sched/doc: Document Energy Aware Scheduling

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			   Energy Aware Scheduling

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1. Introduction

---------------

Energy Aware Scheduling (or EAS) gives the scheduler the ability to predict

the impact of its decisions on the energy consumed by CPUs. EAS relies on an

Energy Model (EM) of the CPUs to select an energy efficient CPU for each task,

with a minimal impact on throughput. This document aims at providing an

introduction on how EAS works, what are the main design decisions behind it, and

details what is needed to get it to run.

Before going any further, please note that at the time of writing:

   /!\ EAS does not support platforms with symmetric CPU topologies /!\

EAS operates only on heterogeneous CPU topologies (such as Arm big.LITTLE)

because this is where the potential for saving energy through scheduling is

the highest.

The actual EM used by EAS is _not_ maintained by the scheduler, but by a

dedicated framework. For details about this framework and what it provides,

please refer to its documentation (see Documentation/power/energy-model.txt).

2. Background and Terminology

-----------------------------

To make it clear from the start:

 - energy = [joule] (resource like a battery on powered devices)

 - power = energy/time = [joule/second] = [watt]

The goal of EAS is to minimize energy, while still getting the job done. That

is, we want to maximize:

	performance [inst/s]

	--------------------

	    power [W]

which is equivalent to minimizing:

	energy [J]

	-----------

	instruction

while still getting 'good' performance. It is essentially an alternative

optimization objective to the current performance-only objective for the

scheduler. This alternative considers two objectives: energy-efficiency and

performance.

The idea behind introducing an EM is to allow the scheduler to evaluate the

implications of its decisions rather than blindly applying energy-saving

techniques that may have positive effects only on some platforms. At the same

time, the EM must be as simple as possible to minimize the scheduler latency

impact.

In short, EAS changes the way CFS tasks are assigned to CPUs. When it is time

for the scheduler to decide where a task should run (during wake-up), the EM

is used to break the tie between several good CPU candidates and pick the one

that is predicted to yield the best energy consumption without harming the

system's throughput. The predictions made by EAS rely on specific elements of

knowledge about the platform's topology, which include the 'capacity' of CPUs,

and their respective energy costs.

3. Topology information

-----------------------

EAS (as well as the rest of the scheduler) uses the notion of 'capacity' to

differentiate CPUs with different computing throughput. The 'capacity' of a CPU

represents the amount of work it can absorb when running at its highest

frequency compared to the most capable CPU of the system. Capacity values are

normalized in a 1024 range, and are comparable with the utilization signals of

tasks and CPUs computed by the Per-Entity Load Tracking (PELT) mechanism. Thanks

to capacity and utilization values, EAS is able to estimate how big/busy a

task/CPU is, and to take this into consideration when evaluating performance vs

energy trade-offs. The capacity of CPUs is provided via arch-specific code

through the arch_scale_cpu_capacity() callback.

The rest of platform knowledge used by EAS is directly read from the Energy

Model (EM) framework. The EM of a platform is composed of a power cost table

per 'performance domain' in the system (see Documentation/power/energy-model.txt

for futher details about performance domains).

The scheduler manages references to the EM objects in the topology code when the

scheduling domains are built, or re-built. For each root domain (rd), the

scheduler maintains a singly linked list of all performance domains intersecting

the current rd->span. Each node in the list contains a pointer to a struct

em_perf_domain as provided by the EM framework.

The lists are attached to the root domains in order to cope with exclusive

cpuset configurations. Since the boundaries of exclusive cpusets do not

necessarily match those of performance domains, the lists of different root

domains can contain duplicate elements.

Example 1.

    Let us consider a platform with 12 CPUs, split in 3 performance domains

    (pd0, pd4 and pd8), organized as follows:

	          CPUs:   0 1 2 3 4 5 6 7 8 9 10 11

	          PDs:   |--pd0--|--pd4--|---pd8---|

	          RDs:   |----rd1----|-----rd2-----|

    Now, consider that userspace decided to split the system with two

    exclusive cpusets, hence creating two independent root domains, each

    containing 6 CPUs. The two root domains are denoted rd1 and rd2 in the

    above figure. Since pd4 intersects with both rd1 and rd2, it will be

    present in the linked list '->pd' attached to each of them:

       * rd1->pd: pd0 -> pd4

       * rd2->pd: pd4 -> pd8

    Please note that the scheduler will create two duplicate list nodes for

    pd4 (one for each list). However, both just hold a pointer to the same

    shared data structure of the EM framework.

