Guerrilla Capacity Planning and the Law of Universal Scalability

In the era of Web 2.0 traditional approaches to capacity planning are often difficult to implement. Guerrilla Capacity Planning facilitates rapid forecasting of capacity requirements based on the opportunistic use of whatever performance data and tools are available.

One unique Guerrilla tool is Virtual Load Testing, based on Dr. Gunther's "Universal Law of Computational Scaling", which provides a highly cost-effective method for assessing application scalability. Neil Gunther, M.Sc., Ph.D. is an internationally recognized computer system performance consultant who founded Performance Dynamics Company in 1994.

Some reasons why you should understand this law:

1. A lot of people use the term "scalability" without clearly defining it, let alone defining it quantitatively. Computer system scalability must be quantified. If you can't quantify it, you can't guarantee it. The universal law of computational scaling provides that quantification.

2. One the greatest impediments to applying queueing theory models (whether analytic or simulation) is the inscrutibility of service times within an application. Every queueing facility in a performance model requires a service time as an input parameter. No service time, no queue. Without the appropriate queues in the model, system performance metrics like throughtput and response time, cannot be predicted. The universal law of computational scaling leapfrogs this entire problem by NOT requiring ANY low-level service time measurements as inputs.

The universal scalability model is a single equation expressed in terms of two parameters α and β. The relative capacity C(N) is a normalized throughput given by:

C(N) =   N / 1 + α (N − 1) + β N (N − 1)

where N represents either:

1. (Software Scalability) the number of users or load generators on a fixed hardware configuration. In this case, the number of users acts as the independent variable while the CPU configuration remains constant for the range of user load measurements.

2. (Hardware Scalability) the number of physical processors or nodes in the hardware configuration. In this case, the number of user processes executing per CPU (say 10) is assumed to be the same for every added CPU. Therefore, on a 4 CPU platform you would run 40 virtual users.

with `α' (alpha) the contention parameter, and `β' (beta) the coherency-delay parameter.

This model has wide-spread applicability, including:

• Accounts for such effects as VM thrashing, and cache-miss latencies.
  
• Can also be used to model disk arrays, SANs, and multicore processors.
  
• Can also be used to model certain types of network I/O
  
• The user-load form is the most common application of eqn.
  
• Can be used in combination with measurement tools like LoadRunner, Benchmark Factory, etc.

Gunther's book: Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services

and its companion Analyzing Computer Systems Performance: With Perl: PDQ

offers practical advise on capacity planning. Distilled notes of his Guerilla Capacity Planning (GCaP) classes are available online in The Guerilla Manual.