According to the latest worldwide market study by Juniper Research, telecom network operator spending on MEC (multi-access edge computing) will increase from $2.7 billion in 2020 to $8.3 billion in 2025. You read that right: Spending will almost triple within the next four years.

MEC (formerly known as mobile edge computing) is a communications approach that enables cloud computing capabilities at the edge of a cellular network, or really any network. 

[ Also on InfoWorld: Amazon, Google, and Microsoft take their clouds to the edge ]

As the emerging 5G (fifth generation) network systems continue to roll out during

One risk in deploying fleets of powerful and flexible clusters on constantly changing infrastructure like Kubernetes is that mistakes happen. Even minute manual errors that slip past review can have substantial impacts on the health and security of your clusters. Such mistakes, in the form of misconfigurations, are reportedly the leading cause of cloud breaches, for example. And, with everything that can happen in the containerized world, these types of mistakes are virtually guaranteed to occur.

The question, then, is how developers and platform engineers can, under today’s accelerated development timeframes, minimize these errors — if not eliminate them entirely

The two most mentioned advantages of cloud-based platforms are pay-per-use billing and the ability to scale up to an almost unlimited number of resources. No more buying ahead of resource demand, attempting to guess the amount of physical hardware and software that you’ll need.

But enterprise IT needs to understand that scale and costs are coupled concepts in cloud computing. The more resources you use, either self-scaling or auto-scaling, the more you pay. How much you pay may depend on the architecture patterns as much as on the cost of the resources themselves. Here’s why.

In building cloud-based systems, I’ve

Microservices are at the heart of many cloud-native architectures, using tools such as Kubernetes to manage service scaling on demand. Microsoft has been at the forefront of much of this movement, with a deep commitment to the Cloud Native Computing Foundation and by using Kubernetes to support its hyperscale Azure and its on-premises hybrid Azure Stack.

Part of that commitment comes from its tools, with a range of different platforms and services to support cloud-native microservice development. One of those tools is Dapr, the Distributed Application Runtime, an event-driven runtime that supports creating and managing service elements using

Google Cloud Platform (GCP) is the most performant public cloud infrastructure-as-a-service (IaaS) provider for running online transactional processing (OLTP) workloads, but Amazon Web Services (AWS) remains the best value for the money.

That’s according to the 2021 Cloud Report from Cockroach Labs, the company behind the open source CockroachDB database, which recently raised a $160 million mega-round of funding.

“Declaring a winner was much harder to declare than in years past,” according to the authors of the report, as the gap on most metrics was razor thin.

The researchers benchmarked each of the major providers according to 54 machine configurations