I have an azure cloud service which scales instances out and in. This works fine using some app insights metrics to manage the auto-scaling rules.
The issue comes in when the scales in and azure eliminates hosts; is there a way for it to only scale in an instance once that instance is done processing its task?
There is no way to do this automatically. Azure will always scale in the highest number instance.
The ideal solution is to make the work idempotent and chunked so that if an instance that was doing some set of work is interrupted (scaling in, VM reboot, power loss, etc), then another instance can pick up the work where it left off. This lets you recover from a lot of possible scenarios such as power loss, instead of just trying to design something specific for scale in.
Having said that, you can manually create a scaling solution that only removes instances that are not doing work, but doing so will require a fair bit of code on your part. Essentially you will use a signaling mechanism running in each instance that will let some external service (a Logic app or WebJob or something like that) know when an instance is free or busy, and that external service can delete the free instances using the Delete Role Instances API (https://learn.microsoft.com/en-us/rest/api/compute/cloudservices/rest-delete-role-instances).
For more discussion on this topic see:
How to Stop single Instance/VM of WebRole/WorkerRole
Azure autoscale scale in kills in use instances
Another solution but this one breaks an assumption that we are using Azure cloud service; if you use app services instead of the cloud service you will be able to setup auto scaling on the app service plan effectively taking care of the instance drop you are experiencing.
This is an infrastructure change so it's not a two click thing but I believe app services are better suited in many situations including this one.
You can look at some pros and cons but if your product is traffic managed this switch will not be painful.
Kwill, thanks for the links/information, the top item in the second link was the best compromise.
The process work length was usually under 5 minutes and the service already had re-handling of failed processes, so after some research it was decided to track state of when the service was processing a queue item and use a while loop in the RoleEnvironment.Stopping event to delay restart and scale-in events until the process had a chance to finish.
App Insights was used to track custom events during the on stopping event to track how often it completes vs restarts during the delay cycles.
Related
We have a service running as an Azure function (Event and Service bus triggers) that we feel would be better served by a different model because it takes a few minutes to run and loads a lot of objects in memory and it feels like it loads it every time it gets called instead of keeping in memory and thus performing better.
What is the best Azure service to move to with the following goals in mind.
Easy to move and doesn't need too many code changes.
We have long term goals of being able to run this on-prem (kubernetes might help us here)
Appreciate your help.
To achieve first goal:
Move your Azure function code inside a continuous running Webjob. It has no max execution time and it can run continuously caching objects in its context.
To achieve second goal (On-premise):
You need to explain this better, but a webjob can be run as a console program on-premise, also you can wrap it into a docker container to move it from on-premise to any cloud but if you need to consume messages from an Azure Service Bus you will need an On-Premise-Azure approach connecting your local server to the cloud with a VPN or expressroute.
Regards.
There are a couple of ways to solve the said issue, each with slightly higher amount of change from where you are.
If you are just trying to separate out the heavy initial load, then you can do it once in a Redis Cache instance and then reference it from there.
If you are concerned about how long your worker can run, then Webjobs (as explained above) can work, however, that is something I'd suggest avoiding since its not where Microsoft is putting its resources. Rather look at durable functions. Here an orchestrator function can drive a worker function. (Even here be careful, that since durable functions retain history after running for very very very long times, the history tables might get too large - so probably program in something like, restart the orchestrator after say 50,000 runs (obviously the number will vary based on your case)). Also see this.
If you want to add to this, the constrain of portability then you can run this function in a docker image that can be run in an AKS cluster in Azure. This might not work well for durable functions (try it out, who knows :) ), but will surely work for the worker functions (which would cost you the most compute anyways)
If you want to bring the workloads completely on-prem then Azure functions might not be a good choice. You can create an HTTP server using the platform of your choice (Node, Python, C#...) and have that invoke the worker routine. Then you can run this whole setup inside an image on an AKS cluster on prem and to the user it looks just like a load balanced web-server :) - You can decide if you want to keep the data on Azure or bring it down on prem as well, but beware of egress costs if you decide to move it out once you've moved it up.
