Efficiency Analysis of Decentralized
Grid Scheduling with Job Migration
and Replication
International Conference on Ubiquitous Information Management and Communication (ACM ICUIMC 2013)
January 17β19, 2012, Kota Kinabalu, Malaysia
[email protected], [email protected]
Rzhanov Institute of Semiconductor Physics Siberian Branch of Russian Academy of Sciences
Siberian State University of Telecommunications and Information Sciences
Mikhail Kurnosov, Alexey Paznikov
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Decentralized Grid Scheduling
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 2M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦
Subsystem1
EC1 EC2 ECn1
β¦
RMS
Local jobs
Subsystem3
EC1 EC2 ΠΠn3
β¦
RMS
Subsystem2
EC1 EC2 ECn2
β¦
RMS
Subsystem4
EC1 EC2 ECn4
β¦
RMS
Local jobs
Local jobs Local jobsJob
r β number of processes,
z1, z2, β¦, zk β stage-in files sizes,
h1, h2, β¦, hk β subsystems with stage-in files.
ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Scheduling of Parallel MPI-programs
in Geographically-distributed Computer Systems
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 3
Centralized
scheduling
β’ GridWay
β’ GrADS
β’ AppLeS
β’ Condor-G
β’ Nimrod/G
β’ WMS
Decentralized
scheduling
β’ GBroker
β’ (Korneev, 1985)*,
(Monakhov, 2000)**
Korneev V.V. Architecture of Computer Systems with Programmable Structure. Novosibirsk, 1985.
Monakhov O.G., Monakhova E.A. Parallel Systems with Distributed Memory. Resource and Task Management. Novosibirsk, 2001.
*
**
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Decentralized Scheduling of Parallel Programs
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 4
Scheduler1
EC1 EC2 ECn1
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Scheduler2
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn4
β¦
RMS
Scheduler4
Local jobs Local jobs
Local jobs Local jobs
Subsystem1
Subsystem3
Subsystem2
Subsystem4
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Algorithms of Decentralized Scheduling
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 5
i j1
j2
ju
Scheduler Scheduler
Scheduler
Scheduler
The algorithms are proposed:
β’ Locally optimal scheduling (LOS),
β’ Based on job replication (RS),
β’ Based on job migration (MS),
β’ Based on job migration and
job replication (MRS).
Each of the algorithms describes
functioning of the scheduler i after
submitting a job to its queue.
Step 0: tij, cj, sj, qj and nj are obtained from the monitoring system, then
the set S(i) = {j | nj β₯ r, j β L(i) βͺ {i}} is built up.
L(i) ={ j1, j2, β¦, ju }
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 6
Locally Optimal Scheduling (LOS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Job
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem3
Subsystem2
Subsystem4
++
=
β
β
,
,
)(
max
max
1
max
1
max
t
t
w
w
c
c
t
t
jFj
jjjif cj < r or qj > 0,
else.
β=
=
k
l
llj zjhtt1
),,(
cj β number of non-used EC on subsystem j;
qj β number of jobs in the queue;
wj = qj / nj β number of jobs in the queue per one EC
tj β time of staging-in to the subsystem j
Step 1. Subsystem j* = argmin{F(j)} is chosed
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 7
Locally Optimal Scheduling (LOS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Job
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem2
Subsystem1
Subsystem3
Step 2. Job is submitted to the local RMS
of subsystem j*.
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 8
Scheduling Based on Job Replication (RS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Job
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem2
Subsystem1
Subsystem3
Step 1. m subsystem j*1, j*2, β¦, j*m β S(i)
with the least function F(j) value are chosen
cj β number of non-used EC on subsystem j;
qj β number of jobs in the queue;
wj = qj / nj β number of jobs in the queue per one EC
tj β time of staging-in to the subsystem j
++
=
β
β
,
,
)(
max
max
1
max
1
max
t
t
w
w
c
c
t
t
jFj
jjjif cj < r or qj > 0,
else.
