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This research is based on the fact that the
processor frequency has a linear relation with its supply voltage, while the
power-consumption is a strictly increasing convex function of the
supply
voltage. The idea is to lower the supply voltage to some extend such
that the reduced processor frequency can still meet the deadlines of tasks. In this
case, we will get considerable energy-savings.
Consider a very simple example: to execute one task
with full processor frequency which needs 10 time units, while the deadline is 20
time units. If there
is no power management, the task will be executed at full processor
frequency
with the energy-consumption of: power*time = 1*10 = 10. When power
management is employed, the processor frequency can be slowed to 1/2 with
supply voltage also reduced to 1/2. The time needed will be 20 which
just meet the task's deadline. The power consumption will be 1/4, so
the energy consumption will be: power * time = 1/4 * 20 = 5. We get
the energy saving of 5 while still meet the task's deadline of 20.
- Efficient Energy Management Schemes for Parallel Systems
First, we considered a
task set of { Ti , i = 0..n } that will be executed on parallel
systems with a common deadline D, each task has a WCET (worst case execution time)
and the dependence of tasks is represented by a directed acyclic
graph (DAG). If tasks can finish well before the deadline and there
is some static slack time, or some tasks
do not use up their WCET and dynamic slack exists, we explore the
optimal schemes to schedule the tasks in a parallel systems using
the voltage/frequency scaling techniques to manage
the power consumption of systems and obtain the maximum energy savings.
With
the observation that different schedule sections may have different
parallelism due the dependence between tasks and the schedule
sections with higher parallelism should get more slack for energy
efficiency, one static power management with parallelism (SPM-P) has
been proposed, which can save more energy compared with simple
static power management (S-SPM) where the static slack is
proportionally allocated over the whole schedule regardless the
different parallelisms within the schedule. See [3] for more
details.
Considering the dynamic slack that appears online due to the
early completion of tasks, a slack sharing algorithm has been
proposed to share slack between processing units which could balance
the workload among processing units and achieve more energy savings
[1]. The result was further extended to AND/OR model applications where only a
subset of tasks maybe executed during any executions [2].
Although processors normally consume a significant amount
of power in the computing system (especially embedded
systems), other power consuming components may also be managed (such
as low power memory). Considering the power consumption in a whole
system, we have proposed a simple power model that could be easily
incorporated in previous voltage/frequency scaling algorithms [4].
Considering static/leakage power in a system, it may not be
always energy efficient to operating systems at a lower speed. For
clusters of multiple servers, considering the current/expected
workload, it could be more energy efficient to power off some
servers and let others operate at appropriate speed for maximal
energy savings. Based on this observation, energy efficient schemes
have been studied for clusters. See [5] for more details.
- Energy Efficient Fault Tolerance in Parallel Systems
For reliable systems, fault tolerance will be an important
concern in addition to energy consumption. Considering the interplay
between power consumption and system reliability for different
degree of redundancy, we have studied the speed setting problem for
an optimistic-TMR scheme, where one unit of a traditional TMR system
is slowed down for saving energy provided that other two units do
not have an agreement. See [4] for more details.
Moreover, for parallel reliable servers, different redundancy
configurations will lead to different level of system reliability as
well as different amount of energy consumption. For independent
unit-size requests, we analyzed the optimal redundant configurations
of the servers to minimize energy consumption for a given
reliability goal, or to maximize system reliability for a given
energy budget. See [6] for more details.
Future work: We have
ignore the communication and task migration overhead in our previous
study, which maybe significant, especially in distributed systems.
For systems that communicate through wireless channel, the combined
power management for computation and communication would be
interesting to explore. Considering tasks in an application may have
separate deadline, extending our work for applications with tasks
having different deadlines (or periodic task) on parallel real-time
systems could also be the next step.
[1] Dakai
Zhu, Rami Melhem, and Bruce R. Childers.
Scheduling with Dynamic
Voltage/Speed Adjustment Using Slack Reclamation in Multi-Processor
Real-Time Systems, in IEEE Real-Time Systems Symposium
(RTSS'01), London, England, Dec 2001;
[2] Dakai Zhu, Nevine AbouGhazaleh, Daniel Mossé and Rami Melhem.
Power Aware Scheduling for AND/OR Graphs in Multi-Processor
Real-Time Systems, in International Conference on Parallel
Processing (ICPP'02), Vancouver, B.C. Canada, Aug., 2002;
[3] Ramish Mishra, Namrata Rastogi, Dakai Zhu,
Daniel Mossé and Rami Melhem,
Energy Aware Scheduling for
Distributed Real-Time Systems, in International Parallel &
Distributed Processing Symposium (IPDPS'03), pages 21 - 29, Nice France, Apr.
2003.
[4] Dakai Zhu, Rami Melhem, Daniel Mossé and E. (Mootaz) Elnozahy.
Analysis of an Energy Efficient Optimistic TMR Scheme
in
International Conference on Parallel and Distributed Systems (ICPADS),
Newport Beach, CA, Jul. 2004;
[5] Ruibin Xu, Dakai Zhu, Cosmin Rusu, Rami
Melhem and Daniel Mossé, Energy Efficient Policies for Embedded
Clusters, in Proc. of the Conference on Language, Compilers,
and Tools for Embedded Systems (LCTES'05), pages 1 - 10, Chicago, Jun. 2005.
[6] Dakai Zhu, Rami Melhem and Daniel Mossé,
Energy Efficient Configuration for QoS in Reliable Parallel Servers,
in Proc. of the Fifth European Dependable Computing Conference (EDCC'05),pages
122 - 139, Budapest, Hungary, Apr. 2005
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