Changes between Version 35 and Version 36 of FGBI
- Timestamp:
- 10/06/11 23:01:16 (13 years ago)
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FGBI
v35 v36 50 50 51 51 Figures 2a, 2b, 2c, and 2d show the type I downtime com- 52 parison among [wiki:FGBI FGBI], [wiki:LLM LLM], and [http://nss.cs.ubc.ca/remus/ Remus] mechanisms under Apache, NPB-EP,52 parison among [wiki:FGBI FGBI], [wiki:LLM LLM], and [http://nss.cs.ubc.ca/remus/ Remus] mechanisms under Apache, [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP], 53 53 SPECweb, and SPECsys applications, respectively. The block size used in all 54 54 experiments is 64 bytes. For [http://nss.cs.ubc.ca/remus/ Remus] and [wiki:FGBI FGBI], the checkpointing period is the … … 73 73 rather than Figure 2a. In conclusion, compared with [wiki:LLM LLM], [wiki:FGBI FGBI] reduces the 74 74 downtime by as much as 77%. Moreover, compared with [http://nss.cs.ubc.ca/remus/ Remus], [wiki:FGBI FGBI] yields a 75 shorter downtime, by as much as 31% under Apache, 45% under NPB-EP, 39%75 shorter downtime, by as much as 31% under Apache, 45% under [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP], 39% 76 76 under SPECweb, and 35% under SPECsys. 77 77 … … 84 84 main observations: (1) Their downtime results are very similar for "idle" run. 85 85 This is because [http://nss.cs.ubc.ca/remus/ Remus] is a fast checkpointing mechanism and both [wiki:LLM LLM] and [wiki:FGBI FGBI] are based on it. There is rare memory update for "idle" run, so the type 86 II downtime in all three mechanisms is short. (2) When running NPB-EPap-86 II downtime in all three mechanisms is short. (2) When running [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP] ap- 87 87 plication, the guest VM memory is updated at high frequency. When saved for 88 88 the checkpoint, [wiki:LLM LLM] takes much more time to save huge "dirty" data caused … … 90 90 much lower downtime than [http://nss.cs.ubc.ca/remus/ Remus] (reduce more than 70%) and [wiki:LLM LLM] (more 91 91 than 90%). (3) When running Apache application, the memory update is not so 92 much as that when running NPB, but the memory update is definitely more than92 much as that when running [http://www.nas.nasa.gov/Resources/Software/npb.html NPB], but the memory update is definitely more than 93 93 "idle" run. The downtime results shows [wiki:FGBI FGBI] still outperforms both [http://nss.cs.ubc.ca/remus/ Remus] and 94 94 [wiki:LLM LLM]. … … 101 101 Figure 3a shows the overhead during VM migration. The figure compares the 102 102 applications' runtime with and without migration, under Apache, SPECweb, 103 NPB-EP, and SPECsys, with the size of the fine-grained blocks varies from 64103 [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP], and SPECsys, with the size of the fine-grained blocks varies from 64 104 104 bytes to 128 bytes and 256 bytes. We observe that in all cases the overhead is 105 105 low, no more than 13% (Apache with 64 bytes block). As we discuss in Section 3, … … 112 112 In order to understand the respective contributions of the three proposed 113 113 techniques (i.e., [wiki:FGBI FGBI], sharing, and compression), Figure 3b shows the break- 114 down of the performance improvement among them under the NPB-EPbench-114 down of the performance improvement among them under the [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP] bench- 115 115 mark. It compares the downtime between integrated [wiki:FGBI FGBI] (which we use for 116 116 evaluation in this Section), [wiki:FGBI FGBI] with sharing but no compression support, 117 117 [wiki:FGBI FGBI] with compression but no sharing support, and [wiki:FGBI FGBI] without sharing nor 118 compression support, under the NPB-EPbenchmark. As previously discussed,119 since NPB-EPis a memory-intensive workload, it should present a clear difference among the three techniques, all of which focus on reducing the memory-118 compression support, under the [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP] benchmark. As previously discussed, 119 since [http://www.nas.nasa.gov/Resources/Software/npb.html NPB-EP] is a memory-intensive workload, it should present a clear difference among the three techniques, all of which focus on reducing the memory- 120 120 related overhead. We do not include the downtime of [wiki:LLM LLM] here, since for this 121 121 compute-intensive benchmark, [wiki:LLM LLM] incurs a very long downtime, which is more