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Archive for the ‘GCP’ Category

Smart use of cloud services

Many organizations are talking and beginning to embrace system migration to the cloud, as part of a global IT strategy to use public clouds.
The migration from the on premise to the public cloud, allows organizations to choose from a variety of alternatives, each with its own benefits, knowledge requirement and different pricing model (as compared to the on premise licensing model).
In this article, I shell review the different methods, with their pros and cons and I will try to explain which method suites which scenario.

Lift & Shift Migration
In this method, the customer chooses an existing system from the on premise environment, reviewing the required resources for running the system in a cloud environment (number of CPU/amount of Memory and required disk space), the operating system version (assuming the cloud provider has an image for this operating system), checking with the software vendor the ability to run the system a cloud environment (licensing issues) and deploying all software components on a virtual server in the cloud environment (or migrating the entire system, assuming the cloud provider offers a managed service for system migration).

This is the most common method and the simplest one (comparing to other alternatives…) for migrating to the cloud, and most cloud vendors (Infrastructure as a Service) support this method, but we should take under consideration, that cost-wise, this method is considered expensive (in terms of cost and resource usage) when comparing to purchasing physical hardware for 3-5 years in advanced in the on premise environment.
The most common ways to lower the server cost are:
• Resizing the server size (number of CPU/amount of memory) to the actual usage
• Purchase reserved instance for 1 or 3 years in advanced
• Using Spot instances for servers who does not require availability of 24×7 or for applications who can survive temporary downtime, without harming the entire service

Moving to Micro-Services and Containers
In this method, the organization begins migration from monolith application (a system where all components relay on each other and required to be deployed together) to micro-services based development, where each component runs independently (usually inside its own container) and it can be replaced, upgraded and vertically scale out as needed and independently from the rest of the system components.
It is possible to run containers on virtual servers (the entire management, update and scale is the customer’s responsibility) or as part of a managed service (such as managed Kubernetes clusters service).
This method requires the developer’s teams to know how to package their applications inside containers, take care of scaling, monitoring of containers activities (and the communications between containers), and taking care of security topics (such as who can access a container or whether or not the communication between containers is encrypted, etc.)
This method is suitable for organizations who wish to change their current application architecture or being developing new applications. Modern applications are being developed today as containers and allows the customer to migrate between the on premise environments to the public cloud, and with proper adjustments between different cloud providers (once we solve topics such as connectivity to current cloud vendor’s services such as message queuing, storage, logging, etc.)

Moving to Serverless / Function as a Service
In this method, the customer isn’t in charge of operating system maintenance, system availability or scale. Due to the fact that the entire infrastructure is been managed by the cloud vendor, the vendor takes care of scale, as required by the application needs.
This method is suitable for event based services, with short running time (few seconds to few minutes). As part of moving to modern applications, there are many scenarios for choosing specific development language, uploading the code to a managed cloud environment (Serverless), selecting the desired compute power (amount of memory, which effects the number of CPU) and creating triggers for running the function.
It is possible to embed Serverless capabilities, as part of modern micro-services architecture.
The pricing model for this method is based on the amount of time the function was running and the amount of memory used for running the function.
Common use cases for Serverless – image processing, data analysis from IoT devices, etc.
This method is not suitable for every application (due to short running intervals), and also not every development language is currently been supported by every cloud vendor.
For example:
• AWS Lambda (currently) support natively the following languages: Java, Go, PowerShell, Node.JS, C#, Python, Ruby
• Azure Functions (currently) support natively the following languages: Java, JavaScript, C#, PowerShell, Python, TrueScript
• Google Cloud Functions (currently) support natively the following languages: Python, Go, Node.JS
• Oracle Functions (currently) support natively the following languages: Java, Python, Node.JS, Go, Ruby

Migration to managed services (SaaS / PaaS)
In this method, the organization chooses an existing SaaS (such as Messaging, CRM, ERP, etc.) or existing PaaS (such as Database, Storage, etc.)
This method suites many scenarios in which the organization would like to consume existing service, without the need to maintain the infrastructure (operating system, storage, backup, security aspects, etc.). After choosing an existing service, the organization begin migrating data to the managed service, configure proper access rights, sometimes configure VPN connectivity between the on premise and the cloud environment, configures backup (according to the service support this capability) and being consuming the service.
The pricing model changes between cloud vendors (sometime is it based on monthly pricing and sometimes it is based on consumed compute power or consumed storage space).
Mature and transparent the cloud vendors, reveal accurate monthly billing information.
Also, mature cloud vendors knows how to handle privacy, low and regulation aspects (such as GDPR compliance and other privacy regulations) using data processing agreements.

Summary
In this article, I have reviewed the various methods of using cloud service wisely. As we can see, not every method suites every scenario or every organization, but there is no doubt that the future is heading cloud and managed services.
My recommendation for organizations – focus on what brings your organization business value (such as banking, commerce, retail, education, etc.), train your IT and development teams on the coming technological changes and migrate the responsibility for maintaining your organization infrastructure to vendors who specialized on the topic.

Benefits of using managed database as a service in the cloud

When using public cloud services for relational databases, you have two options:

  • IaaS solution – Install a database server on top of a virtual machine
  • PaaS solution – Connect to a managed database service

In the traditional data center, organizations had to maintain the operating system and the database by themselves.

The benefits are very clear – full control over the entire stack.

The downside – The organization needs to maintain availability, license cost and security (access control, patch level, hardening, auditing, etc.)

Today, all the major public cloud vendors offer managed services for databases in the cloud.

To connect to the database and begin working, all a customer needs is a DNS name, port number and credentials.

The benefits of a managed database service are:

  • Easy administration – No need to maintain the operating system (including patch level for the OS and for the database, system hardening, backup, etc.)
  • Scalability – The number of virtual machines in the cluster will grow automatically according to load, in addition to the storage space required for the data
  • High availability – The cluster can be configured to span across multiple availability zones (physical data centers)
  • Performance – Usually the cloud provider installs the database on SSD storage
  • Security – Encryption at rest and in transit
  • Monitoring – Built-in the service
  • Cost – Pay only for what you use

Not all features available on the on-premises version of the database are available on the PaaS version, and not all common databases are available as managed service of the major cloud providers.

Amazon RDS

Amazon managed services currently (as of April 2018) supports the following database engines:

Azure Managed databases

Microsoft Azure managed database services currently (as of April 2018) support the following database engines:

Google Cloud SQL

Google managed database services currently (as of April 2018) support the following database engines:

Oracle Database Cloud Service

Oracle managed database services currently (as of April 2018) support the following database engines: