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School of Computer Science Carnegie Mellon University
 

Summary: School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Modern servers typically process request streams by assigning a worker thread to a request, and rely
on a round robin policy for context-switching. Although this programming paradigm is intuitive, it
is oblivious to the execution state and ignores each software module's affinity to the processor
caches. As a result, resumed threads of execution suffer additional delays due to conflict and com-
pulsory misses while populating the caches with their evicted working sets. Alternatively, the staged
programming paradigm divides computation into stages and allows for stage-based (rather than
request thread-based) cohort scheduling that improves module affinity.
This technical report introduces (a) four novel cohort scheduling techniques for staged software
servers that follow a "production-line" model of operation, and (b) a mathematical framework to
methodically quantify the performance trade-offs when using these techniques. Our Markov chain
analysis of one of the scheduling techniques matches the simulation results. Using our model on a
staged database server, we found that the proposed policies exploit data and instruction locality for a
wide range of workload parameter values and outperform traditional techniques such as FCFS and
processor-sharing. Consequently, our results justify the restructuring of a wide class of software
servers to incorporate the staged programming paradigm.
Email: {stavros, natassa}@cs.cmu.edu

  

Source: Ailamaki, Anastassia - School of Computer Science, Carnegie Mellon University
Carnegie Mellon University, School of Computer Science
Carnegie Mellon University, School of Computer Science, Informedia Project
Massachusetts Institute of Technology (MIT), Department of Electrical Engineering and Computer Science, Database Group

 

Collections: Computer Technologies and Information Sciences