Since the access to these lists can happen concurrently with hotplug and other

things, they are protected by RCU, like the rest of topology structures

manipulated by the scheduler.

EAS also maintains a static key (sched_energy_present) which is enabled when at

least one root domain meets all conditions for EAS to start. Those conditions

are summarized in Section 6.

4. Energy-Aware task placement

------------------------------

EAS overrides the CFS task wake-up balancing code. It uses the EM of the

platform and the PELT signals to choose an energy-efficient target CPU during

wake-up balance. When EAS is enabled, select_task_rq_fair() calls

find_energy_efficient_cpu() to do the placement decision. This function looks

for the CPU with the highest spare capacity (CPU capacity - CPU utilization) in

each performance domain since it is the one which will allow us to keep the

frequency the lowest. Then, the function checks if placing the task there could

save energy compared to leaving it on prev_cpu, i.e. the CPU where the task ran

in its previous activation.

find_energy_efficient_cpu() uses compute_energy() to estimate what will be the

energy consumed by the system if the waking task was migrated. compute_energy()

looks at the current utilization landscape of the CPUs and adjusts it to

'simulate' the task migration. The EM framework provides the em_pd_energy() API

which computes the expected energy consumption of each performance domain for

the given utilization landscape.

An example of energy-optimized task placement decision is detailed below.

Example 2.

    Let us consider a (fake) platform with 2 independent performance domains

    composed of two CPUs each. CPU0 and CPU1 are little CPUs; CPU2 and CPU3

    are big.

    The scheduler must decide where to place a task P whose util_avg = 200

    and prev_cpu = 0.

    The current utilization landscape of the CPUs is depicted on the graph

    below. CPUs 0-3 have a util_avg of 400, 100, 600 and 500 respectively

    Each performance domain has three Operating Performance Points (OPPs).

    The CPU capacity and power cost associated with each OPP is listed in

    the Energy Model table. The util_avg of P is shown on the figures

    below as 'PP'.

    CPU util.

      1024                 - - - - - - -              Energy Model

                                               +-----------+-------------+

                                               |  Little   |     Big     |

       768                 =============       +-----+-----+------+------+

                                               | Cap | Pwr | Cap  | Pwr  |

                                               +-----+-----+------+------+

       512  ===========    - ##- - - - -       | 170 | 50  | 512  | 400  |

                             ##     ##         | 341 | 150 | 768  | 800  |

       341  -PP - - - -      ##     ##         | 512 | 300 | 1024 | 1700 |

             PP              ##     ##         +-----+-----+------+------+

       170  -## - - - -      ##     ##

             ##     ##       ##     ##

           ------------    -------------

            CPU0   CPU1     CPU2   CPU3

      Current OPP: =====       Other OPP: - - -     util_avg (100 each): ##

    find_energy_efficient_cpu() will first look for the CPUs with the

    maximum spare capacity in the two performance domains. In this example,

    CPU1 and CPU3. Then it will estimate the energy of the system if P was

    placed on either of them, and check if that would save some energy

    compared to leaving P on CPU0. EAS assumes that OPPs follow utilization

    (which is coherent with the behaviour of the schedutil CPUFreq

    governor, see Section 6. for more details on this topic).

