecycle

Once your devices are connected, there's still some open questions or challenges you need to tackle before processing your data. How will you securely identify and enroll your devices onto your IoT Platform. How will you scale that enrollment for many devices? Next to enrollment there is also a question of configuration and managing your devices. When looking at Device Management and Lifecycles there are a couple of management patterns like updates, reboots, configuration updates or even software updates.

Data storage/Visualization

Another key component within an IoT solution is data. Data is key on getting the insights the business is looking for. Without a proper data storage/visualization strategy you're in for some trouble, think fast IO and high scale. When it comes to storing your data, there is no silver bullet. It really depends on the use-case and what the ultimate goal is. Key action there is to pick the storage based on the actions you will perform with your stored data. There is storage that is a perfect input for your analytics tiers but mighy not be a good option when it's just about archiving the data for later use.

Analytics

As already mentioned during this blog, data is key inside your IoT solution. The real value of your IoT project is making sense of your data and getting insights from that data. Once you captured that insight, it is key to connect these insights back to the business and evolve your business by learning from those insights.

Edge Computing

When doing IoT projects you're not always in the position of having full-blown connected sites or factories. There might be a limit on communication bandwidth or even limited internet connectivity. What if you would like your devices to only send aggregated data of the last minute to the cloud? What if you would like to keep all your data close to your device and only send fault data to the cloud. If this is the case, you need introduce Edge Computing into your IoT Solution, Edge Computing allows you to perform buffering, analytics, machine learning and even executing custom code on your device without the need of a proper internet connection.

Security

Let's not go into detail on this one. Start implementing it from day zero as this is the most important part of your IoT Solution. Your end to end value chain must be secured. Never cut budget on your security strategy and implementation when doing IoT Projects.

Simplifying IoT

Congratulations, you've survived the scary part... Thanks to the Azure cloud some of the above challenges are just a couple of button clicks away. The goals of Azure and Microsoft is making it easier to build, secure and provision scalable solutions from device to cloud. The list of recent IoT innovations on the Azure platform is endless, with major focus on some of the key challenges every IoT project phases: Security, Device Management, Insights and Edge Computing.
The future is bright, time to do some IoT!!
Cheers, Glenn
Categories: Azure
Tags: IoT
written by: Glenn Colpaert

Posted on Tuesday, October 17, 2017 12:53 PM

Tom Kerkhove by Tom Kerkhove

Auto-scaling is a great way to not only optimize your costs but also a flexible way of doing asynchronous processing.We will look at how Azure Monitor Autoscale allows you to define auto-scaling rules, what the caveats are and what would be good additions to the service

Building scalable systems is crucial for any cloud platform.

One way to achieve this is to decouple your frontend nodes from your backend processing by using the Competing Consumer pattern. This makes it possible to easily add more processing instances (scale out) when the workload is growing, being messages filling the queue.
Automating things is always great, but it is crucial to be aware of what is going on in your platform. This is often forgotten, but should be part of your monitoring as well.
Once everything is setup you can save money by optimizing your resources based on your needs, instead of overprovisioning.

A question I have received a couple of times is - Great! But how do I do that?

Enter Azure Monitor Autoscale

Azure Monitor Autoscale enables you to define rules that will automatically scale your workloads based on specific metrics.

These metrics can be Service Bus Queues, Storage Queues, Application Insights, custom metrics and more. Currently, Azure Monitor Autoscale is limited to workloads running on Azure Cloud Services (Yes, you've read that right!), App Service Plans and/or Virtual Machine Scale Sets.

When more advanced auto-scaling rules are required, you can define multiple autoscale conditions. This allows you to vary your scaling based on day of the week, time of day or even date ranges.

This makes it really great because this allows you to have more aggressive scaling over the weekend, when more people are buying products than during working hours. The date ranges are also interesting because you can define specific rules for a specific period when you are launching a new marketing campaign and expect more traffic.

Configuring auto-scaling for an Azure Service Bus Queue

Sello is hosting an online platform for selling items online and would like to improve their scalability. To achieve this, they want to start auto-scaling their worker role based on the message count of their Service Bus queue.

In order to configure it, we need to go to "Azure Monitor" and click on "Autoscale". There it will give you an overview of all resources that can be autoscaled and their current status:

As you can see, there is no auto-scaling configured which we can easily add by clicking on the specific role we'd like to autoscale.

When no auto-scaling is configured you can easily change the current instance count, or you can enable auto-scaling and define the profile that fits your needs.

Each auto-scaling condition has a name and contains a set of scaling rules that will trigger a scaling action. Next to that, it provides you the capability to limit the instances to a certain amount of instances.

