I am evaluating pros / cons of Functions Vs RunBooks to Autoscale PaaS Services (HDI, AppService, SQLDB, SQLDataWarehouse, Batch etc). Scaling should happen based on schedule / workload.
What's the best practice to go forward between both technologies. Any known issues / limitations I should be aware.
Related
To automatically manage Cloud resources in order to meet the felt need of my infrastructure, I need to increase VM resources. But this is possible only while the machine status is TERMINATED.
The problem is that I have got applications on the VMs that must not stop running. Do you have any suggestions about how I could proceed, like increasing my machines resources without interrupting its services? (database, web, etc...)
The purpose of that is to automate my whole infrastructure, to ensure its quality of service even if I'm not monitoring it by myself.
I suggest to take a look to Managed Instance Groups. It will offer some of the characteristics you need: high availability, scalability and the ability to add the instance group to a load balancer.
According to the Google official documentation about MIGs:
Make your workloads scalable and highly available by taking advantage of automated MIG services, including: autoscaling, autohealing, regional (multiple zone) deployment, and automatic updating.
Regarding the need of automation of the services you want, I suggest to generally use fully managed services. You can check a summary of GCP services and you can always inspect if they fit your demands.
I can choose to pay more to have dedicated AWS EC2 instances so that my VMs are physically isolated from other people's instances.
However, using EC2 also means I bear the responsibility of maintenance, either through automation or not.
So I would like to use things like Fargate and Lambda, which removes the maintenance burden from me.
Is possible to still have the same level of hardware isolation?
Can I require Amazon to run my Lambda functions and Fargate containers in a physically isolated fashion?
It is not possible as far as I know.
Pulling from the documentation of AWS
For FarGate
Ensure that the VPC that you choose is not configured to require dedicated hardware tenancy, as that is not supported by Fargate tasks.
And at the moment, Lambda also share resource. One Lambda invocation takes up some part of the big chip's CPU time and I do not think they will roll dedicated Lambda out soon as It's one of the reason they can offer computational power that cheap ( keeping their hardware busy serving multiple people )
Also from the docs
Lambda doesn't currently support running in dedicated tenancy
Context :
We are prototyping a multi cloud deployment of our application (based on micro services).
For balancing between high availability and co location we used "Availability Sets" feature in Azure. Which kind off ensures that Azure platform/service upgrades doesn't happen in two distinct sets simultaneously.
Availability sets Azure
Scenario :
I couldn't find anything similar in Google Cloud Platform and AWS. So in this case we have to go with separate "Zones" for high availability.
One argument in favor of Availability sets ( theoretically) are they are kind of more closer that Zones as the former is inside an data center.
Do we have anything close to "availability sets" in GCP and AWS. Please share your thoughts.
Regarding GCP, there are several solutions for high-availability. In general it is recommended to Design Robust Systems prone to failures and Building scalable and resilient applications.
By designing robust systems you are insuring that your VMs are available in case of single instance failure, reboot of the instance or if there is an issue with the zone.
What looks most similar to Availability Sets is Managed Instance Groups.
The managed instance group auto-updater allows you to deploy new versions of software to instances in your MIG, supporting different rollout scenarios (rolling updates, canary updates). You can control the speed and scope of deployment as well as the level of disruption to your service.
Also you can use Regional Persistent Disk that replicates data across zones (datacenters).
It sounds like Placement Groups may be an equivalent feature in AWS. There are a few different configurations where you can ask AWS to cluster your instances very close to maximize network I/O performance or spread your instances across hardware to reduce correlated failures.
Cluster – packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication that is typical of HPC applications.
Partition – spreads your instances across logical partitions such that groups of instances in one partition do not share the underlying hardware with groups of instances in different partitions. This strategy is typically used by large distributed and replicated workloads, such as Hadoop, Cassandra, and Kafka.
Spread – strictly places a small group of instances across distinct underlying hardware to reduce correlated failures.