It appears that the functions are affected by cold starts:
Serverless cold starts within Azure
Upgrading to the Premium plan would move your functions to pre-warmed instances, which should counter the problem you are experiencing:
Pre-warmed instances for Azure Functions
However, if you potentially want to deploy your function/triggers to on-prem, you should spin them out as microservices and deploy them with containers.
Currently, the fastest way would probably be to deploy the containerized triggers via Azure Container Instances if you don't already have a Kubernetes Cluster running. With some tweaking, you can deploy them on-prem later on.
There are few options:
Move your function app on to premium. But it will not help u a lot at the time of heavy load and scale out.
Issue: In that case u will start facing cold startup issues and problem will be persist in heavy load.
Redis Cache, it will resolve your most of the issues as the main concern is heavy loading.
Issue: If your system is multitenant system then your Cache become heavy during the time.
Create small micro durable functions. It will be not the answer of your Q as u don't want lots of changes but it will resolve your most of the issues.
We're looking for automated way to horizontally, vertically scale the pull of self hosted integration runtime virtual machines used in ADF.
Reading Microsoft docs does not provide answer.
Well, I don't have the experience, so I can only give you a theoretical answer, but maybe it's helpfull for you.
AFAIK, neither way is configurable out-of-the-box. For scale-out you'll have to deploy an additional IR machine yourself. So probably you'll want to create an image that you can provision from docker or kubernetes and has the IR and pre-requirements installed. The IR installation provides an PowerShell script that can be used to create an automated connection.
For scale-up/down, you'll have to run some script that scales your vm. In an IaaS solution (f.e.) Azure VM, that should be doable with an API call to change your VM.
For both cases you'll have to have some kind of montitor in place that monitors the IR loads and makes changess as needed. I think the measures provided in the Data Factory should do. Maybe you can use Log Analyics to monitor the loads.
I'm curious about your use case for this.
My solution is just for scaling out/in since the VM must be restarted if you are scaling up/down, which causes downtime and job failures etc.
At a high level this solution requires just 3 simple things:
Azure Metric Alert that fires when Scale-Out should occur (VM Start)
Azure Metric Alert that fires when Scale-In should occur (VM Deallocation)
Logic App that is triggered by Azure Alert and actually executes the Start/Stop of the VM, along with any other automation associated with this (eg posting to a Teams channel when Scale in/out occurs)
Here are more of the details surrounding how we setup the conditions for the alerts, but the main thing to keep in mind is (IR CPU %, IR queue length, Number of Nodes, and possibly IR Memory)
Scale-Out
Scale-In
Actions for Alerts
As you can see below we have the alert triggering 1 Logic App, using the payload that is passed to the Logic App, you can determine if the Logic App should be starting the VM, or stopping the VM. (As well as any other additional actions)
Logic App
There is a small chance that due to timing (and depending on how many ADF's the IR is shared to), that pipeline activities could be sent to Node 2 at the same time a deallocation command is sent to the VM for Node 2. I have not seen this as of yet, but adjusting the alert conditions based on your need could help avoid this. Feel free to play around with the conditions of the alerts, granularity, thresholds, etc. This is not a one size fits all solution.
When I try to scale out my Azure Web App I experience very slow response times for requests on the second or third instance of the app.
This seems to happen because the other instances were in cold mode and had to switch into hot mode once the load balancer redirected the request to them.
The problem is that in my scenario most of the time there isn't going on much on the system so probably only one instance will be used via the load balancer but approx four times a day there is a peak and I need more than one instance. But if these instances are in cold mode and had to wake up first it actually makes things worse.
The question is what to do?
I've already set the app to "always on" and ARR Affinity to "off".
In the past I've already experienced problems with my app going into some sort of sleep mode even though the app was set to "always on". I solved this by setting up a scheduler task that called the app every hour. But I don't think this would work with multiple instances anymore because the task would only call one instance and the other instances would still stay in sleep mode.
Any suggestions?
increase your app service plan(S3) and try it again. I had similar problem and this solved it.
Alternatively, you can reconfigure your scaling rules.
