Fastly this week announced it is adding the ability to collect tracing, customizable logging data and both real-time and historical metrics from a serverless computing framework designed to be deployed on edge computing platforms.
Company CTO Tyler McMullen said the goal is to make it easier for IT to bring observability within the context of a DevOps to an edge computing platform.
The [email protected] serverless compute platform enables IT teams to employ functions to execute code on an edge computing platform versus having to install virtual machines or containers. Those edge computing platforms are connected to a content delivery network (CDN) managed by Fastly.
McMullen said that approach provides a way to deploy microsecond applications at the edge while still attaining observability in keeping with best DevOps practices. Otherwise, edge computing platforms become yet another black box that provides IT teams with no visibility.
DevOps teams can send real-time logs to 27 and counting supported endpoints. Default log fields are automatically exposed to better determine the root cause of any issue. DevOps teams can also capture custom event details.
Metrics such as CPU and RAM utilization can also be surfaced either in real-time or on a historical basis. The [email protected] service also honors request tracing parameters by maintaining them when they enter and leave the Fastly platform. Developers can tag individual end user requests with unique identifiers that can be shared with third-party platforms such as Datadog.
Currently available in beta, [email protected] makes it easier for IT teams to employ a serverless computing framework to drive event-driven applications at the edge, McMullen said. Latency issues that arise when applications attempt to access cloud services are pushing more organizations to deploy real-time applications at the network edge. Functions running on a local serverless computing platform enable IT teams to achieve that goal in a way that consumes the smallest amount of IT infrastructure possible, he added.
With the rise of 5G networking services, it’s now only a matter of time before the number of platforms on which a DevOps team needs to deploy code expands significantly. Most DevOps teams are already challenged by all the unique platforms to which need to deliver code. Edge computing could potentially exacerbate those issues because each edge computing platform tends to have unique attributes. A common serverless computing framework running on each of those edge computing platforms could help streamline continuous delivery.
Of course, it’s too early to say what impact the downturn on the economy will have on edge computing projects. In theory, at least, organizations are gearing up to deploy these applications in anticipation of wireless 5G connectivity services being widely available and affordable. Chances are high, however, with more organizations not willing to send people to install applications on those endpoints manually, reliance on DevOps processes and CDNs to automate application delivery at the edge will become a requirement.