A number of important changes are being introduced to AzureWatch this weekend (March 16th):

• Ability to execute rules only after a sustained period of time
  • This feature allows for much better control of the scaling process.  For example, in certain situations it can be far more effective to scale when sustained load is over a certain threshold rather than try to predict what a moving average looks like.  It is important to know that if a rule is configured with a sustained time delay, it will only be executed after continuously being evaluated to TRUE for the specified period of time.
• Ability to send ON and OFF alerts (a single ON email when alert evaluates to TRUE, and a single OFF email when it evaluates to FALSE)
  • This feature reduces spam when a certain rule's condition is continuously TRUE.  It also simplifies configuration since ON/OFF alerts no longer require throttling.  Unless modified, existing Alerts will work as they currently do.
• Separation of Alerts from Management Actions (rules that notify will be separated from rules that execute scale actions, shutdowns, restarts, etc.)
  • This feature is relatively important as it may impact existing rule sets.  Going forward, rules that are Alerts will be evaluated separately from rules that are Management Actions.  When evaluating rules, all Alerts that qualify for execution will be evaluated and acted upon, not just the first one. Management Actions will continue to be evaluated until the first rule that qualifies for execution. Users who currently rely on Alerts to control execution of their Management Actions will want to revisit their scaling configurations.  We do expect percentage of such users to be either very small or non-existent.
  • After the upgrade, we plan to monitor AzureWatch's email queues and switch highly spamming Alerts to have ON/OFF logic.  Impacted customers will be notified.

While we expect minimum impact during or after the upgrade, we want to be transparent with our users: this is probably the most significant change to the Rules engine since the inception of AzureWatch. If you have any concerns, please contact Paraleap support team

We are currently working on changing the way alerts work in AzureWatch.  At this time, Rules for a monitored resource, that trigger either Alerts or Management Actions are ALL evaluated together in a single loop.  When a single Rule is evaluated to TRUE, all further evaluation of rules for that resource stops.  This will be changing going forward.  ALL rules that trigger alerts will be evaluated, regardless of their success or failure.  Only rules that contain Management Actions will be evaluated to the first TRUE rule.

Furthermore, we will be adding the following two options to the Rule engine:

1) Ability to send ON/OFF notifications for Alerts.  This means that when a condition for a Rule is TRUE, AzureWatch will send out an "Alert ON" alert and subsequently when the condition for a Rule no longer is true, AzureWatch will send out an "Alert OFF" alert.

2) Ability to trigger Rules (Alerts or Management Actions) only after a sustained period of time.  This means that when a condition is TRUE, a particular Rule will not be immediately acted upon.  Only if Rule's condition has been evaluated as TRUE for the specified period of time, will it trigger it's Alert or Management Action.

We are excited to introduce a new AzureWatch feature: daily performance charts delivered via email to our users.  This feature was requested rather frequently in the recent months, and we are happy to oblige.  No action is required to enable this feature.  Every active account (whether trial or paid) will automatically begin receiving daily reports free of charge.

We know that this new capability will provide AzureWatch users with greater insight into the performance of their applications running on top of the Windows Azure platform.

This article will describe techniques that we use to manage configuration changes among various projects in a large distributed Azure-based solution called AzureWatch and keep settings in sync as various environments are targeted during development, testing, and debugging sessions.  AzureWatch is an auto-scaling and monitoring system for Windows Azure applications.  By its nature it utilizes multiple WCF services, a few Windows-based clients and services, a number of web-based projects.  As readers can imagine, keeping all configuration settings in sync and "shifting" them together on demand without making a mistake can be challenging.

Visual Studio technologies utilized

• Using Visual Studio Configurations to define target debugging environments
• Using ConfigSource to split configuration files
• Pre/Post build events
• Add existing files as Links to a Project
• Config Transformations

Approach at a high level

For every debugging environment, create Visual Studio Configuration.  Break out sections from every .Config file that vary by environment and include them back via ConfigSource settings.  Include these sections as unique files into a separate Config project that has folders for every Configuration.  Also, copy *.csdef and *.csccfg files from Azure-related projects into these folders as well. In each folder, customize the broken off partial configuration files (that are included via ConfigSource) and azure configuration files. Create Post or Pre-Build event of the Config project to xcopy the files from a folder that matches current Configuration environment to root folder of Config project.  Include files from root folder of Config project into other projects as needed via “Add as Link” command.

