Performance Analysis and Methodology

Performance analysis and methodologies are very broad topics. It is about optimization. Performance as a set of Bellman equations to solve. Traditional states and performance functions
enumerations does not address the performance evaluation issues
since a traditional MDP problem results in large state transitions
or high dimensional performance feature extractions. For a networking
only related problem formulation may involved into 30+ performance
parameters and for Solaris kernel, it involves 100+ parameters.
Hence, dynamic and adaptive performance analysis and associated
resource utilization analysis may reach the optimum
performance function evaluation with fast convergence.

It is involved with Performance Metrics (Parameters, feature extractions), Performance Functions, Performance Evaluations, Performance Learning, Performance Instrumentation, Performance Management and Adaptive Tuning etc. It really depends on specific issues to formulate the specific problem into adequate function, models to resolve the performance issues.

In addition, from CS analysis and design methods such as dynamic programming, divide by conquer, greedy and amortization. They are popular techniques to address performance from subproblem to global problems. However, to achieve end-to-end performance gains such as network tuning, global optimum may be the most concerns instead of local optimum. In addition, queuing theory has been widely adopts for traditional SMP based performance management and capacity planning.
Core based parallelism and pipelining introducing many new issues down
the road. Is queuing still works well for parallelism paradigm, if not
what will be the optimization, if yes, what will be proper queue
partitioning etc


In general quantitative methods should be the main theme of the analysis
and evaluation. It is hard to generalize as a whole but specific to
the target problem formulations.