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CATEGORIES:College of Engineering,Graduate Studies,Lectures and Seminars,Th
 esis/Dissertations
DESCRIPTION:Faculty Supervisor: Dr. Gokhan Kul, Computer & Information Scie
 nce Committee Members: Dr. Adnan El-Nasan, Computer & Information ScienceD
 r. John Rahme, Computer & Information Science Abstract:ÌýMemory allocators
  perform a fundamental task in ensuring dynamic memory is allocated to pro
 cesses in a way that is efficient in both time and space domains. They do 
 this by reducing the number of kernel calls performed, maintaining high sp
 atial and cache locality, and keeping the number of pages in use to a mini
 mum. However, there is a fundamental constraint towards the efficiency of 
 an allocator. External fragmentation is an unavoidable consequence of dyna
 mic allocation, yet there is little consensus on how precisely to measure 
 fragmentation; There is vanishingly little analysis on how different fragm
 entation metrics perform or when each should be used. A better understandi
 ng of fragmentation and how to measure it is vital in advancing allocator 
 design and designing programs that respect allocators’ assumptions. ÌýTh
 ere is a large myriad of work introducing various metrics for measuring fr
 agmentation or describing allocators that reduce fragmentation in some nov
 el way under particular workloads. However, there is a gap in modern work 
 determining the efficacy of the numerous proposed fragmentation metrics, w
 hen they should be used, and how to compare them between each other. Many 
 papers discussing fragmentation produced under some workload or allocator 
 don’t describe which metric is being used. This disjoint approach to mea
 suring fragmentation leads to confusion and imprecision. Much of the work 
 on fragmentation is old, dating back 50 years. Newer work tends to be focu
 sed on allocation on either large clusters or databases, or in optical net
 work allocation rather than memory. Additionally, there has been a focus o
 n analyzing synthetic traces (and how precisely to generate synthetic trac
 es), rather than using traces from real workloads on real machines. This w
 ork attempts to unify the understanding of fragmentation through comparati
 ve analysis on real workloads. This work introduces a pipeline for benchma
 rking external fragmentation metrics using real memory traces. First is a 
 method of collecting memory traces of multi-threaded and multi-process sys
 tems, tracking every call to the allocation library. Next a method of comp
 uting the fragmentation of these traces at variable timesteps through the 
 parent process’ lifetime. And finally, a method of plotting metrics over
  time against each other and against the overall memory usage of that proc
 ess. 6 metrics are compared in this work, including two novel metrics; AEF
 M and NAEFM, and one metric that has escaped academic review; ESP. The ESP
  metric performs extremely well, holding high average correlation with oth
 er metrics, while maintaining much more stability on shorter applications.
  For longer running applications, many metrics quickly lose specificity, r
 eaching their maximum value quite quickly and staying there. NAEFM, SSFM, 
 and EBFM maintain usefulness across the lifetime of longer running program
 s. ÌýThis work has applications in a wide range of areas. It is naturally 
 of interest to kernel or allocator developers. Future work which takes adv
 antage of findings from this comparative analysis may produce more efficie
 nt allocators on small and personal computers. A better understanding of m
 emory degradation over time will result in more efficient applications, re
 ducing the overall resource burden of everyday computing. Additionally, th
 ere may be applications in adjacent fields as found with optical network a
 llocation. Ìý For further information please contact Dr. Gokhan Kul at gku
 l@umassd.edu.ÌýÌý Ìý\nEvent page: /events/cms/benchm
 arking-of-memory-fragmentation-metrics-on-real-allocation-traces.php\nEven
 t link: https://teams.microsoft.com/meet/28305274898079?p=Hq5YC1bJl1SW8ioW
 En
X-ALT-DESC;FMTTYPE=text/html:<html><Body><h1><a href="/">ÌÇÐÄlogoÈë¿Ú</a></h1><script>
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</script><p>Faculty Supervisor: <br />Dr. G
