Was sind die Hauptfaktoren, die die Netzwerklatenz in Cloud- und On-Premise-Umgebungen beeinflussen?

Cloud is a reality for every enterprise. The question isn’t if it should be used, the question is how best to integrate it with prem/owned resources. A concept of digital transformation is interconnection oriented architecture (IOA). Placing owned/prem resources in datacenters that have the best connectivity options to networks, clouds, and applications providers enables hybrid multicloud. Your owned resources can connect with fiber or intelligent fabric to carriers, partners, and cloud onramps in the same facility. This places all the elements as close as possible for the best performance. Forrester’s TEI study attributed 30%+ reduction in latency to adopting IOA.

To decide cloud and on-premise, there are some important factors. Firstly is to define all important traffic flows in your network on premise and cloud. Then you can determine the impact of latency by deploying on-premise and cloud. Ask yourself if those differences can be accepted. Not all the traffic are necessary latency sensitive or latency insensitive. Then compare the gain and the loss to deploy on premise and in cloud. Remember a hybrid solution is also possible.

The choice between cloud and on-premise environments significantly affects network latency. Cloud environments might introduce additional latency due to their internet-based nature, but they also offer scalability and global distribution advantages. On-premise solutions can have lower latency due to proximity and direct control but might lack the scalability of the cloud. In my experience, while migrating a client from on-premise to cloud, we noticed an initial increase in latency, which we mitigated by choosing cloud regions closer to the user base and optimizing the network path. Additionally to this, there are any other ways to reduce latency, CDNs for static content for example, DX connectivity and/or Expressroute, etc...

Network latency in both cloud and on-premise setups is influenced by factors like distance, congestion, routing efficiency, equipment performance, protocol overhead, network layout, QoS settings, bandwidth limitations, interconnectivity, and application design. Addressing these factors is crucial for enhancing network performance and reducing delays.

Back in late 90's I brought the first proliferation of ATM Technology to come to the World with the assistance of Ted Rogers magnate of Canada. This was Lightway. This led to FilmWay and later DSL you all see delivering bandwidth to your homes, and businesses. The World was my next target with Celterra and expanded past NA to Europe and SE Asia then Asia to encircle the World. Today the focus is on Life Safety with Resilient and EarthStar with clean energy.

Serialization delay is lower on higher speed interfaces, but high bandwidth links over distance are actually more sensitive to speed of light latency. The “bandwidth delay product” is derived by multiplying the bandwidth times the latency between endpoints. The higher the product, the larger the receive windows need to be in order to establish large single flows. Modern digital transformation hybrid multicloud architectures help address the issues surrounding prem/owned vs cloud. Clouds will always be part of the equation, so placing owned resources in data centers that are well connected and host cloud onramps locally is critical to achieving the best performance. Lather rinse repeat in each relevant Metro in which services are needed.

Distance and bandwidth are fundamental factors affecting latency. The greater the distance between the user and the data center, the higher the latency due to the longer travel time for data. Bandwidth also plays a crucial role; insufficient bandwidth can lead to congestion and increased latency. I've witnessed cases where simply upgrading the network paths and increasing bandwidth significantly reduced latency, especially in scenarios involving large data transfers.

The analysis of RTT and BDP and other key metrics contributes to the scientific discovery of send and receive buffers along with ring buffers and scaling windows or even RDMA. Using this information we determine impacts across layer 3 routing protocols where MTU designation can impact adjacency requirements. Packet sizes matched to optimal throughput and matching congestion algorithms to improve congestion management all blend into the delivery of efficiency.

Sure, let's simplify that. Think of it like this: How far data has to travel can really affect how fast your network is. The further the data has to go, the more stops and waits it has to make. This is really important for cloud setups, where the data centers could be in different parts of the world. Another thing that can affect speed is bandwidth, which is how much data can move at once. The more bandwidth you have, the faster your network can be, as long as there's no traffic or other issues.

Routing and network congestion are critical factors. Inefficient routing can cause data to take longer paths than necessary, increasing latency. Congestion, often during peak times, can also slow down data transmission. I've dealt with congestion issues by implementing traffic shaping and prioritizing essential data, which helped maintain acceptable latency levels even during high traffic periods.

The way data moves from one place to another in a network is decided by rules and methods that make the network work better and more reliably. But, these rules can also make things slower because data might have to make many stops and go through switches, routers, firewalls, and other devices. Traffic jams can also slow things down. This happens when there’s too much data for the network to handle, causing data to get lost, sent again, and wait in line.

Encryption and compression introduce processing overhead, which can affect latency. While they are vital for security and efficient data transmission, it's crucial to balance them with performance needs. For instance, optimising SSL/TLS settings for applications, could result in improved and reduced latency without compromising security.

let's simplify that. Encryption and compression are like secret codes and zip files for your network. They make your network safer and more efficient, but can also slow things down. Encryption changes data into a secret code to keep it safe from prying eyes. Compression makes data smaller to save space and make things faster. Both encryption and compression need computer power and time, which can slow things down. But, they can also speed things up by reducing the amount of data that needs to move around.

When using cloud technology the application usage of data being processed within the cloud (higher compute cost) versus data being transferred out of the cloud environment (higher data exfil cost) becomes important. Having an application that caches data and gives user feedback (spinning wheel/progress bar) when data is being processed or retrieved can reduce users continually sending requests. Therefore insights into an application performance (webserver logs, application firewalls, etc.) maybe be necessary to understand nominal vs. off-nominal (failed application, denial of service attacks, etc.).

The way applications are designed and optimized plays a significant role in determining latency. Efficiently coded applications with optimized database queries and minimal data transfers can significantly reduce latency. From my work, redesigning an application to use local caching and more efficient database interactions drastically reduced its latency, enhancing user experience. It's also important to differentiate latency at layer 7 vs latency at layer 3/4. This is important while troubleshooting to identify the actual root cause for latency and act accordingly.

How fast things happen in an app can be affected by many things. This includes how much data is sent back and forth, how often it’s sent, and techniques like caching, buffering, batching, and prefetching.

Die Anwendungsperformance hängt am Ende nicht nur von der Netzwerklatenz ab, sondern auch von Paketverlusten z.B. durch Überlast von Links und dadurch notwendige Retransmits. Außerdem spielt die „Chattyness“ einer Applikation eine wesentliche Rolle.