Consider enabling logging to debug which instances are receiving the requests and why these requests are slow.
For the comment around four times a day, you need more than once instance -- consider setting up Autoscale with recurrence profile on your app service plan to automatically scale out. You can setup autoscale rule with different instance counts based on the time of day.
Is it possible to create one or several azure VMs on my local machine? I want to create a web app and load test it locally, without the need of putting it in the cloud. I'm thinking at the following scenario: I have a local VM running a IIS server with my web app; I use a tool to generate a lot of load; I need to deploy the second VM containing the same things as the first VM. The downtime of the web app should be equal to 0(hopefully).
Clarification(update):
I want to achieve the following: create a web app and a monitoring app(CPU,Memory) and deploy them on one VM. On a load test, if the VM cannot handle it(e.g. CPU goes above 80%), I want to programmatically deploy a new VM(with the same configuration, having both the web app and the monitoring app), such that no downtime occurs.
Azure has several ways for you to host sites.
Virtual Machines is just that, normal VMs. You can create them locally and upload them, but everything is up to you, including how to handle upgrades. If that is what you need to do then I don't know how you would handle upgrades with no down time; though, you can add multiple VMs to a load balancer and then upgrade them one at a time.
It sounds like what you really want to explore is Cloud Services. You can run one or more VMs locally in the emulator, upgrade with no down time once in the cloud, implement auto scaling (you will have to use a tool or write some code).
Alternatively you may want to look at Azure Web sites, but that is a completely different concept and you can't really test load and load balancing locally the same way.
Based on your statement that you essentially want to auto-scale your application you want to look at Cloud Services with Auto Scaling. However, you can't fully test this in the cloud emulator - but you can test your logic.
Background
Azure Cloud Services is designed for this kind of thing; You don't really work with VMs in the way you may be used to, instead you create a package that Azure then deploys to as many servers as you like. Once up and running, you can manually go into the management console and increase or decrease the number of active servers simply by moving a slider. Of course, you want to do this automatically, so you have a few options.
There is a management API you can use to change the number of servers. So, it would be quite simple to write a bit of code that you spin up in another thread from WebRole.Start and that simply sits and monitors the CPU on the machine and then calls the management API to spin up a new server instance if your CPU goes over a certain treshold. Okay, locally you can only test that the call to the management API is made, you won't actually see the new server coming up. But, if you grab your free trial of Azure and just try it you will see that you really don't need to test that part - it just works.
However, in practice there is an awful lot more to auto scaling. Here are some of the things you need to consider;
Even relatively idle web servers will often spike briefly to 100% so just having a simple treshold is unlikely to be good enough; You need to decide on how long the server needs to be over a certain treshold before you spin up another server instance.
What happens when you have more than one server? And, on Azure, you should always have at least two servers to ensure you have resilience. Note that the idea with Cloud Services really is to have many small servers rather than a few big servers. You pay per core, not per number of servers.
Imagine you currently have three servers and one is really busy for some reason and the other two are idle. Do you want to spin up a fourth server?
Imagine you currently have two servers and they are both quite busy. Do you really want them both to start a new server so you end up with four servers running?
There are several ways to handle these challenges. For starters, rather than having monitor programs running locally on each server, you are better of moving that monitoring outside; Azure comes with the ability to dump performance metrics to table storage at whatever interval you choose. You can then run an external program that retrieves the performance data over time from all your current servers and then reason about the overall workload before deciding to spin up or shut down additional servers. Now, you can of course host that external monitor program in a separate thread on each of your webroles to give your monitoring resilience - but the key point is that the monitoring program doesn't monitor the server it runs on, it monitors all the servers. You will, of course, still have to deal with stopping multiple monitoring program instances from all starting and stopping servers. One way to do is to place stop/start commands onto an Azure "message queue" (there are a few different types) and use the built-in "de-duper" which will automatically delete identical commands that are put on the queue within a certain time window (I am over simplyfing but you get the idea).
The actual answer
Really, though, you want to look at the Auto Scaling Application Block which will do most of this for you. I guess that is the real answer to your question, but I wanted to provide a bit of context first.
