Software Engineering Daily

Computational load is the amount of demand that is being placed on a computer system. “Load” can take the form of memory, CPU, network bandwidth, disk space, and other finite resources.

When we design systems, we need to prepare for high-load events. On a social network, people are much more active in the mornings. On an e-commerce site, Black Friday causes many more users to come online for discount shopping. Our distributed application must be able to scale in response to these spikes in traffic.

Cloud computing has changed the popular software architecture patterns, and load balancing has changed along with it. With on-demand, infinite infrastructure, we don’t need to worry about ordering servers and provisioning. With infrastructure as code, it becomes simpler to manage lots of deployable units–so we can break up our monolith into microservices, and have hundreds or thousands of virtual machines or containers running.

Enterprises that were started before cloud computing have large on-premise server deployments–but today, many of them also use the cloud. The cloud can be used to augment their classic on-prem deployments with cloud platform-as-a-service features. The cloud can also be used as a reliable way to scale during high load events.

Today, a common architectural pattern is to have your application broken up into services. Each of those services has multiple instances. When the load on a particular service is under lots of demand, you create more instances to handle the increased load. How do you monitor the load on each service? How do you know when to spin up new instances of the service?

Load analysis and load balancing across different services can be implemented by placing “agents” throughout your infrastructure. These agents gather data about services and service instances, and route that data to a centralized place. The centralized “control plane” can be used to make decisions about load-balancing and traffic routing.

Ranga Rajagopalan worked on networking at Cisco for a decade before co-founding Avi Networks as CTO. Avi Networks builds modern load balancing software, and in today’s episode, Ranga describes the requirements of load balancing. We talked about the evolution of network infrastructure, the impact of the cloud, and the technical decisions that his team has made when architecting Avi Networks. Full disclosure: Avi Networks is a sponsor of Software Engineering Daily.

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It’s 9pm at night, and you are hungry. You order a pizza from Domino’s. You live on a street that’s dark, and so you have installed a smart lightbulb in front of your mailbox that lights up the address. When the pizza at Domino’s is ready, you want the lightbulb on your mailbox to light up so that the delivery person can read your address when they arrive in front of your house with the pizza.

The Internet should make it possible to have this kind of event-driven, connected world. Anything that is connected to the Internet should be able to send signals to anything else on the Internet, so that our lives gradually become more automated.

This is what IFTTT does. Users of IFTTT can easily create applets to wire different services together. You can use IFTTT to trigger an email whenever three of your friends retweet something on Twitter. You can use IFTTT to flash the lights in your house when Bitcoin hits new market highs. You can use IFTTT to order a pizza whenever Bitcoin crashes.

IFTTT makes it easy to connect different services together, and a lot of work goes into the infrastructure that enables these billions of events to process correctly. Nicky Leach from IFTTT’s engineering team joins the show to describe how IFTTT allows for integrations between services that were not built to integrate–and he talks about the scheduling, data engineering, and monitoring of the company’s software stack.

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Computer chips have physical limitations. When transistors get too small, electrons start to behave in ways that make the hardware modules less reliable. Our reliable technological progress has been enabled by Moore’s Law: the idea that the number of components we can fit on a chip doubles roughly every 12-18 months.

We can’t keep shrinking the size of these components, because physics is no longer complying.

Quantum computing allows us to operate on qubits rather than bits, giving us better parallelism and continued reliable technological progress. Quantum computing is still mostly an area of research rather than production systems–but it is rapidly approaching usability, and Zlatko Minev joins the show to explain how quantum computing works, and why software engineers should care.

Zlatko is a PhD candidate at the Yale Quantum Information Lab. Today he describes how qubits work, which algorithms quantum computing impacts, and which parts of modern computer architecture will work on a quantum computer. We may have to throw out the Von Neumann architecture when it comes to quantum!

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How would you build a system for indexing and monitoring the entire Internet?

Start by breaking the Internet up into IP address ranges. Give each of those address ranges to servers distributed around the world. On each of those servers, iterate through your list of IP addresses, sending packets to them. Depending on what sorts of packets those IP addresses respond to, and what those responses are, you can build a map of the devices on the Internet: what is running on those devices, and what they respond to.

Qadium is a company that indexes and monitors devices on the Internet, to help organizations understand the devices that are within corporate networks. If you are a large corporation, Qadium can probably do a better job of figuring out your Internet footprint than you can.

Matt Kraning is the CTO of Qadium, and in today’s show he describes the process by which Qadium maps the Internet. Matt used to work on data infrastructure at DARPA, and has deployed Hadoop in Afghanistan–so the infrastructure of Qadium seems relatively manageable. Our data conversations in this episode spam from talking about Storm and Hadoop to Google BigQuery, BigTable, and DataFlow.

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Scala is a functional and object oriented programming language built on the JVM. Scala Native takes this language, loved by many, and brings it to bare metal. Scala Native is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala.

Denys Shabalin is a Research Assistant at the EPFL and the primary creator of Scala Native. In this episode, Adam Bell interviews Denys about the motivations behind the Scala Native project, how it was implemented and future directions. He also briefly touches on how Scala Native made cold compilation times of Scala code twice as fast. If you are interested in functional programming, compiler design, or want to learn some interesting tidbits about garbage collector design and trade offs you will like this episode.

In past shows, we have covered many newer programming languages, and new twists on old programming languages. We’ve talked about ScalaJS, Swift on the Server, and Rust at Mozilla. Download the Software Engineering Daily app for iOS or Android to hear all of our old episodes. They are easily organized by category, and as you listen, the SE Daily app gets smarter, and recommends you content based on the episodes you are hearing. If you don’t like this episode, you can easily find something more interesting by using the recommendation system.

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