β=
=
k
l
llj zjhtt1
),,(
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 9
Scheduling Based on Job Replication (RS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem2
Subsystem1
Subsystem3
Step 2. Job are submitted to the queues of
subsystems j*1, j*2, β¦, j*m.
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
JobJob
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 10
Scheduling Based on Job Migration (MS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Job
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem2
Subsystem1
Subsystem3
Step 1. With the interval β1 locally optimal subsystem
j' search procedure is performed:
j'= argmin{F(j)}
Step 2. If F(j*) β F(j') > Ξ΅, then migration is
performed from subsystem j to the subsystem j'.
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.
Scheduleri
ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 11
Scheduling Based on Combined Approach (MRS)
EC1 EC2 ECni
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler2
Scheduler1
EC1 EC2 ECn2
β¦
RMS
EC1 EC2 ECn3
β¦
RMS
Scheduler3
Job
Local jobs
Local jobs
Local jobs
Local jobs
Subsystemi
Subsystem2
Subsystem1
Subsystem3
The algorithm is based on combination of two
approaches β replication to the multiple subsystem
and migration between the subsystems.
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
GBroker Functional Structure
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 12
β software, developed in CPCT SibSUTIS
β third-party software
Local RMS
Globus Toolkit DCSMon
GBroker
NetMon
GClient
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Multicluster Computer System Testbed
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 13
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GBroker
NetMon DCSMon
Globus Toolkit 5.0
GridWay 5.6.1
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
MPI-programs (SPEC MPI2007):
β’ Weather Research and Forecasting (WRF) β simulation of climate
processes.
β’ The Parallel Ocean Program (POP) β simulation of processes in the
ocean.
β’ LAMMPS β molecular dynamics simulation tools.
β’ RAxML β bioinformatics modeling.
β’ Tachyon β graphical rendering.
Jobs flows:
⒠Simple flows with flux level λλλλ.
β’ The jobs are chosen randomly with uniform distribution.
β’ Rank r of parallel program is chosen from the set {1, 2, 4, 8}.
β’ Stage-in files were on the subsystem Xeon80.
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Experiments Organization
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 14M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
β’ System bandwidth B
B = M / Ο
β’ Mean queue waiting time W
β’ Mean job service time T
Notations:
β’ tk β job submitting time to the scheduler k β {1, 2, β¦, M}.
β’ β time of job k beginning execution.
β’ β time of job k finishing execution.
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Indices of Efficiency of Scheduling Algorithms
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 15
ktβ²
kt β²β²
β=
ββ²=
M
k
kk ttM
W1
)(1
β=
ββ²β²=
M
k
kk ttM
T1
)(1
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Flow of M = 200 jobs was submitted to the subsystem Xeon80
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency comparison of algorithms LOS and RS
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 16
System bandwidth B
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency comparison of algorithms LOS and RS
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 17
Mean queue waiting time W Mean job service time T
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency Comparison LOS, MS, MRS
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 18
Flow of M = 200 jobs was submitted to the subsystem Xeon80
System bandwidth B
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency Comparison LOS, MS, MRS
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 19
Mean queue waiting time W Mean job service time T
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π° Π± Π²
Π³ Π΄ Π΅
Π° β full graph, Π± β D2(6; 2, 3), Π² β ring, Π³ β mesh, Π΄ β 2D-torus, Π΅ β star
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Schedulers Logical Structures
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 20M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Flows of M = 50 jobs were submitted to all the subsystem (algorithm MS)
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Schedulers Logical Structures
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 21
System bandwidth B
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Schedulers Logical Structures
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 22
Mean queue waiting time W Mean job service time T
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency comparison of schedulers GBroker and GridWay
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 23
GBroker GBroker GBroker GBroker
GBroker GBroker
NetMon DCSMon
Globus Toolkit 5.0
NetMon DCSMon
Globus Toolkit 5.0
NetMon DCSMon
Globus Toolkit 5.0
NetMon DCSMon
Globus Toolkit 5.0
NetMon DCSMon
Globus Toolkit 5.0
GridWay 5.6.1
NetMon DCSMon
Globus Toolkit 5.0
GridWay configuration(recommended by developers)
SCHEDULING_INTERVAL (interval of scheduling) = 5 s;
DISCOVERY_INTERVAL (interval of resource discovery) = 100 s;
MONITORING_INTERVAL (interval of resource state monitoring) = 2 s;
POLL_INTERVAL (interval of job state query) = 10 s;
DISPATCH_CHUNK (number of jobs, dispatched simultaneously) = 15.