When adding a scale rule you can select the metric you want to scale on and basically define the criteria that triggers the action you want to perform being scaling up or down.

By using a cooldown, it allows your platform to catch up after the previous scaling activity. This is to avoid that you add more instance again, while the previous scale action has actually already mitigated it.

In this case, we're adding a rule to add 2 instances when the active message count is greater than 2000 with a cooldown of 15 minutes.

Scaling out is great, scaling in is even better! Just follow the same principle, here we're scaling 1 instance down when the criteria are met.

Once everything is configured, your role will start auto-scaling and the configuration looks similar to this:

 

Creating awareness about auto-scaling

Woohoow, auto-scaling! Awesome!

Well - It's great but not done yet. Be aware of how your platform is auto-scaling. By using the Run History you can get an overview of your recent scaling activities and learn from it. Creating scaling definitions is not an easy thing to do and should be re-evaluated frequently.

As you can see below, we can handle the load without any problem but it can be improved by scaling down more aggressively.

A more proactive way of monitoring this is by using notifications where you can either use email notifications or trigger an HTTP webhook when scaling action is happening.

This is very handy when you want to create awareness about these actions - An easy way to achieve this is to create a Logic App that handles these events, similar to how I did this for Azure Alerts.

You can use one centralized handler for this or create dedicated handlers, based on your use-case. I personally prefer to use a centralized handler because it makes it easier to maintain if the handling is the same for all.

When we put everything together, this is a high-level overview of all the settings for auto-scaling.

If we were to add a new autoscale condition, we'd have to specify the period in which it would be in effect and basically ignoring all other scaling conditions.

Caveats

Defining auto-scaling rules are not easy and they come with a few caveats:

Be careful what metric you are auto-scaling on and make sure that it's the correct one. Unfortunately, I've seen a case where we were stuck in an infinite scaling loop because we were auto-scaling our worker roles based on the Message Count of a Service Bus queue. However; Message Count not only includes the active messages but also the dead-lettered messages which weren't going away. What we actually ended up with was changing our auto-scaling metric to Active Message Count which is what we were interested in here.

This brings me to monitor your auto-scaling - This is not only important to detect issues as I've just mentioned but also to learn how your platform is scaling and continuously improve your scaling criteria. It is something that needs to grow since this is use-case specific.

Protect your budget and include instance limitations on your auto-scaling conditions. This will protect you from burning your resource costs in case something goes wrong or if having to wait a little longer is not a problem.

Taking auto-scaling to the next level

Azure Monitor Autoscale is great how it is today, but I see a couple of features that would be nice to have:

  • Scaling Playbooks - Similar to Azure Alerts & Security Center's Security Playbooks, it would be great to have native integration with Azure Logic Apps which makes it not only easier but also encourages people to use a centralized workflow of handling these kinds of notifications. Next to that, it also makes it easier to link both resources together, instead of having to copy the URL of the HTTP connector in your Logic App.
  • Event-Driven Auto-scaling - The current auto-scaling is awesome and it provides a variety of metric sources. However, with the launch of Azure Event Grid, it would be great to see Azure Monitor Autoscale evolve to support an event-based approach as well:
    • Autoscale when certain events are being pushed by Azure Event Grid to react instead of polling a specific metric
    • Emit auto-scaling events when actions are being started or finalized. That would allow subscribers to react on that instead of triggering a webhook. This also provides more extensibility where instead of only notifying one webhook, we can basically open it up for everybody who is interested in this

That said, I think having both a metric-based & eventing-based model would be the sweet spot as these support their own use-cases.

Conclusion

With Azure Monitor Autoscale it is really easy to define auto-scaling rules that handling all the scaling for you, but you need to be careful with it. Having a good monitoring approach here is the key to success.

Every powerful tool comes with a responsibility.

Thanks for reading,

Tom

Categories: Azure
written by: Tom Kerkhove

Posted on Friday, October 13, 2017 10:50 AM

Tom Kerkhove by Tom Kerkhove

A few weeks ago, Microsoft held another edition of its Ignite conference in Orlando, FL.

After going through most of the announcements and digesting them I found that there were a couple of interesting ones in the security & data space.

Let's have a closer look.

Introducing Virtual Network Service Endpoints (Preview)

With the introduction of Virtual Network Service Endpoints (Preview) you can now protect your Azure resources by moving them inside a VNET and thus restricting access to that VNET or subnet itself.

Currently, this is only supported for Azure Storage & Azure SQL Database/Warehouse but the end goal is to provide this for all services.

By using VNET Service Endpoints you can now fully isolate your resources because you can now fully remove all access to the public internet by which you are limiting the risk of exposure.

