The Software Architects' Newsletter
May 2023
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Welcome to the InfoQ Software Architects' Newsletter! Each month, we bring you essential news and experience from industry peers on emerging patterns and technologies.

This month, we focus on "Edge Computing: The Evolution of the Decentralized Cloud". These core topics span the entire "diffusion of innovation" graph in our 2023 Software Architecture and Design InfoQ Trends Report. We see increasing adoption of decentralized applications (dApps), Wasm at the edge, and correctly built distributed systems.

Key challenges remain in this space, including matching user requirements with project goals (i.e., is decentralization a common need, as seen with the inability of Blockchain to become mainstream), assembling and using edge development toolchains, and running and observing decentralized applications.


Vercel Announces New Storage and Security Offerings for the Edge

Vercel recently announced a suite of serverless storage offerings for their cloud platform with Vercel KV, Postgres, and Blob, powered by the company’s infrastructure partners, Neon and Upstash. In addition, the company also launched Vercel Secure Compute, Visual Editing, and Spaces.

The new storage offerings are intended to provide frontend developers building applications for the edge with fast and efficient access to data. In particular, these offerings are:

  • Vercel KV (Key-Value) is a serverless Redis solution powered by Upstash, allowing developers to create Redis-compatible databases that can be written to and read from Vercel’s Edge Network in regions they specify.
  • Vercel Postgres is a serverless SQL database built for the front end powered by Neon, providing developers with a fully managed, highly scalable, fault-tolerant database that delivers high performance and low latency for web applications.
  • Vercel Blob is a solution to upload and serve files at the edge currently in preview. The offering provides an API built entirely on top of web standards without the need to configure buckets or implement SDKs.

Location, Location, Location: MVA Considerations for Distributed Processing and Data

Working on Cloud applications can easily convince developers that the location of resources no longer matters, and as long as all the resources you need are in the cloud, this is largely true. But throw a mobile application in the picture, especially an application relying on data residing in legacy data stores, and all of a sudden, the location of resources, including data, matters intensely.

This InfoQ article explores patterns and tactics related to distributing computing workloads and related data while discussing the issues that Minimum Viable Architecture (MVAs) must consider when distribution comes into play (and it almost always does.)

Huawei Open Sources Kuasar, a Rust-Based Container Runtime

During the KubeCon + CloudNativeCon EU 2023 event, Huawei announced that it had open-sourced Kuasar, a Rust-based container runtime that supports multiple types of sandboxes, including microVMs, Linux containers, app kernels, and WebAssembly (Wasm) runtimes that can be valuable for edge use cases.


Case Study

Data Patterns for the Edge: Data Localization, Privacy Laws, and Performance

With the advent of local data-privacy laws, competition is growing to get robust data experiences closer to the end users. So let's look at different enterprise data patterns (like synchronous data retrieval, subsequent data retrieval, and prefetch data retrieval) and see how the data can be ported to the Edge without the complexities of cloning the entire architecture.


The figure shown above shows the existing data at the enterprise level. Service A is an abstract representation of multiple services. Service B represents the service tier accessing the data locally to the data center. Service C is an abstract of all services accessing the data from external third-party providers. The data retrieval across these services can internally be classified as:

  • Synchronous Data Retrieval. All user data is retrieved in a single or parent request—this could be the initial HTML payload or service calls from the native mobile application. An example would be any transactional experience or personalized user experience: an enhanced version is chunked progressively or paginated to the end user
  • Subsequent Data Retrieval. In this case, the initial critical data is retrieved, and subsequent data is retrieved over Asynchronous calls. An example would be advertisements or game titles. You would see below-the-fold recommendations (wherein the required content is made available to the end user on scroll or content below the initial screen resolution).
  • Prefetch Data Retrieval. In the third scenario, based on predictive statistics, ranking, or workflow, the user engagement time is leveraged to prefetch or load the data, which might be media resources, templates, or personalized data.

For someone unfamiliar with edge computing, anything outside your data center can be considered "the edge" of the network, given that the Software-defined networking (SDN) is closer to the end user. You or your organization qualifies for edge computing (e.g., if the compute is an interaction between the data center and edge components; unlike local computing, where much of the computing is done toward the edge nodes of the cluster).

Even before data patterns of Edge computing are explored, it's clear that the industry norm has been moving to a static data set for user devices or the browser. Unfortunately, this has resulted in an overhead of user resources and bandwidth and, at the same time, a loss of the control mechanism on the data available at some point in time.

This article discusses how Edge can keep the traditional control knobs or semantics for the engineers at data centers and not make users pay the optimization penalty. In short, the expansion of the data horizon from D space to E space is being targeted. This expansion brings impediments associated with the following:

  • Limited Infra Capex
  • Control Mechanisms
  • Fail Over
  • Observability
  • Data Purging
  • Experimentation
  • Traffic Routing and Ramp Up
  • Legacy Support

The full-length edition of this article walks through different data patterns, addresses associated problem sets, and gives insights into what technologies can be leveraged to scale for enterprise data sets.

This content is an excerpt from a recent InfoQ article by Anoop Koloth, "Data Patterns for the Edge: Data Localization, Privacy Laws, and Performanc".

To get notifications when InfoQ publishes content on these topics, follow "Edge Computing", "Distributed Systems", and "Cloud Computing" on InfoQ.

Missed a newsletter? You can find all of the previous issues on InfoQ.



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