Utilize Config transformations to instrument Production (and other environments if needed) specific configuration changes that deal with security, debugging, tracing, etc.

Now, let's look at these steps in detail:

Step 1

Create as many Visual Studio Configurations as necessary to support number of different environments that developers must be able to debug from Visual Studio

Step 2

Create a separate empty project that is a part of the overall solution called Config.  Include into the project folders, one per matching Visual Studio Configuration environment that were created in Step 1

Step 3

Break-off Connection strings, end-point configuration, and other environment-specific sections from existing .config files into separate “include-only” config sections.

Source example web.config:

  <connectionStrings configSource="config\connectionStrings.config"/>

Broken off example connectionStrings.config:

  <connectionStrings>

    <add connectionString="..." name="db_connection"></add>

  </connectionStrings>

May want to make sure that web-projects have "bin" folder as a part of the relative path: "\bin\config\partialConfigFileName.config"

Step 4

Include separate customized partial .config files and Azure-specific .cscfg files under every sub-folder within Config project

Step 5

Customize Pre-Build event for Config project to copy config files for current configuration to root folder:

xcopy /Y /R "$(ProjectDir)$(ConfigurationName)\*.config" "$(ProjectDir)"

xcopy /Y /R "$(ProjectDir)$(ConfigurationName)\*.cscfg" "$(ProjectDir)"

Step 6

Add-as-Link partial .config files into main projects from the ROOT of the Config project.  To keep things organized, feel free to include partial .config files under Config sub-folders.  Do the same with Azure-specific .cscfg files

Files added as links have a shortcut icon in Solution Explorer

Step 7

Manually add project dependencies to Config project from the other projects in the solution, that need partial config files to function.  This will ensure that Config project is built before other projects.  Project Dependencies can be found inside Solution Properties window, under "Project Dependencies" tab.

Do not forget to specify that partial config files set "Copy to Output Directory" as "Copy if Newer"

Conclusion

Now, every time you debug and run, the pre-compile event from Config project will copy all the files from its particular environment folder into its root folder.  And all other projects that link to this root folder for their partial .config files will automatically get updated .configs switched to proper environment in unison.

Revisions

This blog was revised on 06/10/2012 to include the extra XCOPY step that keeps Azure cscfg/csdef files in sync with the Config project

The strategies discussed in this article can be applied to any cloud platform that has an ability to dynamically provision compute resources, even though I rely on examples from AzureWatch auto scaling and monitoring service for Windows Azure

The topic of auto scaling is an extremely important one when it comes to architecting cloud-based systems.  The major premise of cloud computing is its utility based approach to on-demand provisioning and de-provisioning of resources while paying only for what has been consumed.  It only makes sense to give the matter of dynamic provisioning and auto scaling a great deal of thought when designing your system to live in the cloud.  Implementing a cloud-based application without auto scaling is like installing an air-conditioner without a thermostat: one either needs to constantly monitor and manually adjust the needed temperature or pray that outside temperature never changes.

Many cloud platforms such as Amazon's EC2 or Windows Azure do not automatically adjust compute power dedicated to applications running on their platforms.  Instead, they rely upon various tools and services to provide dynamic auto scaling.  For applications running in Amazon cloud, auto-scaling is offered by Amazon itself via a service CloudWatch as well as third party vendors such as RightScale.  Windows Azure does not have its own auto scaling engine but third party vendors such as AzureWatch can provide auto scaling and monitoring.

Before deciding on when to scale up and down, it is important to understand when and why changes in demand occur.  In general, demand on your application can vary due to planned or unplanned events.  Thus it is important to initially divide your scaling strategies into these two broad categories: Predictable and Unpredictable demand.  

