Performance Analysis and Optimization of the Hurricane File System ...

CHAPTER 8. CONCLUSIONS 82
8.1 General Optimization Techniques Applied
In our optimization process, we routinely used general principles of maximizing locality and concurrency
[22], including:
1. Reduce sharing whenever possible.
2. Minimize the use of global data structures and locks.
3. Use of distributed data structures and locks whenever possible.
4. Minimize hash collisions.
5. Reduce lock hold times.
6. Eliminate false sharing of secondary cache lines.
The techniques listed inherently overlap since they begin quite general and become more specific. Peacock
et al. [56, p. 86] had insight into multiprocessor scalability, stating that, “This problem illustrates a general
principle which needs to be followed for reasonable scalability of a multiprocessor kernel, namely that queue
sizes should remain bounded by a constant as the system size is increased.” This principle was applied to the
hash lists of the ORS and block cache system. Another important characteristic to maintain is that, in the
case where true sharing cannot be fundamentally avoided, performance should degrade gracefully (linearly
with the degree of sharing).
8.2 Lessons Learned
A number of lessons were learned during the production of this thesis.
1. Achieving scalability is a non-trivial task. While the optimizations themselves are simple to implement,
the complexity lies in identifying the scalability bottleneck points. A significant investment in time and
effort is required to understand the dynamic behavior of a large system containing complex interactions
between the operating system, file system, hardware, workload, and run-time environment.
2. The user-level operating environment is quite different from the server-level environment, so different
workloads may be needed for each environment to properly stress the system.
The Web server workload provided adequate file system stress in the server-level configuration, whereas
it was not sufficient for the user-level configuration. We resorted to measuring the warm up run of the
Web server workload to provide adequate stress on the file system.
3. Amdahl’s law [1] of parallel computing and “divide & conquer” is helpful in determining the critical
scalability bottleneck points. In very simple terms, Amdahl’s law states that given a program consisting
of segments that can and cannot be optimized, the performance improvement is bounded by the
segments that cannot be optimized.