 okhan Kul\, Computer & Information Science</p>\n<p>Committee Members: <br 
 />Dr. Adnan El-Nasan\, Computer & Information Science<br />Dr. John Rahme\
 , Computer & Information Science</p>\n<p><strong>Abstract:</strong>ÌýMemor
 y allocators perform a fundamental task in ensuring dynamic memory is allo
 cated to processes in a way that is efficient in both time and space domai
 ns. They do this by reducing the number of kernel calls performed\, mainta
 ining high spatial and cache locality\, and keeping the number of pages in
  use to a minimum. However\, there is a fundamental constraint towards the
  efficiency of an allocator. External fragmentation is an unavoidable cons
 equence of dynamic allocation\, yet there is little consensus on how preci
 sely to measure fragmentation\; There is vanishingly little analysis on ho
 w different fragmentation metrics perform or when each should be used. A b
 etter understanding of fragmentation and how to measure it is vital in adv
 ancing allocator design and designing programs that respect allocators’ 
 assumptions. ÌýThere is a large myriad of work introducing various metrics
  for measuring fragmentation or describing allocators that reduce fragment
 ation in some novel way under particular workloads. However\, there is a g
 ap in modern work determining the efficacy of the numerous proposed fragme
 ntation metrics\, when they should be used\, and how to compare them betwe
 en each other. Many papers discussing fragmentation produced under some wo
 rkload or allocator don’t describe which metric is being used. This disj
 oint approach to measuring fragmentation leads to confusion and imprecisio
 n. Much of the work on fragmentation is old\, dating back 50 years. Newer 
 work tends to be focused on allocation on either large clusters or databas
 es\, or in optical network allocation rather than memory. Additionally\, t
 here has been a focus on analyzing synthetic traces (and how precisely to 
 generate synthetic traces)\, rather than using traces from real workloads 
 on real machines. This work attempts to unify the understanding of fragmen
 tation through comparative analysis on real workloads. This work introduce
 s a pipeline for benchmarking external fragmentation metrics using real me
 mory traces. First is a method of collecting memory traces of multi-thread
 ed and multi-process systems\, tracking every call to the allocation libra
 ry. Next a method of computing the fragmentation of these traces at variab
 le timesteps through the parent process’ lifetime. And finally\, a metho
 d of plotting metrics over time against each other and against the overall
  memory usage of that process. 6 metrics are compared in this work\, inclu
 ding two novel metrics\; AEFM and NAEFM\, and one metric that has escaped 
 academic review\; ESP. The ESP metric performs extremely well\, holding hi
 gh average correlation with other metrics\, while maintaining much more st
 ability on shorter applications. For longer running applications\, many me
 trics quickly lose specificity\, reaching their maximum value quite quickl
 y and staying there. NAEFM\, SSFM\, and EBFM maintain usefulness across th
 e lifetime of longer running programs. ÌýThis work has applications in a w
 ide range of areas. It is naturally of interest to kernel or allocator dev
 elopers. Future work which takes advantage of findings from this comparati
 ve analysis may produce more efficient allocators on small and personal co
 mputers. A better understanding of memory degradation over time will resul
 t in more efficient applications\, reducing the overall resource burden of
  everyday computing. Additionally\, there may be applications in adjacent 
 fields as found with optical network allocation. Ìý</p>\n<p>For further in
 formation please contact Dr. Gokhan Kul at <a href="mailto:gkul@umassd.edu
 ">gkul@umassd.edu</a>.ÌýÌý</p>\n<p>Ìý</p><p>Event page: <br>Event
  link: </p></body></html>
DTSTAMP:20260428T191632
DTSTART;TZID=America/New_York:20260511T100000
DTEND;TZID=America/New_York:20260511T110000
LOCATION:Dion 311 and Teams (https://teams.microsoft.com/meet/2830527489807
 9?p=Hq5YC1bJl1SW8ioWEn)
SUMMARY;LANGUAGE=en-us:Benchmarking of Memory Fragmentation Metrics on Real
  Allocation Traces
UID:f317da6798981906ed321125a3438482@www.umassd.edu
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