The goal of this article is to describe scaling strategies that gracefully handle unplanned and planned spikes in demand.  I'll use AzureWatch to demonstrate specific examples of how these strategies can be implemented in Windows Azure environment.  Important note: even though this article will mostly talk about scale up techniques, do not forget to think about matching scale down techniques. In some cases, it may help to think about building an auto scaling strategy in a way similar to building a thermostat.

Unpredictable demand

Conceptualizing use-cases of rarely occurring unplanned spikes in demand is rather straight forward.  Demand on your app may suddenly increase due to a number of various causes, such as:

• an article about your website was published on a popular website (the Slashdot effect)
• CEO of your company just ordered a number of complex reports before a big meeting with shareholders
• your marketing department just ran a successful ad campaign and forgot to tell you about the possible influx of new users
• a large overseas customer signed up overnight and started consuming a lot of resources

Whatever the case may be, having an insurance policy that deals with such unplanned spikes in demand is not just smart.  It may help save your reputation and reputation of your company.  However, gracefully handling unplanned spikes in demand can be difficult.  This is because you are reacting to events that have already happened.  There are two recommended ways of handling unplanned spikes:

Strategy 1: React to unpredictable demand

When utilization metrics are indicating high load, simply react by scaling up.  Such utilization metrics can usually include CPU utilization, amount of requests per second, number of concurrent users, amounts of bytes transferred, or amount of memory used by your application.  In AzureWatch you can configure scaling rules that aggregate such metrics over some amount of time and across all servers in the application role and then issue a scale up command when some set of averaged metrics is above a certain threshold.  In cases when multiple metrics indicate change in demand, it may also be a good idea to find a "common scaling unit", that would unify all relevant metrics together into one number.

Strategy 2: React to rate of change in unpredictable demand

Since scale-up and scale-down events take some time to execute, it may be better to interrogate the rate of increase or decrease of demand and start scaling ahead of time: when moving averages indicate acceleration or deceleration of demand.  As an example, in AzureWatch's rule-based scaling engine, such event can be represented by a rule that interrogates Average CPU utilization over a short period of time in contrast to CPU utilization over a longer period of time

(Fig: scale up when Average CPU utilization for the last 20 minutes is 20% higher than Average CPU utilization over the last hour and Average CPU utilization is already significant by being over 50%)

Also, it is important to keep in mind that scaling events with this approach may trigger at times when it is not really needed: high rate of increase will not always manifest itself in the actual demand that justifies scaling up.  However, in many instances it may be worth it to be on the safe side rather than on the cheap side.

Predictable demand

While reacting to changes in demand may be a decent insurance policy for websites with potential for unpredictable bursts in traffic, actually knowing when demand is going to be needed before it is really needed is the best way to handle auto scaling.  There are two very different ways to predict an increase or decrease in load on your application.  One way follows a pattern of demand based on historical performance and is usually schedule-based, while another is based on some sort of a "processing queue".

Strategy 3: Predictable demand based on time of day

There are frequently situations when load on the application is known ahead of time.  Perhaps it is between 7am and 7pm when a line-of-business (LOB) application is accessed by employees of a company, or perhaps it is during lunch and dinner times for an application that processes restaurant orders.  Whichever it may be, the more you know at what times during the day the demand will spike, the better off your scaling strategy will be.  AzureWatch handles this by allowing to specify scheduling aspects into execution of scaling rules.

Strategy 4: Predictable demand based on amount of work left to do

While schedule-based demand predictions are great if they exist, not all applications have consistent times of day when demand changes.  If your application utilizes some sort of a job-scheduling approach where the load on the application can be determined by the amount of jobs waiting to be processed, setting up scaling rules based on such metric may work best.  Benefits of asynchronous or batch job execution where heavy-duty processing is off-loaded to back-end servers can not only provide responsiveness and scalability to your application but also the amount of waiting-to-be-processed jobs can serve as an important leading metric in the ability to scale with better precision.  In Windows Azure, the preferred supported job-scheduling mechanism is via Queues based on Azure Storage.  AzureWatch provides an ability to create scaling rules based on the amount of messages waiting to be processed in such a queue.  For those not using Azure Queues, AzureWatch can also read custom metrics through a special XML-based interface.

