Network testing suite- Realistic ip stress load generator

Network testing suite- Realistic ip stress load generator

When stress testing networks and applications, using poor-quality traffic loads generates misleading results. Real-world networks exhibit diverse and complex behaviors.

Leveraging protocol stacks

Basic packet generation, leverage protocol stacks to create structured traffic flows. For example, model TCP properly with handshakes, keepalive packets, sliding windows, MSS calibration, etc.  The same goes for UDP, IPSec, VoIP, and other protocols. This provides application-layer test coverage beyond just network loads. Protocol stacks bring realism.

Crafting background noise

Background traffic noise is crucial for realism. Add random streams of DNS lookups, NTP checks, ARP announcements, ICMP pings, and more. Vary byte patterns to appear encrypted or compressed. Without background noise, test traffic appears suspiciously clean. Ensure noise makes up at least 20% of packets. Tune noise levels proportionally as main flows increase or decrease.

Simulating users

Model individual virtual users with distinct personalities and habits assign profiles mimicking various ages, locations, devices, and personalities. Give each user base probabilities for behaviors. A teen mobile user may frequently use Snapchat, Instagram, TikTok, and streaming apps. An executive uses business apps and web conferencing. Apply personas to create diverse populations.

Mimicking browser traffic

They are craft synthetic browser traffic by modeling page loads, AJAX calls, caching, errors, JavaScript execution, DOM updates, and redirects. Pull real browser traces to reverse engineer sequences and timing. Render pages like a real browser and normalize flows. Include browser quirks such as retry loops, DNS pinning, and shared connections. Realistic synthetic browsers enable front-end load testing beyond raw network traffic.

Following human patterns

Study real user patterns and program lifelike habits. Humans follow daily cycles of activity, take breaks, get distracted, and vary behaviors model actions like walking away or multi-tasking during long flows. Inject random human-like actions such as restarting apps, losing connectivity briefly, or revisiting sites. Avoid robotic continuous repeatable cycles. Human-like actions exercise fault tolerance.

Realistic load shaping 

Shape load realistically using distributions like diurnal curves for cyclical peaks and valleys. Introduce random spikes mirroring real events or outages. Allow defining custom load shapes using CSV data or math formulas.

Support progressive ramp-up and down durations or sudden load transitions. Configure shaping per protocol mix or test objective. Realistic load shapes provide meaningful stress levels.

Live protocol analysis

Passively analyze live traffic in real-time while generating loads. Continuously detect usage patterns, new apps, errors, encrypted flows, and other traits. Automatically adapt generated profiles to match the latest network behaviors.  Dynamic analysis keeps traffic fresh as networks evolve. Capture new application behaviors and incorporate them into flows. This keeps your toolset aligned to changing target environments illegitimate use of IP stressers.

Comprehensive metrics

Good metrics are key for effective load testing. Track detailed performance indicators like bandwidth, latency, jitter, errors, retries, concurrent connections, and more.  Provide protocol-specific metrics like page load times, requests per session, DNS lookup latency, TCP handshake RTT, and so on. In-depth metrics reveal how networks perform under realistic stress.

Custom protocol modules

Allow creating custom protocol modules for proprietary or unique apps. Provide SDKs to build new protocol behaviors in Python, Java, C#, Golang, etc. Load test IoT devices, SCADA systems, and other tech using realistic data models tailored specifically to them. Pre-built modules only cover common protocols. Extensibility satisfies one-off needs to test lesser-known apps and devices using their native communication methods.

Sheri gill