The "latency" Problem

Basics of Distributed Computing

In Computer science, Distributed Computing studies distributed systems. In a distributed system, the components are spread across multiple networked computers and communicate and coordinate their actions by transferring messages from one system to the next. The components interact with one another so that they can reach a common purpose. Maintaining component concurrency, overcoming the lack of a global clock, and controlling the independent failure of parts are three crucial issues of distributed systems. When one system's component fails, the system does not fail. Peer-to-peer applications, SOA-based systems, and massively multiplayer online games are all examples of distributed systems. The use of distributed systems to address computational issues is called distributed computing. A problem is divided into numerous jobs in distributed computing, each of which is solved by one or more computers that communicate with one another via message passing. 

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Latency problem in Distributed  Computing 

• The latency impact has been one of the recurring challenges with managing data, especially considerable amounts of data. Latency is the time delay within a system caused by delays in task execution. Communication, data management, system performance, and other aspects of computing are all affected by latency.
• If you've ever used a wireless phone, you've had firsthand experience with latency. It's the time difference between you and your caller's broadcasts. When organizations need to examine outcomes behind the scenes to plan for a new product release, latency has little impact on consumer happiness. Latency is unlikely to necessitate immediate response or access.
• Building an extensive data application in a high latency environment may not be practical if high speed is required. Latency can also impact the need to authenticate data in near real-time. A high level of latency might make the difference between success and failure when dealing with real-time data.

Drawbacks of Latency 

• Latency leads to degraded performance where real-time data is presented or used. It affects big data applications.
• When dealing with real-time data, a high latency could mean the difference between success and failure, which leads to undesired results.
• Latency lets you compromise with speed; high speed can't be achieved in a high-latency environment.
• Authentication is critical for any application, and a high-latency climate holds you back from proper authentication.
• Different types of data and their quantities require low latency, and high latency would mean degradation in performance.

Solutions to the problem of Latency

While zero-latency may never be achieved, the goal is always to send information in the quickest time feasible, so guaranteeing predictable, low latency processing is critical when developing a real-time application. Knowing the sources of latency in your application and then reducing them is sometimes the most challenging step. If you can't eliminate them, there are methods you may do to decrease or control their effects.

Network I/O

Most programs use the network somehow, whether it's to communicate between client and server applications or between server-side activities and applications. The primary thing to note here is that proximity matters: the closer your client is to the server, the lower the network latency.

Things to do :

1. Use faster networking, such as 10GigE networking and better network interface cards and drivers.
2. Remove network hops. Data can be horizontally distributed across several host machines in clustered queuing and storage systems, which can help you avoid additional network round-trip connections.
3. Keep all processing in one availability zone if your application runs in the cloud. 

Disk I/O

Many real-time applications are data-intensive, necessitating a database to handle the request. Databases, even in-memory databases, make data long-lasting by storing it on persistent storage, but this can add significant unwanted latency to high-velocity real-time applications, and disc I/O, like network I/O, is expensive.

Things to do:

1. Use fast storage devices with battery-backed caches, such as SSDs or spinning drives.
2. Avoid writing to the hard drive. Use write-through caches, in-memory databases, or grids instead (current in-memory data stores are designed for low latency and good read/write performance).

The operating environment

The operating environment in which your real-time application is run—shared hardware, containers, virtual machines, or the cloud—can significantly impact latency.

Things to do :

1. Run your application on dedicated hardware to prevent other applications from consuming system resources and affecting performance.
2. Virtualization should be avoided—even on dedicated hardware, hypervisors impose a code layer between your application and the operating system. Performance degradation can be avoided when adequately configured, but your application is still running in a shared environment and may be influenced by other apps on the actual hardware.

Code for Program 

A few basic core functions can slow you down when it comes to coding.

Things to do :

1. The most obvious sources of code delay are inefficient algorithms. Look for extra loops or nested expensive operations in code wherever possible; loop restructuring and storing expensive computation results usually help.
2. Multithreaded locks cause processing to stall, resulting in delay. When designing server-side apps, use design techniques that eliminate locking.
3. To effectively utilize hardware resources like network and disc I/O, employ an asynchronous (nonblocking) programming approach instead of blocking activities.

FAQs

What is Big Data? 

Big data refers to data collections that are too massive or complicated for typical data-processing application software to handle. Data with more fields have more statistical power; however, data with more fields have a higher false discovery rate.

What is distributed computing? 

The subject of computer science known as distributed computing explores dispersed systems. A distributed system is one in which the components are spread across multiple networked computers and communicate and coordinate their actions by transferring the messages from one system to another.

What is the latency problem in distributed computing? 

The ability to load and update data in near real-time while enabling query workloads is known as data latency. The time it takes for your data to appear in your database or data warehouse following an event is known as data latency. The time it takes for a message to travel from one point to another in a distributed system. Bandwidth is the amount of data that is transported in a stable state per unit of time.

Conclusion

In this article, we have briefly discussed big data and latency problems in distributed computing, and we have examined the need to reduce latency and, finally, ways to reduce latency. 

I hope you have gained some insight into this topic of latency problems, and by now, you must have developed a clear understanding of them. You can learn more about such topics on our platform Coding Ninjas Studio.    

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