Combining strategies

In the real world, implementing a combination of more than one of the above scaling strategies may be prudent.  Application administrators likely have some known patterns for their applications's behaviour that would define predictable bursting scenarios, but having an insurance policy that would handle unplanned bursts of demand may be important as well.  Understanding your demand and aligning scaling rules to work together is key to successful auto scaling implementation.

A question frequently asked during architectural stages of a new Azure-based project: should we choose SQL Azure or Azure Table Storage to store our data?

Answer is typically: YES. Both of these storage technologies compliment rather than compete with each other.  If you are looking to gain maximum scalability, performance, and flexibility while at the same time paying the least amount of money, the trick is to understand the strong points of each technology and utilize both effectively.

Azure Table Services (ATS) is a new storage technology from Microsoft and is specifically designed to handle mega-scalability for applications and websites of Twitter/Facebook/Amazon's capacity.  Storage space is super cheap with ATS.  To offset the low cost and mega-scalability there are a few negative impacts that one must be aware of.  You are not only paying for storing the data, but also for accessing the data that lives in ATS.  There is also only limited support for transactions.  This is key to understand and design around.  ATS also is a new paradigm for developers to grasp.  Lastly, ATS forces your compute nodes to become mini-relational servers.  It simply does not do any of the relational processing we are all used to.  JOINs, GROUP BY's, ORDER BY's all have to be designed around or performed manually. 

I would say that ATS is best suited for large amounts of data that only rarely needs to be accessed or massaged.

On the other hand, SQL Azure is a fast, lighter-weight cloud-based relational storage.  Your developers will know how to code against it right away, because (barring a few small gotchas) coding for it simply like coding for any other SQL database.  On the negative side: SQL Azure is not meant to support huge applications like Facebook, Ebay, Twitter, etc.  Azure, stores your SQL Azure databases along with others in a multi-tenant environment on same servers and thus it has to throttle access should your application become too hot and impacts other databases on the same SQL Azure node.  SQL Azure is also somewhat pricey, at $10/gigabyte/month.  Having said, there is no cost to access the data stored in SQL Azure and there is plenty of CPU power dedicated to perform relational functionality on your data.  

I would recommend that SQL Azure for smaller amounts of frequently accessed data.

A few typical use-cases to illustrate the points:

Data in a banking system that stores customer account information along side a large amount of financial transactions would best divided across SQL Azure and ATS in the following way: Customer account information in SQL Azure, Financial Transactions in ATS.

Content for a large blog site should probably live in SQL Azure until it reaches a certain age and can be archived to ATS.

So you are deploying your application on Windows Azure cloud platform.  One of the key features of a cloud platform like Azure is the ability to consume your compute resources with a utility model.  Pay for only what is used, whether storage space, compute power, or amount of data transferred. Dynamic allocation of more space or bandwidth is built-in to Azure, but with respect to compute power, Windows Azure allows you to issue scale up or scale-down commands relatively easily.  However, deciding when to do so can be a challenging task.  This blog entry describes a service AzureWatch and how it can dynamically scale Windows Azure applications.

Part One - Introduction

At its core, AzureWatch aggregates and analyzes performance counters, queue lengths, and other metrics and matches that data against user-defined rules. When a rule produces a "hit", a scaling action or a notification occurs.  You will need an account to install and use AzureWatch.  Follow this link to fill out a simple registration form.  After registration, download link for Windows-based configuration utility will be provided.

AzureWatch currently ships in two flavors: desktop edition and server-side edition.  Server-side AzureWatch will monitor and auto-scale your Azure applications from its cloud-based servers.  Desktop edition requires a special AzureWatch Monitoring agent to be installed on your premises.  In desktop-edition the agent is responsible for gathering metrics and initiating scaling events.

Part Two - Start Control Panel

After installation is complete, start AzureWatch ControlPanel and login with your newly created account.  You will be presented with a wizard to enter your Azure connection information. 

