Latency

Overview Latency and throughput are two orthogonal measures of system performance: latency is the time a single request takes to complete, while throughput is the volume of work a system finishes per unit of time. The distinction matters because architectures optimized for one can quietly degrade the other. Comparison Diagram Latency Client Server Time for ONE request to complete Throughput Client Server Total requests completed per second Comparison Table Aspect Latency Throughput Definition Time elapsed for one request to travel and complete Amount of work completed across all requests per unit time What is measured A single request’s round trip or processing delay Aggregate output of the system over an observation window Unit of measurement Milliseconds, microseconds, or seconds Requests/sec, transactions/sec, or Mbps Primary driver Network round-trip time, serialization, and processing delay Available bandwidth, parallel capacity, and resource pool size Effect of concurrency Individual request latency can rise as queueing builds up Throughput rises with more parallel workers, up to a capacity limit Behavior under overload Tail latency spikes as queues grow (p95/p99 degrade) Throughput plateaus or drops once the system saturates Typical optimization Reduce round trips, cache results, shorten the critical path Batch requests, add parallel workers, scale out capacity Measurement method Ping, request timers, percentile latency (p50/p95/p99) Requests-per-second counters, load testing, capacity benchmarks Key Differences Latency measures the time for one request; throughput measures the volume processed per unit time. Batching to raise throughput can increase tail latency for individual requests. Latency is bounded by physical round-trip time; throughput is bounded by system capacity. Under heavy load, latency spikes from queueing while throughput plateaus at a ceiling. Little’s Law links the two: average latency times concurrency roughly equals throughput. When to Use Each Latency ...

Overview Edge computing and cloud computing both run workloads away from the end-user device, but they differ in where that processing physically happens relative to the data source. Edge computing pushes compute to nodes near the device to cut latency, while cloud computing centralizes it in remote data centers for scale and simplicity. The choice shapes latency budgets, bandwidth costs, and how much infrastructure you have to manage yourself. Comparison Diagram Where Processing HappensEdge ComputingDeviceEdgeNode~1-5 ms round tripCloud ComputingDeviceCloudDataCenter~50-150 ms round trip, multiple network hopsSame device, two distances to compute Comparison Table Aspect Edge Computing Cloud Computing Processing location Local nodes, gateways, or on-device hardware near the data source Centralized data centers operated by the provider, often far from the source Latency Single-digit to low double-digit milliseconds due to physical proximity Tens to hundreds of milliseconds depending on distance and network path Network dependency Can operate with intermittent or low-bandwidth connectivity to the core network Requires a stable, sufficiently fast connection to reach the data center Bandwidth usage Filters or pre-processes data locally, sending only summaries upstream Raw data typically travels over the network to be processed centrally Compute and storage capacity Limited by the size and power of local hardware Effectively unlimited, elastic capacity provisioned on demand Data handling and privacy Sensitive data can be processed and stay on-site, reducing exposure Data leaves the local environment and is subject to provider-side controls Scalability and management Scaling means deploying and maintaining more physical nodes across sites Scaling is a configuration change managed by the provider Cost model Upfront hardware and per-site operational costs Pay-as-you-go operating expense with no hardware to own Key Differences Edge computing minimizes latency by keeping processing physically close to the data source Cloud computing offers far greater elastic capacity since it draws on a shared, centralized pool of resources Edge deployments reduce bandwidth costs by filtering data before it ever leaves the site Cloud computing is simpler to manage since there’s no distributed hardware fleet to maintain Edge nodes can keep sensitive data local, while cloud centralization concentrates data in provider infrastructure When to Use Each Edge Computing ...

Overview Latency and bandwidth both describe network performance, but they measure completely different things: latency is how long a single piece of data takes to travel from source to destination, while bandwidth is how much data can move through the connection per second. A link can have huge bandwidth and still feel laggy, or tiny bandwidth and still respond instantly — understanding which one is limiting you determines whether the fix is a faster link or a shorter path. ...