It has been a long-awaited feature to isolated access, certainly for Azure Storage & Azure SQL Database, and I am excited and very happy that it's finally here!

Additional resources:

Introducing Azure Data Factory 2.0 (Preview)

This must be my favorite announcement - Azure Data Factory 2.0 (Preview)the next generation of data integration.

While Azure Data Factory 1.0 was limited to a data-slicing model only, it now supports different types of triggers such as webhooks.

With Azure Data Factory 2.0 comes the new Integration Runtime that provides you with the infrastructure to orchestrate data movement, activity dispatching & SSIS package execution, both in Azure & on-premises.

But that's not all, there is more - Http activity support, integration with Azure Monitor, integration with Azure Key Vault, and much more! We'll dive deeper into this announcement in a later article.

Additional resources:

Azure DDOS Protection Service (Preview)

Distributed Denial-Of-Service attacks can be brutal and unfortunately is very easy to use. Nowadays, you can find it on the internet as a managed offering or even do it yourself just like Troy Hunt explains.

That's why Microsoft is announcing Azure DDOS Protection Service (Preview) that allows you to protect your Virtual Networks in order to secure your Azure resources even more.

However, Microsoft Azure already brings you DDOS protection out-of-the-box. The difference here is that Azure DDOS Protection Service takes this a step further and give you more features & control.

Here is a nice comparison:

Azure DDOS Protection Service is a turn-key solution which makes it easy to use and is integrated into the Azure Portal. It gives you dedicated monitoring and allows you to define policies on your VNETs. By using machine learning it tries to create a baseline of your traffic pattern and identifies malicious traffic.

Last but not least, it also integrates with Azure Application Gateway allowing you to do L3 to L7 protection.

Additional resources:

Taking Azure Security Center to the next level

Another example of the security investment by Microsoft are there recent announcements for Azure Security Center. You can not only use it for cloud workloads but also for on-premises workloads as well.

Define corporate security standards with Azure Policy (Limited Preview)

Azure Policy allows you to define corporate standards and enforce them on your Azure resources to make sure that the resources are compliant with your standards. They also come with some default rules, such as running at least SQL Server 12.0 and can be scoped to either a management group or resource group level.

By using initiative definitions, you can group one or multiple policy definitions as a set of requirement. An example could be an initiative that consolidates all SQL database related definitions.

To summarize, Azure Policy allows you to define security standards across multiple subscriptions and/or resource groups making it easier to manage your complete infrastructure.

It is currently in limited preview but sign-up for the preview in the Azure portal.

Introduction of Security Playbooks

With the addition of Security Playbooks you can now easily integrate certain playbooks in reaction to specific Security Center alerts.

It allows you to create & link an Azure Logic Apps which orchestrates the handling of the alert, tailored to your security needs.

Investigation Dashboard

Azure Security Center now provides a new visual, interactive investigation experience to analyze alerts and determine root cause analysis.

It visualizes all relevant information linked to a specific security incident, in this case an RDP brute force attack.

It makes it a lot easier to get the big picture of the potential cause, but also the impact of the incident. By selecting certain nodes in the equasion, it provides you with more information about that specific segment. This enables you to drill deeper and get a better understanding of what is going on.

However, these are only a subset of the announcements, you can find all of them in this blog post.

Additional resources:

Introducing SQL Vulnerability Assessment (VA)

SQL Vulnerability Assessment (VA) is a new service that comes with Azure SQL Database and SQL on-premise via SQL Server Management Studio (SSMS).

It allows you to discover, track and remediate potential database vulnerabilities. You can see it as a lite version of Azure Security Center focused on SQL DBes that lists all potential vulnerabilities after running a scan.

This is another example of Microsoft making security more approachable, even if you are not a security expert. After running a scan you will probably see some quick wins making your database more secure step by step.

Additional resources:

Summary

Microsoft made some great announcements at Ignite and this is only the beginning, there were a lot more of them and I recommend read more about them on the Azure blog or watch the Ignite sessions on-demand.

Personally, I recommend Mark Russinovich' interesting talk called "Inside Microsoft Azure datacenter hardware and software architecture" which walks you through how Azure datacenters work, their recent investments & achievements and what their future plans are.

Lately, the IT side of Azure is coming closer to the developer side where services such as Azure Networking is becoming easier to integrate with PaaS services such as Azure Storage & SQL DB. It looks like this is only the beginning and we can expect more of these kinds of integrations making it easier for both IT & Devs to build more secure solutions.

Last but not least, don't forget that the Azure Roadmap gives a clear overview of what service is at what stage. Here you can see all services that are in preview for example.

Thanks for reading,

Tom Kerkhove.