同步感知能源管理：VFI多核实时系统的新策略

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"这篇论文探讨了在基于电压和频率岛（VFI）的多核实时系统中的同步感知能源管理策略。研究重点在于如何通过优化任务调度、资源分配和动态松弛管理来提高系统的能效和可调度性。" 在当前的多核处理器中，电压和频率岛（VFI）技术被广泛应用于能源管理，它允许处理器根据工作负载动态调整电压和频率，以降低功耗。针对一组定期执行的实时任务，这些任务在VFI系统上运行时会共享资源并使用DVFS功能，研究者提出了静态和动态的同步感知能源管理计划。首先，研究者基于增强型的MSRP（Multi Server Resource Partitioning）资源访问协议，设计了一种同步感知的分区调度任务映射算法。这个启发式方法考虑了任务之间的同步开销，通过将访问相似资源的任务分配到同一个核心，有效减少了同步延迟，从而提高了任务的可调度性。接下来，研究者对静态方案进行了分析，探讨了如何在不同的VFI上为任务分配均匀或不均匀的频率。这涉及到任务频率的最优比例，以便在满足任务时序和同步约束的同时，最大化能效。为了进一步挖掘动态余量，论文提出了一个集成的同步感知松弛管理框架。该框架能够在运行时适当地回收、保留、释放和窃取松弛，以应对VFI的电压/频率限制和任务的时序/同步约束。这一框架考虑了由任务同步引起的额外延迟，公平地分配空闲时间，并压缩任务的非关键部分，使得关键部分可以更快执行，从而节省更多能源。仿真结果显示，同步感知映射显著提升了任务的可调度性。静态方案在不同VFI上的任务频率设置接近于通过非线性编程（INLP）方法得出的最佳解，实现了良好的节能效果。同时，提出的策略与现有的单处理器系统解决方案相比，表现出更优的性能。这篇论文深入研究了多核实时系统中VFI的能源管理问题，提出了同步感知的方法，以提升系统的能效和实时性，为多核处理器的能源优化提供了新的思路。

that a task can be executed in parallel, Lee introduced an

energy-saving scheduling scheme that turns off rarely used

cores [33]. In [37], Qi and Zhu studied energy efficiency for

different partitioning of cores to VFIs and proposed an

asymmetric buddy VFI configuration that can effectively

support various workload with great energy savings.

For real-time tasks that access nonpreemptable shared

resources, several resource access protocols have been

studied for uniprocessor real-time systems, such as

Priority Ceiling Protocol (PCP) [42] and Stack Resource

Policy (SRP) [8]. The protocols have been extended for

multiprocess or systems, s uch as Multiprocessor PCP

(MPCP) [32], and Multiprocessor SRP (MSRP) [23]. More

recently, Block et al. introduced a Flexible Multiprocessor

Locking Protocol (FMLP) [10] and Brandenburg and

Anderson studied suspension-based optimal locking pro-

tocols (namely, OMLP) [12].

Based on the resource access protocols, a few studies have

focused on energy management with task synchronization

for uniprocessor systems. Jejurikar and Gupta studied a

static Uniform Slowdown with Frequency Inheritance (USFI)

scheme, where a job inherits the maximum frequency of its

blocked jobs when it executes a critical section [29]. With

intuition that executing low priority tasks at a higher speed

can be more effective to reduce the blocking time between

tasks, Chen et al. studied two static frequency assignment

algorithms for uniprocessor systems with task synchroniza-

tion to obtain better energy savings [17]. However, both

studies did not consider dynamic energy management.

In [46], Zhang and Chanson proposed a Dual-Speed (DS)

algorithm for uniprocessor systems, where two (i.e., high

and low) speeds are calculated offline for each task to

satisfy the feasibility condition under the worst case

scenarios. At runtime, when no job is blocked, the low

speed with slack reclamation is applied for energy savings.

