Skip to main content

Design of Large-Scale Services on Cloud Services PART 1

Cloud computing is distributed computing; distributing computing requires thoughtful planning and delivery – regardless of the platform choice. The purpose of this document is to provide thoughtful guidance based on real-world customer scenarios for building scalable applications

Fail-safe noun. Something designed to work or function automatically to prevent breakdown of a mechanism, system, or the like.
Individuals - whether in the context of employee, citizen, or consumer – demand instant access to application, compute and data services. The number of people connected and the devices they use to connect to these services are ever growing. In this world of always-on services, the systems that support them must be designed to be both available and resilient.
The Fail-Safe initiative  is intended to deliver general guidance for building resilient cloud architectures, guidance for implementing those architectures  and recipes for implementing these architectures for specific scenarios. 

 This article focuses on the architectural considerations for designing scalable and resilient systems.
  • Decompose the Application by Workload: Defining how a workload-centric approach provides better controls over costs, more flexibility in choosing technologies best suited to the workload, and enables a more finely tuned approach to availability and resiliency.
  • Establish a Lifecycle Model: Establishing an application lifecycle model helps define the expected behavior of an application in production and will provide requirements and insight for the overall architecture.
  • Establish an Availability Model and Plan: The availability model identifies the level of availability that is expected for your workload. It is critical as it will inform many of the decisions you’ll make when establishing your service.
  • Identify Failure Points and Failure Modes: To create a resilient architecture, it’s important to understand and identify failure points and modes. Specifically, making a proactive effort to understand and document what can cause an outage will establish an outline that can be used in analysis and planning.
  • Resiliency Patterns and Considerations: This section represents the majority of the document, and contains key considerations across compute, storage, and platform services. These considerations focus on proven practices to deliver a healthy application at key considerations across compute, storage, and platform services.
  • Design for Operations: In a world that expects services to be “always on”, it’s important that services be designed for operations. This section looks at proven practices for designing for operations that span the lifecycle, including establishing a health model to implementing telemetry to visualizing that telemetry information for the operations and developer audiences.
We will discuss in detail on the bullet point in the coming article.....keep watching

Comments

Post a Comment

Popular posts from this blog

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
Post a Comment
Read more

Kubernetes Configuration Provider to load data from Secrets and Config Maps

Using Kubernetes Configuration Provider to load data from Secrets and Config Maps When running Apache Kafka on Kubernetes, you will sooner or later probably need to use Config Maps or Secrets. Either to store something in them, or load them into your Kafka configuration. That is true regardless of whether you use Strimzi to manage your Apache Kafka cluster or something else. Kubernetes has its own way of using Secrets and Config Maps from Pods. But they might not be always sufficient. That is why in Strimzi, we created Kubernetes Configuration Provider for Apache Kafka which we will introduce in this blog post. Usually, when you need to use data from a Config Map or Secret in your Pod, you will either mount it as volume or map it to an environment variable. Both methods are configured in the spec section or the Pod resource or in the spec.template.spec section when using higher level resources such as Deployments or StatefulSets. When mounted as a volume, the contents of the Secr...
Post a Comment
Read more

Andriod Bug

A bug that steals cash by racking up charges from sending premium rate text messages has been found in Google Play.  Security researchers have identified 32 apps on Google Play that harbour the bug called BadNews. A security firm Lookout, which uncovered BadNews, said that the malicious program lays dormant on handsets for weeks to escape detection.  The malware targeted Android owners in Russia, Ukraine, Belarus and other countries in eastern Europe. 32 apps were available through four separate developer accounts on Google Play. Google has now suspended those accounts and it has pulled all the affected apps from Google Play, it added. Half of the 32 apps seeded with BadNews are Russian and the version of AlphaSMS it installed is tuned to use premium rate numbers in Russia, Ukraine, Belarus, Armenia and Kazakhstan.
Post a Comment
Read more