Cloud-based computing, leveraging the power of serverless architectures, has garnered a reputation of growing prominence. Their enthusiasm for improving performance stems directly from the ease with which they can introduce new solutions. Why not leverage a serverless function to process and analyze an incoming image or stream of events from an Internet of Things device? It’s quick, easy, and scalable. You’re free from allocating and preserving computing resources; instead, just deploy utility code seamlessly. The major cloud providers, including Amazon, Microsoft, and Google, all offer serverless computing capabilities.
Serverless computing offers significant value for occasional or one-off tasks, effectively making them more practical and cost-effective. Can machine learning algorithms accurately replace the expertise of human domain experts in complex workflows, where subtle nuances and context-dependent decision-making are crucial? What lies at the heart of a global air travel network like ours is the seamless orchestration of thousands of flights every day?
Scalable NoSQL knowledge shops, such as or, can store and manage complex data sets, including information on flights, passengers, baggage, gate assignments, and pilot scheduling, among other critical details. While serverless capabilities may enable knowledge shops to process high volumes of event-driven scenarios like flight cancellations and passenger rebookings, do they necessarily represent the most effective approach for air carriers seeking to leverage these applications at scale?
The inherent constraint of serverless computing lies in its very essence as serverless, inherently imposing limitations on its potential. Since these processes inherently necessitate overhead to allocate computational resources upon invocation? Additionally, these models are designed to operate statelessly, relying instead on external knowledge repositories for information retrieval. This additional slows them down. Because they eschew native, in-memory caching, data cannot always circulate seamlessly over the cloud’s network to reach the serverless function that executes it?
While building large-scale architectures, serverless computing also lacks transparency in its programming framework, making it challenging to implement intricate workflow processes. Developers should enforce a distinct ‘separation of concerns’ mechanism in each program they execute. Without sacrificing scalability and maintainability, it’s easy to succumb to the temptation of replicating functionality and inadvertently developing a complex, unwieldy codebase. Additionally, serverless capabilities can lead to uncommon exceptions, such as timeouts and quota limits, which must be addressed through utility logic.
By sidestepping the limitations inherent in serverless technologies, we can opt for an alternative approach: relocating our code to the client-side? Consider leveraging serverless computing’s scalability features to execute your code and minimize infrastructure management? In-memory computing stores data and applications within the primary memory of a distributed network across multiple servers. Objects may potentially exhibit capabilities when responding to received messages. It can also retrieve and persist knowledge, storing adjustments in knowledge shops that resemble NoSQL databases.
Instead of implementing a serverless function that processes data stored in a remote location, we can directly send a request to an in-memory compute environment to execute the operation. This approach accelerates processing by eliminating the need to repeatedly access a data repository, thereby minimizing the amount of data that needs to traverse the network. As a result of its in-memory architecture, knowledge computing has the potential to efficiently handle enormous workloads comprising vast quantities of objects. The need to write utility code to handle exceptions is eliminated when extremely accessible message processing is utilized.
In-memory computing offers a unique set of benefits when crafting code that describes intricate workflows, leveraging the synergies between data-structure specialists and software engineers to create efficient, scalable solutions. Unlike a traditional serverless approach, an in-memory knowledge graph can prevent processing on objects according to strategies defined by their knowledge types? This feature enables builders to avoid duplicating code across various serverless functions. This approach eliminates the need for object locking, thus circumventing potential issues that may arise in persistent data stores.
To assess efficiency disparities between serverless computing and in-memory processing, we compared a straightforward workflow implemented using AWS Lambda against an identical workflow developed with Hazelcast, a scalable in-memory computing framework. The workflow represents a process for an airline to cancel a flight and reaccommodate passengers on alternative flights. The application utilized two distinct data structures – flight and passenger entities – to store all scenarios within a DynamoDB database. The occasion controller initiated a mass cancellation of flights following an unforeseen circumstance and subsequently monitored the duration necessary to complete all subsequent rebooking processes.
Within the serverless architecture, the occasion controller triggers a Lambda function to cancel all flights. Passengers were rebooked by each ‘passenger lambda’ selecting a distinct flight and subsequently updating the relevant passenger information. When this event occurred, the system leveraged its serverless functionality to verify the seamless rebooking of the passenger from their original flight to a newly allocated one. To leverage these features, synchronization mechanisms such as locking were essential for managing concurrent access to DynamoDB resources.
The implementation of a digital twin leverages in-memory object creation for every flight and passenger, only generating these entities once they are queried from DynamoDB. When flight objects received cancellation notifications from the occasion controller, they promptly dispatched these updates to corresponding passenger digital twin entities. The passenger digital twins successfully re-routed themselves by selecting a distinct flight itinerary and transmitting notifications to both their original and revised travel plans. The software code eschewed the use of locking mechanisms, allowing for seamless updates to be written directly to DynamoDB’s in-memory platform.
Efficiency metrics revealed that the deployment of digital twins successfully processed a whopping 25 flight cancellations, accommodating over 2,500 passengers (100 per flight), an astonishing 11-fold speed boost compared to serverless alternatives. While our initial attempts to leverage serverless capabilities were hindered by limitations in handling the demanding workload of cancelling 250 flights with 250 passengers, we successfully demonstrated the capacity of ScaleOut Digital Twins to process a volume twice that size, accommodating 500 flights without any issues.
While serverless architectures excel in handling small, ad-hoc tasks, their limitations become apparent when building complex workflows that require processing numerous data objects and scaling to accommodate immense workloads? Transferring the code to an in-memory computing environment could be a more prudent decision. This innovative approach optimizes productivity by streamlining knowledge transfer, thereby ensuring seamless collaboration, and its unparalleled scalability enables organizations to effortlessly adapt to evolving demands. The streamlined approach to utility design also benefits from structured access to information.
Want to learn more about ScaleOut Digital Twins? Explore how to manage complex workflow objects effectively by visiting https://www.scaleoutdigitaltwins.com/touchdown/scaleout-data-twins.