I can't speak for Google Cloud as I am not aware of a similar feature but I am also not nearly as familiar with their offerings.
Hope that helps.
We are trying to evaluate the best ways to scale our J2EE web application and use hosting services with AWS. Are there reasons why we would use the Lambda service over Kubernetes (EKS)? Although it seems that Lambda can scale functional units, I'm not clear why anyone would use that as a substitute for Kubernetes, given Kubernetes can replicate containers based on performance metrics.
They serve different purposes. If you want to have horizontal scalability on a "ec2/pod/container" level and handle the availability yourself (through k8s of course), go for Kubernetes.
If you have a straight forward function doing a particular thing and you don't want to bother yourself with operating costs of having to manage a cluster or packaging it, then you can let Lambda administer it for you (at the time of writing, you would pay 20 US cents per million call). It is just another layer of abstraction on top of a system that is probably similar to Kubernetes, scaling your function per needs.
The goal of these technologies is to remove as much overhead as possible between you and the code and infrastructure can be painful. To summarize, serverless is to Kubernetes what Kubernetes is to containers.
To make a conscious decision, take the following into account:
Does your budget covers operation and maintenance of infrastructure
Do you have the expertise in Kubernetes
How much would it cost to redesign your J2EE app into serverless
ready code
Your timeline (of course...)
Based on the AWS resources you will use, how much do you save or not
by implementing a k8s cluster (database service?, EBS, EC2s, etc.)
I am at the beginning of a large migration from a single monolithic web service to a collection of microservices using Spring Cloud/Spring Cloud Netflix. Through my research of microservices I understand that the lines of demarcation between services should mirror the separations of concerns between them. An additional factor affecting separation is which services are required to scale individually.
As a concrete example, depending on the level of granularity desired, a microservice environment could end up like this:
Accounts (containing Signup, Login, Profiles, etc.)
Store (containing Products, Payments, Reporting, Inventories, etc.)
Chat/Social (containing chat rooms, user statuses, etc.)
...
Or it could end up with each of the areas of concern in brackets represented by their own microservice, e.g:
Accounts
Signup
Login
...
I believe there is a preference in the microservices community for the second approach, and I tend to agree. However, the issue I have is one of hosting and resource limitations.
In the migration I would like to streamline the provisioning of resources and the installation of updated services. Since we use the AWS stack, Elastic Beanstalk seemed like the perfect choice. While researching Elastic Beanstalk though I was rather disheartened to discover that there was a limit of 25 applications per account. Not only that, but EC2 has a limit of 20 instances per region per account. It seems like a microservice architecture will hit that limit very quickly, especially when you add multiple environments (staging and production) for each service into the mix, let alone websites and internal tooling.
With all of the amazing content that I've seen around the web regarding microservices, I'm surprised and somewhat disappointed at the lack of information regarding the actual hosting of microservices beyond the development of them. Have I missed something? Is there any information about deploying more than a couple of microservices on AWS?
It is my understanding that Netflix use AWS for their own microservice hosting, beyond requesting additional resources from Amazon and throwing money at it, are there other solutions? Would their Asgard tool help with this issue (possibly by handling the sharing of instances between services) or would it result in the same outcome?
As mentioned in the above comments, AWS will raise your limits if you have a legit use case - why wouldn't they? they are in the business of selling you services.
But since you have asked for suggestion other than increasing those limits, and since you are in the early stages of designing your solution, you should consider basing part of your micro-services architecture on Docker or another container/container like service (my own preference would be for the AWS's container service). Depending on the nature of you solution, even within the limits of 20 EC2 instances (per region), if you had large enough instances running you could fit dozens (or even hundreds of lightweight) docker images running on each of those allocated 20 instances - so potentially hundres or thousands of walled off micro-services running on those 20 EC2 instances.
Using an entire EC2 image for each of many micro-services you may have may end up being a lot more expensive than it needs to be.
You should also consider the use of AWS Lamba for at least portions of your micro-service architecture - its the 'ultra-micro service' tool also offered by AWS.