Network latency is the delay between sending and receiving data over a network. It can affect the performance, scalability, and reliability of distributed computing environments, where multiple nodes communicate and coordinate to solve complex problems. In this article, you will learn some strategies to reduce network latency and improve your distributed systems.
The first step to reduce network latency is to measure and monitor it. You can use various tools and methods to collect and analyze latency data, such as ping, traceroute, network monitoring software, or application performance monitoring software. You should also define your latency requirements and SLAs, and identify the sources and causes of latency, such as network congestion, routing, distance, bandwidth, or protocol overhead.
In a distributed system, it's important to follow Amdahl's law and tackle the most common latency spikes first. There are a ton of really good tools out there, but for my kubernetes folks, using the following stack is amazing in analyzing your request times: - Istio - Jaeger - Prometheus - Grafana With those tools, you can track each request as it goes through your system, from subsystem to subsystem, and build spans which show where most of the latency lives. We used something similar at Sporttrade to find some hidden blocking calls. By profiling our code, we were able to find the blocking calls and mitigate their latency by removing them entirely or wrapping them within background threads.
The network topology is the way the nodes are connected and arranged in a distributed system. It can have a significant impact on latency, as different topologies have different characteristics, such as diameter, degree, redundancy, or fault tolerance. You should choose the right network topology for your distributed system, depending on your goals and constraints. For example, you can use a star topology to minimize the number of hops between nodes, a mesh topology to increase the redundancy and reliability of connections, or a hierarchical topology to reduce the complexity and cost of routing.
The network protocol is the set of rules and standards that govern how data is transmitted and received over a network. It can also affect latency, as different protocols have different features, such as reliability, security, or efficiency. You should optimize the network protocol for your distributed system, depending on your needs and trade-offs. For example, you can use UDP instead of TCP to reduce the overhead and latency of acknowledgments and retransmissions, or you can use HTTP/2 instead of HTTP/1.1 to enable multiplexing and compression of requests and responses.
Another way to reduce network latency is to compress and cache data. Compression reduces the size of data that is sent and received over a network, which can improve the bandwidth utilization and speed up the data transfer. Caching stores frequently accessed or repeated data in a local or intermediate storage, which can reduce the network traffic and latency of fetching data from a remote source. You should use compression and caching techniques for your distributed system, depending on your data type and format. For example, you can use gzip or brotli to compress text-based data, or you can use memcached or redis to cache key-value data.
One thing that I have seen work wonders is switching from something like JSON to protobuf for internal communication for a few reasons: 1. Protobuf uses a binary format, which is inherently more efficient in terms of both space and parsing speed compared to the human-readable text format of JSON. 2. Protobuf-encoded data is more compact because it omits unnecessary metadata that JSON includes. This reduces the amount of data that needs to be sent over the network. 3. When parsing Protobuf data, the binary format allows for efficient and direct memory mapping, reducing the need for string parsing, which can be costly in terms of CPU usage.
Parallelization and batching are two techniques that can reduce network latency by exploiting the concurrency and aggregation of data. Parallelization splits a large data set into smaller chunks that can be processed simultaneously by multiple nodes, which can reduce the overall processing time and latency. Batching groups multiple small data items into a larger batch that can be sent or received in a single network operation, which can reduce the number of network calls and latency. You should use parallelization and batching for your distributed system, depending on your data size and frequency. For example, you can use map-reduce or spark to parallelize data processing, or you can use message queues or buffers to batch data transmission.
Use asynchronous communication patterns like message queues or pub-sub systems (think Kafka/Pravega/RabbitMQ) to aid in the parallelization and batching of data. Doing so will decouple components of your distributed system, allowing them to operate independently without waiting for immediate responses. Your application can place tasks on the queue and independent worker nodes can handle the processing of the task on another thread, node, etc. The beauty here is that once the application's task is on the queue, it is free to handle other network requests and isn't held up doing the processing itself. Doing so will keep your application responsive even under heavy traffic.
Edge computing is a paradigm that moves some of the computation and storage from the central cloud to the edge of the network, closer to the data sources and consumers. It can reduce network latency by reducing the distance and hops between nodes, as well as the dependency and load on the cloud. You should use edge computing for your distributed system, if you have latency-sensitive or real-time applications, such as IoT, gaming, or streaming. For example, you can use edge devices, such as smartphones or sensors, to perform some of the data processing and filtering, or you can use edge servers, such as CDNs or edge clouds, to deliver some of the data and services.