Several factors, such as increasing data usage, higher scalability demands, and technological advances, have combined to accelerate the growth of distributed computing. It is now estimated that more than 94 percent of companies utilize cloud computing that relies on distributed computing. That means most companies today depend on distributed computing to some degree. This increased usage brings new trends and developments that allow distributed systems to better handle complex tasks and large data volumes.
One rapidly evolving area of distributed computing is server-side computation, where application architects can now use multiple nodes or node clusters to build efficient solutions for computation-intensive and data-intensive tasks. Other growing distributed computing trends include microservices architecture, cloud computing, edge/fog computing, event-based architecture, remote AKKA actors, and swarm intelligence. Professionals and organizations that stay on top of these advancements will position themselves for optimal success in the future.
A time for change
Distributed computing started approximately 25 years ago with a simple client-server architecture. Today, it has grown to include three innovative architectures that enable companies to take greater advantage of the affordability and convenience of cloud computing and the data generated by the Internet of Things (IoT) devices. Now, instead of relying on slower, less efficient monolithic applications, organizations can distribute applications over a cluster of nodes and break tasks down into simpler microservices that reduce network latency. The result is faster results, greater scalability, and increased cost efficiency.
At the heart of this trend revolutionizing how computational work is completed is the ability to create nodes with the click of a button. Developing and deploying micro applications on multiple nodes are easy and affordable because of increased hardware power, more affordable cloud computing, and containerizing solutions like Docker and Kubernetes. Then, with advances like microservice gateways and remote AKKA actors, organizations can improve load balancing, communication, and response times thereby maximizing system performance even during high-traffic periods.
Distributed computing architectures
These three architectures make it easy to develop and deploy distributed computing systems:
1. Microservices architecture. This architecture distributes computations across multiple microservices instead of relying on one monolithic application. For example, a monolithic telecommunication e-commerce application is broken down into cart service, checkout service, billing service, and product catalog service. When end users access the site, their requests are addressed by smaller, more efficient applications. Also, companies avoid the scenario where the failure of a monolithic application brings down the whole system. Microservice distribution is handled with microservices gateways and load balancers. The gateway is for multiple microservices deployed over a cluster of nodes. The load balancer is utilized when numerous instances of the same microservice are deployed on multiple nodes.
2. Event-based architecture. In this distributed system, computations are triggered by certain events. For example, a customer purchases a home internet service, and the internet company wants to alert a local services vendor to install it. With this architecture, the installation service’s application doesn’t have to be synchronously linked to the main e-commerce application. Instead, the e-commerce application sends an asynchronous event notification to the installation application through the messaging server (Kafka).
3. Distributed AKKA model. There are two ways to achieve distributed computing using this architecture—utilize remote AKKA actors or distributed clusters, which involve groups of actors. Consider this example from the supply chain management world: heavy computations are required to determine the rate of sales (ROS) and its related measures. Another set of computations is required for forecasting sales. In this situation, one remote AKKA actor calculates ROS while another calculates sales forecasts. With remote AKKA actors, developers configure their paths, and the actors, when invoked, execute the computing and return the results. With a distributed cluster, organizations can have a collection of actors working toward the same objective.
Microservices architecture offers synchronous and asynchronous communication. Synchronous communication is vital for computations that require an immediate response, while asynchronous communication is preferred for situations that involve loose coupling and high scalability. Event-based architecture is completely asynchronous, and remote AKKA actors offer synchronous and asynchronous communication as Akka actors use “tell” and “ask” patterns. Tell is asynchronous and ask is synchronous.
What the future holds
Several additional trends are gaining momentum and may be crucial in the future of distributed computing. Those trends include:
– IoT. These devices serve as data generation points, acting as “eyes” and “ears” that collect valuable data for processing at the node level.
– Fog computing. This type of computer brings data processing and storage closer to the data source. One example of this technology is a self-driving car where the computation and storage are in the car to avoid network latency.
– Swarm intelligence. This distributes intelligence across multiple nodes, making each element in the swarm a decentralized and self-organized system. That means if one node in a cluster goes down, it won’t affect the objective/performance of the other nodes.
Distributed computing offers many benefits
Everything from web browsing and online gaming to social media and basic mobile apps now relies on distributed systems to provide users with the desired information and services. Distributed computing has also become widespread in most industries because of the easy availability of cloud computing, cloud storage, and IoT devices. For instance, in healthcare, distributed systems speed up image analysis, medical drug research, and gene structure analysis. In engineering research, distributed systems improve product design, build complex structures, and design faster vehicles. Distributed systems assess portfolio risks, predict market movements, and support financial decision-making in financial services.
By decentralizing tasks and resources across various industries, distributed computing allows companies to meet increasingly complex computational demands while maintaining efficiency and resilience. Companies no longer need to deal with the negatives accompanying monolithic architectures, like slower development speed, poor scalability, and reduced flexibility. By staying current on distributed computing, they can enjoy faster performance, greater scalability and flexibility, and optimal resource utilization so that workload is better managed, costly system failures are reduced, and a valuable competitive edge is gained.
Meher Siddhartha Errabolu is a technical architect for Blue Yonder Inc., a world leader in supply chain management solutions. His expertise includes building high-throughput applications and designing microservice, data analytics, and distributed computing applications. Siddhartha has worked for leading telecommunication companies and banks in the United States and Canada and holds a Bachelor of Engineering from Bangalore University. He can be reached at siddartha.errabolu@gmail.com or on LinkedIn.