Again, I recognise you asked for how to test this locally - but I believe that that question doesn't really make sense in the context of Azure and I hope the above information helps.
I'm pretty sure you can't do that and it wouldn't make sense anyway. If you want load testing, you need to run that in an environment as similar to production as possible and that means you have to run your application is Azure cloud. How else do you know that the load will actually be processed fine on real cloud?
I'm running a Windows Azure web role which, on most days, receives very low traffic, but there are some (foreseeable) events which can lead to a high amount of background work which has to be done. The background work consists of many database calls (Azure SQL) and HTTP calls to external web services, so it is not really CPU-intensive, but it requires a lot of threads which are waiting for the database or the web service to answer. The background work is triggered by a normal HTTP request to the web role.
I see two options to orchestrate this, and I'm not sure which one is better.
Option 1, Threads: When the request for the background work comes in, the web role starts as many threads as necessary (or queues the individual work items to the thread pool). In this option, I would configure a larger instance during the heavy workload, because these threads could require a lot of memory.
Option 2, Self-Invoking: When the request for the background work comes in, the web role which receives it generates a HTTP request to itself for every item of background work. In this option, I could configure several web role instances, because the load balancer of Windows Azure balances the HTTP requests across the instances.
Option 1 is somewhat more straightforward, but it has the disadvantage that only one instance can process the background work. If I want more than one Azure instance to participate in the background work, I don't see any other option than sending HTTP requests from the role to itself, so that the load balancer can delegate some of the work to the other instances.
Maybe there are other options?
EDIT: Some more thoughts about option 2: When the request for the background work comes in, the instance that receives it would save the work to be done in some kind of queue (either Windows Azure Queues or some SQL table which works as a task queue). Then, it would generate a lot of HTTP requests to itself, so that the load balancer 'activates' all of the role instances. Each instance then dequeues a task from the queue and performs the task, then fetches the next task etc. until all tasks are done. It's like occasionally using the web role as a worker role.
I'm aware this approach has a smelly air (abusing web roles as worker roles, HTTP requests to the same web role), but I don't see the real disadvantages.
EDIT 2: I see that I should have elaborated a little bit more about the exact circumstances of the app:
The app needs to do some small tasks all the time. These tasks usually don't take more than 1-10 seconds, and they don't require a lot of CPU work. On normal days, we have only 50-100 tasks to be done, but on 'special days' (New Year is one of them), they could go into several 10'000 tasks which have to be done inside of a 1-2 hour window. The tasks are done in a web role, and we have a Cron Job which initiates the tasks every minute. So, every minute the web role receives a request to process new tasks, so it checks which tasks have to be processed, adds them to some sort of queue (currently it's an SQL table with an UPDATE with OUTPUT INSERTED, but we intend to switch to Azure Queues sometime). Currently, the same instance processes the tasks immediately after queueing them, but this won't scale, since the serial processing of several 10'000 tasks takes too long. That's the reason why we're looking for a mechanism to broadcast the event "tasks are available" from the initial instance to the others.
Have you considered using Queues for distribution of work? You can put the "tasks" which needs to be processed in queue and then distribute the work to many worker processes.
The problem I see with approach 1 is that I see this as a "Scale Up" pattern and not "Scale Out" pattern. By deploying many small VM instances instead of one large instance will give you more scalability + availability IMHO. Furthermore you mentioned that your jobs are not CPU intensive. If you consider X-Small instance, for the cost of 1 Small instance ($0.12 / hour), you can deploy 6 X-Small instances ($0.02 / hour) and likewise for the cost of 1 Large instance ($0.48) you could deploy 24 X-Small instances.
Furthermore it's easy to scale in case of a "Scale Out" pattern as you just add or remove instances. In case of "Scale Up" (or "Scale Down") pattern since you're changing the VM Size, you would end up redeploying the package.
Sorry, if I went a bit tangential :) Hope this helps.
I agree with Gaurav and others to consider one of the Azure Queue options. This is really a convenient pattern for cleanly separating concerns while also smoothing out the load.