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency comparison of schedulers GBroker and GridWay
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 24
System bandwidth B
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Efficiency comparison of schedulers GBroker and GridWay
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 25
Mean queue waiting time W Mean job service time T
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Algorithms usage recommendations
β’ Small stage-in files β LOS, MS, RS, MRS.
β’ Large stage-in files β LOS and MS.
β’ Low rate of job flows or small stage-in files β RS and MRS
LOS β locally optimal scheduling,
RS β based on job replication,
MS β based on job migration,
MRS β based on job migration and job replication
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Recommendations
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 26M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Conclusion
β’ Job migration helps increase system bandwidth.
β’ More system bandwidth (algorithm MS) of decentralized scheduling as
compared with centralized scheduling (GridWay).
β’ Usage of non-full graphs with small diameter (e.g. ND-torus, D2-graphs) as
logical structures of decentralized schedulers do not leads to job service
efficiency degradation.
β’ Decentralized algorithms are independent from the number of subsystems
and allows to robustness job flows service.
Future Works
β’ Algorithms of scheduling Grid workflows
β’ Forecasting methods in network and subsystems monitoring
β’ Generation of optimal local neighborhood structures
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Conclusions and Future Works
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 27M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
Thank you for your attention!
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 28M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
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Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
if ci β₯ r ΠΈ qi = 0,
else
Subsystem Ranking
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 29M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
1 β LOS, F1; 2 β LOS, F2; 3 β LOS, F3
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ-ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ.
ΠΡΠΎΠΏΡΡΠΊΠ½Π°Ρ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΡΠΈΡΡΠ΅ΠΌΡ
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 30
B, jobs/s
Ξ», s-1
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
1 β LOS, F1; 2 β LOS, F2; 3 β LOS, F3
Π±Π°
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ-ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ. Π‘ΡΠ΅Π΄Π½Π΅Π΅ Π²ΡΠ΅ΠΌΡ
ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ Π² ΠΎΡΠ΅ΡΠ΅Π΄ΠΈ ΠΈ ΡΡΠ΅Π΄Π½Π΅Π΅ Π²ΡΠ΅ΠΌΡ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 31
Ξ», s-1 Ξ», s-1
W, s T, s
Mean queue waiting time W Mean job service time T
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19
1 β forecasting by NetMon; 2 β time of GridFTP
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ²
Forecasting of File Transfer Time
Π‘Π΅ΠΌΠΈΠ½Π°Ρ Β«ΠΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», ΠΠΎΠ²ΠΎΡΠΈΠ±ΠΈΡΡΠΊ, 29 Π°ΠΏΡΠ΅Π»Ρ 2011 Π³.ΠΠΈΡ Π°ΠΈΠ» ΠΡΡΠ½ΠΎΡΠΎΠ², ΠΠ»Π΅ΠΊΡΠ΅ΠΉ ΠΠ°Π·Π½ΠΈΠΊΠΎΠ² 32
Time of transfer file with size
m = 100 ΠΠ± between subsystems
Xeon80 ΠΈ L400l1
Time of transfer file with size
m = 1 ΠΠ± between subsystems
Xeon80 ΠΈ L400l1
M. Kurnosov, A. Paznikov. Efficiency Analysis of Decentralized Grid Scheduling β¦ ACM ICUIMC, Kota Kinabalu, Malaysia, January 17-19