What are the best practices for designing real-time communication software services?

Design system to be horizontally scalable to handle increasing numbers of users and messages. Use distributed architectures such as microservices or serverless to allow for easy scaling. Utilize message queues like RabbitMQ to manage the flow of messages between components of your system. This helps in decoupling components, handling bursts of traffic, and ensuring message delivery.

Best practices for designing real-time communication software services include optimizing latency, ensuring scalability, implementing robust security measures, prioritizing quality of service, designing clear APIs, thorough testing, continuous monitoring, and gathering user feedback for iteration.

Architect a scalable system for real-time message processing. System should be reliable and resilient to network failures, and thiss should be a priority. Security and UX are also high priority when designing a system for real-time communication. Consider also possible integrations with other communication or automation systems.

With the variety of technologies available to fix any given requirement, the challenge is to do a need based analysis. Often very high-end solutions are proposed for simpler needs, just in the name of modernization, which requires larger time, effort, and money to implement and manage. Start with the simplest tech, and check if any specific requirement is not being met. Then evaluate if the missing requirement can be handled at another layer of the hierarchy. This will ensure Single-Responsibility and be more specific to the challenge at hand.

Best practices for real-time communication software services include optimizing network protocols for low latency, implementing efficient data synchronization mechanisms, ensuring scalability through load balancing and distributed architectures, prioritizing security with encryption and authentication measures, conducting thorough testing for reliability, and providing robust error handling for fault tolerance. Additionally, user-friendly interfaces and seamless cross-platform compatibility enhance user experience.

Depending on sensitivity of data being accessed, just having standard authN and authZ is not sufficient and needs zero trust architecture to avoid confused deputy problem. System needs to monitor continuously of suspicious access via AI/ML and breaks the circuit when traffic is malicious.

Additionally make sure the layer is safe from cyber attacks. Code is secured, due checks are implemented, network firewall and access points are considered.

Authentication and authorization are key to make any communication channel secure. However, while implementing authentication, we need to think about user experience. First thing to consider - How often the user opens the software everyday? based on that we can expire and recreate a token. Second - Which other software it can connect with? Accordingly the third-party authorization flow can be developed using the available transport layer and socket layer proticols Third - latency in authentication and authorization. How can we minimize this ?

In the age of AI, consider whether secure, serverless, scalable, resilient, responsive, documented API services can be automagically derived from your schema? API routes, authentication, authorization, security at rest, security in transit, data governance, etc. are principles already solved beautifully in many a software product - consider optimizing your time in API design and implementation - keep the business logic focused and scalable and close to the organization's heart :-)

Communication software can be broadly of 4 types 1. Peer to peer (P2P) 2. Application to peer (A2P) 3. Peer to Application (P2A) 4. Application to Application (A2A). Based on the type and seasonality/ random event factor in usage, capacity planning can be done. It includes scalability and load balancing. Scalability can be horizontal or vertical based on type and usage pattern to optimize cost. We need to consider - how many messages, concurrent connections, parallel processes or threads can it afford to provide seemless user experience. For high availability, load balancing plays a vital role. How quick huge number of connections or messages are distributed across nodes through load balancer defines the usability of software

With mini/microservices and serverless architecture; scalability maybe automagically addressed - don't consider monolith practices unless you need to.

Automated monitoring, alerting and root cause identification can boost the operation and availability. Few tools and apps that can be used for monitoring are 1.Monit 2.splunk 3.Ganglia 4. Nagios 5.Tivoli/netcool 6.Grafana 7. Prometheus 8. Appdynamic 9.dynatrace 10. ManageEngine 11.ELK 12. cloud service monitoring like AwS cloudwatch A centralised dashboard eases our job.

Focus on automation and end to end testing. User flows are more important to automate and validate - focus energies more on selenium, cucumber, etc BDD approaches to your testing? On cloud, there are wonderful tools (e.g, AWS cloudtrail, AWS cloudwatch, AWS xray) which you might use very easily - custom end to end tracing is useful, but remember, difficult to maintain and remain comprehensive/complete.

Use BDD frameworks with accessibility plugins across the device and browser footprint where feasible, as the first approach to comprehensive validations of user interface, user experience, and fidelity across supported interfaces.

If we can't catch the trend, we miss out the opportunity. Innovation is the only way to stay ahead of the curve. With ubiquitous ultra-speed connection through 5G we can do a lot using AI, AR, VR to impress the end users without any visible latency. However, Technology should be used wisely. If a solution or innovation is not liked by users then it won't be adapted widely. Hence feedback is necessary. Continuous improvement using users feedback and automated usage report certainly helps. One can go for A/B testing to know which one is widely accepted.

When designing real time communications systems it's not an either or situation. You cannot compromise security for the best communication protocol, also at the same time its important to consider trends. Therefore all options are just as equally important and the should all be implemented effectively when implemented such a system. Here are a few more ways I found to be helpful when creating a real-time communication system: - Scalability - Security - Trends

Real-Time communication payloads are sensitive to loss, latency and jitter. Losses in the path can be due to bad networks at various levels - bad transmission medium, bad physical interfaces and network equipment. Some latency will always be present because of physics. Other than that, inefficient n/w equipment will cause latency. Sometimes bad traffic engineering may result in overwhelming even good n/w equipment resulting in latency. Jitter is the variance in latency. Excessive jitter even with acceptable latency may cause issues. This may be attributed to scheduling issues in the OS. A cloud VM may be running on a crammed host and competing for resources with other VMs and processes. Lastly choice of codecs is very important.

Make sure messages get through quickly and without delays so the communication feels instantaneous. Design the system to handle lots of users or sudden increases in activity without slowing down. Use methods that ensure messages are sent and received reliably, even if there are problems along the way. Keep data safe by making sure it's encrypted from the sender to the receiver. Keep an eye on how well the system is working and fix any problems before they become serious.

As silly as it might sounds, start with a design. Choosing the protocol, security, NFR, UX, scalability, etc should always start with a solid design. That being said, the design should always leave room for a good implementation that allows evolution in time. Never start building without a solid design (including event the test design in the process). You'll save time & money.