This basic Queue-Centric Workflow (QCW) pattern has the work request placed on a queue in the handling of the Web Role's HTTP request (the mechanism that triggers the work, apparently done via a cron job that invokes wget). Then the IIS web server in the Web Role goes on doing what it does best: handling HTTP requests. It does not require any support from a load balancer.
The Web Role needs to accept requests as fast as they come (then enqueues a message for each), but the dequeue part is a pull so the load can easily be tuned for available capacity (or capacity tuned for the load! this is the cloud!). You can choose to handle these one at a time, two at a time, or N at a time: whatever your testing (sizing exercise) tells you is the right fit for the size VM you deploy.
As you probably also are aware, the RoleEntryPoint::Run method on the Web Role can also be implemented to do work continually. The default implementation on the Web Role essentially just sleeps forever, but you could implement an infinite loop to query the queue to remove work and process it (and don't forget to Sleep whenever no messages are available from the queue! failure to do so will cause a money leak and may get you throttled). As Gaurav mentions, there are some other considerations in robustly implementing this QCW pattern (what happens if my node fails, or if there's a bad ("poison") message, bug in my code, etc.), but your use case does not seem overly concerned with this since the next kick from the cron job apparently would account for any (rare, but possible) failures in the infrastructure and perhaps assumes no fatal bugs (so you can't get stuck with poison messages), etc.
Decoupling placing items on the queue from processing items from the queue is really a logical design point. By this I mean you could change this at any time and move the processing side (the code pulling from the queue) to another application tier (a service tier) rather easily without breaking any part of the essential design. This gives a lot of flexibility. You could even run everything on a single Web Role node (or two if you need the SLA - not sure you do based on some of your comments) most of the time (two-tier), then go three-tier as needed by adding a bunch of processing VMs, such as for the New Year.
The number of processing nodes could also be adjusted dynamically based on signals from the environment - for example, if the queue length is growing or above some threshold, add more processing nodes. This is the cloud and this machinery can be fully automated.
Now getting more speculative since I don't really know much about your app...
By using the Run method mentioned earlier, you might be able to eliminate the cron job as well and do that work in that infinite loop; this depends on complexity of cron scheduling of course. Or you could also possibly even eliminate the entire Web tier (the Web Role) by having your cron job place work request items directly on the queue (perhaps using one of the SDKs). You still need code to process the requests, which could of course still be your Web Role, but at that point could just as easily use a Worker Role.
[Adding as a separate answer to avoid SO telling me to switch to chat mode + bypass comments length limitation] & thinking out loud :)
I see your point. Basically through HTTP request, you're kind of broadcasting the availability of a new task to be processed to other instances.
So if I understand correctly, when an instance receives request for the task to be processed, it pushes that request in some kind of queue (like you mentioned it could either be Windows Azure Queues [personally I would actually prefer that] or SQL Azure database [Not prefer that because you would have to implement your own message locking algorithm]) and then broadcast a message to all instances that some work needs to be done. Remaining instances (or may be the instance which is broadcasting it) can then see if they're free to process that task. One instance depending on its availability can then fetch the task from the queue and start processing that task.
Assuming you used Windows Azure Queues, when an instance fetched the message, it becomes unavailable to other instances immediately for some amount of time (visibility timeout period of Azure queues) thus avoiding duplicate processing of the task. If the task is processed successfully, the instance working on that task can delete the message.
If for some reason, the task is not processed, it will automatically reappear in the queue after visibility timeout period has expired. This however leads to another problem. Since your instances look for tasks based on a trigger (generating HTTP request) rather than polling, how will you ensure that all tasks get done? Assuming you get to process just one task and one task only and it fails since you didn't get a request to process the 2nd task, the 1st task will never get processed again. Obviously it won't happen in practical situation but something you might want to think about.
Does this make sense?
i would definitely go for a scale out solution: less complex, more manageable and better in pricing. Plus you have a lesser risk on downtime in case of deployment failure (of course the mechanism of fault and upgrade domains should cover that, but nevertheless). so for that matter i completely back Gaurav on this one!