However, when blocking occurs, the high speed is needed

to guarantee deadlines of blocked tasks. Different from all

existing work that has focused on uniprocessor systems, in

this paper, we focus on VFI-based multicore real-time

systems with shared resources and study both static and

dynamic synchronization-aware schemes for periodic tasks,

which exploit spare system utilization and dynamic slack,

respectively, for energy savings.

3PRELIMINARIES

3.1 System and Power Models

We consider a real-time system with a multicore processor

that has M identical processing cores fO

; ...; O

g. The

cores are organized into V groups fG

; ...; G

g, where each

group has the same

number of cores that are on a voltage

and frequency island. That is, we assume that M is a multiple

of V and the number of cores on each VFI is M=V.

Moreover, each VFI is assumed to have a DVFS-enabled

voltage supply that can provide D discrete supply voltages v

and corresponding highest processing frequencies

ð1  i  DÞ. The cores on a VFI share the common DVFS-

enabled voltage supply and thus have the same processing

frequency. However, considering that cores can be easily put

into power saving states [1], [2], each core is assumed to have

two states: active and sleep. When a core does not have

workload to process, it becomes inactive and is put to the

sleep state for energy savings. When all cores on a VFI are in

the sleep state, we can power off the VFI to save more energy.

For the widely used CMOS technology, dynamic power

dissipation is generally a dominant component, which is

quadratically related to supply voltage and linearly related

to processing frequency [13]. However, with scaled feature

size and increased level of integration, leakage power has

been ever-increasing and it has become a necessity for

effective energy management to consider both dynamic and

leakage power components [5], [15], [30]. In this work, we

adopt the power model

utilized in [41], where the power

consumption of a multicore system P

sys

can be given by

sys

i¼1

h

 P

dyn

ðfðO

ÞÞ þ P

leak

ðfðO

ÞÞ: ð1Þ

Here, h

indicates the state of core O

. When O

is active,

h

¼ 1; otherwise h

¼ 0. When core O

runs at frequency

fðO

Þ (and the corresponding voltage vðO

Þ), its dynamic

power consumption can be given as [13]:

dyn

ðfðO

ÞÞ ¼ C

 vðO

 fðO

Þ: ð2Þ

The leakage power P

leak

ðfðO

ÞÞ, which exists even when

a core is put to sleep, can be given as [41]:

leak

ðfðO

ÞÞ ¼ L

ðvðO

ÞI

sub

þjV

jI

Þ; ð3Þ

sub

¼ K

 e

vðO

 e

V

; ð4Þ

where I

sub

and I

are leakage currents and V

, L

, K

and K

are system-dependent constants [41].

Note that energy is the integral of power over time. From

the above equations, we can see that, reducing processing

frequency of a core can lower the energy consumption due

to dynamic power while increasing the one due to leakage

power. Therefore, there exists a critical frequency f

crit

below which more energy may be consumed [5], [15], [30],

[37]. To simplify our discussion, we assume that the

minimum available frequency f

min

¼ f

 f

crit

. Moreover,

assuming normalized frequencies, we have the maximum

frequency to be f

max

¼ f

¼ 1. In addition, the overhead

caused by voltage and frequency changes is assumed to be

negligible (or such overhead can be incorporated into slack

reclamation or the worst case execution time (WCET) of

tasks [48]).

3.2 Task and Resource Models

We consider a set of N periodic real-time tasks  ¼

; ...;T

g, where each task T

has a period p

that is also

its relative deadline. The jth task instance (or job) of task T

arrives at time ðj  1Þp

and has to complete by its absolute

deadline j  p

. Since there is only one active job for each task

at any time, without causing ambiguity, we use task and job

exchangeably in this paper.

1684 IEEE TRANSACTIONS ON COMPUTERS, VOL. 61, NO. 12, DECEMBER 2012

1. Although it is possible to have heterogeneous VFIs that contain

different number of cores [37], such options can increase processor design

complexity.

2. Note that, similar power models have been adopted in other recent

work on power management [5], [15], [30].

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