Subscription ID can be found on your Windows Azure developer portal.  If you do not already have the X.509 certificate, AzureWatch can create one for you.  Follow the hyperlink on the wizard to get detailed instructions on creation certificates.  It is a good idea to visit AzureWatch page to understand how your certificates and storage keys are kept secure.

After entering your account SubscriptionID and specifying a valid X.509 certificate, press Connect to Azure.  You will be presented with a list of storage accounts.  Storage account that is monitored by your Diagnostics Monitor is required.

On the next wizard page you can validate default settings for such things as throttle times, notification email, etc.

After the connection wizard is completed, AzureWatch will figure out what services, deployments and roles are present.  For each role found, you will be offered a chance to create simple predefined rules.

The few sample rules offered are simple rules that rely upon basic metrics. We will come back to these rules in a short while.  For now, wizards need to be completed.

Part Three - First time in Control Panel

After wizards complete, you are presented with a dashboard screen.  It likely contains empty historical charts since no data has been collected so far.  Navigation Explorer on the left shows various parameters that can be customized, while Instructions tab on the right shows context-sensitive instructions.

It is a good idea to visit the the Rules section to see the rules that have been defined by the wizard.  Two sample rules should be present and can be edited by double-clicking on each.  Rule Edit screen is simple yet powerful.  You can specify what formula needs to be evaluated, what happens when the evaluation returns TRUE, and what time of day should evaluation be restricted to.  To make formula entry easier, a list of already defined aggregated metrics is provided.  Hovering over the formula box will display allowed operands.

One last place to visit before starting the monitoring process is the screen that contains safety limits for the number of instances that AzureWatch can scale up to or down to.  By clicking on the appropriate Role name in the navigation explorer, you will be presented with a chance to modify these boundaries.

This is it.  If you are ready, press "Publish Changes" button.  Provided your AzureWatch Monitor service is running, it will pick up these configuration settings in the next iteration of its internal loop and instruct Azure Diagnostics Manager to start capturing the metrics required for formulas to work.  Windows Azure will need a few minutes to instruct your instances to start capturing those metrics afterwards, and then a few minutes more before these metrics will be transferred to your storage.  Thus, give AzureWatch at least 5-10 minutes before expecting to see anything on the Dashboard screen.

Part Four - A few tips and tricks

Some things to keep in mind while using AzureWatch

If you just started using AzureWatch and have not accumulated enough metric data, evaluation of your rules may be suspect as your aggregations will lack sufficient data.  It maybe prudent to disable scaling inside Rules in the beginning so that your scaling actions do not trigger unexpectedly.

Metric transfer occurs only when Monitor is running.  If you stopped Monitor service for an hour and then restarted it, it does not "go back" and send the missing hour's worth of metrics to AzureWatch.

When deploying new packages into Azure, you must use do it thru the same storage account that is specified in your DiagnosticsConnectionString and in AzureWatch System Settings.  Failure to do this, will result in incomplete or incorrect metrics.

AzureWatch will always instruct your instances to capture metrics that are defined in the Raw Metrics screen.  You do not need to do anything special with existing or newly started instances.  It may be worthwhile, however, to visit the System Settings screen to further configure how metric enforcement and gathering works.

AzureWatch will send a notification when it scales your instances up or down.  In it, it will provide values for all the aggregated metrics it knows about to help you understand why the scaling event occurred.

Locally installed AzureWatch components automatically self-update whenever a new version is released.

Paraleap Technologies is proud to announce that it became a partner in Microsoft® BizSpark™ program designed to accelerate the success of emerging startups by providing key resources such as software, support and visibility.

“We are very excited to participate in the BizSpark program,” says Igor Papirov, Founder of Paraleap Technologies. "Thanks to the program we now have better access to cutting edge software and services and can concentrate on building cutting edge products targeting Microsoft Windows Azure platform"


About Paraleap Technologies

Founded in 2010, Paraleap Technologies is an emerging Chicago-based startup, focused on providing tools and services for cloud computing technologies.
AzureWatch is Paraleap’s flagship product, designed to add dynamic scalability and monitoring to applications running in Microsoft Windows Azure cloud platform.