Tesi etd-09062021-154840
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Tipo di tesi
Dottorato
Autore
MASCITTI, AGOSTINO
URN
etd-09062021-154840
Titolo
Energy-aware Scheduling of Real-Time Tasks on ARM big.LITTLE Architectures
Settore scientifico disciplinare
INF/01
Corso di studi
Istituto di Tecnologie della Comunicazione, dell'Informazione e della Percezione - PH.D. PROGRAMME IN EMERGING DIGITAL TECHNOLOGIES (EDT)
Commissione
relatore Prof. CUCINOTTA, TOMMASO
Presidente Prof. LIPARI, GIUSEPPE
Membro Prof. BEHNAM, MORIS
Membro Prof. DI NATALE, MARCO
Membro Prof.ssa LO BELLO, LUCIA
Presidente Prof. LIPARI, GIUSEPPE
Membro Prof. BEHNAM, MORIS
Membro Prof. DI NATALE, MARCO
Membro Prof.ssa LO BELLO, LUCIA
Parole chiave
- ARM big.LITTLE
- EDF admission test
- Energy-efficiency
- Heterogeneous multicore processing
- Real-time scheduling
Data inizio appello
01/11/2021;
Disponibilità
completa
Riassunto analitico
This thesis presents Big-LITTLE Constant Bandwidth Server
(BL-CBS), a dynamic partitioning approach to schedule
real-time task sets in an energy-efficient way on multi-core
platforms based on the ARM big.LITTLE architecture. BL-CBS
is designed as an on-line and adaptive scheduler,
supporting ''open'' systems and based on a
push/pull architecture that is suitable to be incorporated
in the current SCHED_DEADLINE code base in the Linux
kernel. It employs a greedy heuristic to dynamically
partition the real-time tasks among the big and LITTLE cores
aiming to minimize the energy consumption and the migrations
imposed on the running tasks. BL-CBS is then combined with
the Task Decomposition technique already proposed in the literature
to design a methodology to be used with any Directed Acyclic Graph (DAG)
task for partitioning the real-time workload in a transparent way.
The new approach is validated
through the open-source RTSim simulator, which has been extended
integrating an energy model of the ODROID-XU3 board, fitting
tightly the power consumption profiles for the big and LITTLE cores of the board.
An extensive set of simulations has been run with randomly
generated real-time task sets,
achieving 15% of energy saving in average with respect
to the state of the art GRUB-PA when used with sequential
tasks and reaching 10% of energy saving in average over all
the performed experiments with respect to GRUB-PA
when used with DAG tasks.
When using BL-CBS in a real system, a key problem is the one of
admitting real-time tasks only if the heuristic will be able
to find on-line a suitable partitioning of all of the
the admitted workload, so to provide the expected guarantees.
Therefore, the related problem of admitting real-time tasks onto both a
symmertric multi-processor (SMP) and an ARM big.LITTLE platform,
where a partitioned EDF-based scheduler is used,
is also explored. For the SMP case, it is proposed to
combine a well-known utilization-based
test for the first-fit partitioning strategy, with a simple heuristic based
on the number of tasks and exact knowledge of the utilization of the first
few biggest tasks, while for the ARM big.LITTLE case the approach is
to combine different formulas for both the non-uniform platform (NUMP)
and the SMP cases. This results in effective and efficient tests improving the
state of the art in terms of admitted tasks, as shown by an
extensive evaluation performed on randomly generated task sets.
(BL-CBS), a dynamic partitioning approach to schedule
real-time task sets in an energy-efficient way on multi-core
platforms based on the ARM big.LITTLE architecture. BL-CBS
is designed as an on-line and adaptive scheduler,
supporting ''open'' systems and based on a
push/pull architecture that is suitable to be incorporated
in the current SCHED_DEADLINE code base in the Linux
kernel. It employs a greedy heuristic to dynamically
partition the real-time tasks among the big and LITTLE cores
aiming to minimize the energy consumption and the migrations
imposed on the running tasks. BL-CBS is then combined with
the Task Decomposition technique already proposed in the literature
to design a methodology to be used with any Directed Acyclic Graph (DAG)
task for partitioning the real-time workload in a transparent way.
The new approach is validated
through the open-source RTSim simulator, which has been extended
integrating an energy model of the ODROID-XU3 board, fitting
tightly the power consumption profiles for the big and LITTLE cores of the board.
An extensive set of simulations has been run with randomly
generated real-time task sets,
achieving 15% of energy saving in average with respect
to the state of the art GRUB-PA when used with sequential
tasks and reaching 10% of energy saving in average over all
the performed experiments with respect to GRUB-PA
when used with DAG tasks.
When using BL-CBS in a real system, a key problem is the one of
admitting real-time tasks only if the heuristic will be able
to find on-line a suitable partitioning of all of the
the admitted workload, so to provide the expected guarantees.
Therefore, the related problem of admitting real-time tasks onto both a
symmertric multi-processor (SMP) and an ARM big.LITTLE platform,
where a partitioned EDF-based scheduler is used,
is also explored. For the SMP case, it is proposed to
combine a well-known utilization-based
test for the first-fit partitioning strategy, with a simple heuristic based
on the number of tasks and exact knowledge of the utilization of the first
few biggest tasks, while for the ARM big.LITTLE case the approach is
to combine different formulas for both the non-uniform platform (NUMP)
and the SMP cases. This results in effective and efficient tests improving the
state of the art in terms of admitted tasks, as shown by an
extensive evaluation performed on randomly generated task sets.
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