    Case 1. P is migrated to CPU1

    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

      1024                 - - - - - - -

                                            Energy calculation:

       768                 =============     * CPU0: 200 / 341 * 150 = 88

                                             * CPU1: 300 / 341 * 150 = 131

                                             * CPU2: 600 / 768 * 800 = 625

       512  - - - - - -    - ##- - - - -     * CPU3: 500 / 768 * 800 = 520

                             ##     ##          => total_energy = 1364

       341  ===========      ##     ##

                    PP       ##     ##

       170  -## - - PP-      ##     ##

             ##     ##       ##     ##

           ------------    -------------

            CPU0   CPU1     CPU2   CPU3

    Case 2. P is migrated to CPU3

    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

      1024                 - - - - - - -

                                            Energy calculation:

       768                 =============     * CPU0: 200 / 341 * 150 = 88

                                             * CPU1: 100 / 341 * 150 = 43

                                    PP       * CPU2: 600 / 768 * 800 = 625

       512  - - - - - -    - ##- - -PP -     * CPU3: 700 / 768 * 800 = 729

                             ##     ##          => total_energy = 1485

       341  ===========      ##     ##

                             ##     ##

       170  -## - - - -      ##     ##

             ##     ##       ##     ##

           ------------    -------------

            CPU0   CPU1     CPU2   CPU3

    Case 3. P stays on prev_cpu / CPU 0

    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

      1024                 - - - - - - -

                                            Energy calculation:

       768                 =============     * CPU0: 400 / 512 * 300 = 234

                                             * CPU1: 100 / 512 * 300 = 58

                                             * CPU2: 600 / 768 * 800 = 625

       512  ===========    - ##- - - - -     * CPU3: 500 / 768 * 800 = 520

                             ##     ##          => total_energy = 1437

       341  -PP - - - -      ##     ##

             PP              ##     ##

       170  -## - - - -      ##     ##

             ##     ##       ##     ##

           ------------    -------------

            CPU0   CPU1     CPU2   CPU3

    From these calculations, the Case 1 has the lowest total energy. So CPU 1

    is be the best candidate from an energy-efficiency standpoint.

Big CPUs are generally more power hungry than the little ones and are thus used

mainly when a task doesn't fit the littles. However, little CPUs aren't always

necessarily more energy-efficient than big CPUs. For some systems, the high OPPs

of the little CPUs can be less energy-efficient than the lowest OPPs of the

bigs, for example. So, if the little CPUs happen to have enough utilization at

a specific point in time, a small task waking up at that moment could be better

of executing on the big side in order to save energy, even though it would fit

on the little side.

And even in the case where all OPPs of the big CPUs are less energy-efficient

than those of the little, using the big CPUs for a small task might still, under

specific conditions, save energy. Indeed, placing a task on a little CPU can

result in raising the OPP of the entire performance domain, and that will

increase the cost of the tasks already running there. If the waking task is

placed on a big CPU, its own execution cost might be higher than if it was

running on a little, but it won't impact the other tasks of the little CPUs

which will keep running at a lower OPP. So, when considering the total energy

consumed by CPUs, the extra cost of running that one task on a big core can be

smaller than the cost of raising the OPP on the little CPUs for all the other

tasks.

The examples above would be nearly impossible to get right in a generic way, and

for all platforms, without knowing the cost of running at different OPPs on all

CPUs of the system. Thanks to its EM-based design, EAS should cope with them

correctly without too many troubles. However, in order to ensure a minimal

impact on throughput for high-utilization scenarios, EAS also implements another

mechanism called 'over-utilization'.

5. Over-utilization

-------------------

From a general standpoint, the use-cases where EAS can help the most are those

involving a light/medium CPU utilization. Whenever long CPU-bound tasks are

being run, they will require all of the available CPU capacity, and there isn't

much that can be done by the scheduler to save energy without severly harming

throughput. In order to avoid hurting performance with EAS, CPUs are flagged as

'over-utilized' as soon as they are used at more than 80% of their compute

capacity. As long as no CPUs are over-utilized in a root domain, load balancing

is disabled and EAS overridess the wake-up balancing code. EAS is likely to load

the most energy efficient CPUs of the system more than the others if that can be

done without harming throughput. So, the load-balancer is disabled to prevent

it from breaking the energy-efficient task placement found by EAS. It is safe to

do so when the system isn't overutilized since being below the 80% tipping point

implies that:

    a. there is some idle time on all CPUs, so the utilization signals used by

       EAS are likely to accurately represent the 'size' of the various tasks

       in the system;

    b. all tasks should already be provided with enough CPU capacity,

       regardless of their nice values;

    c. since there is spare capacity all tasks must be blocking/sleeping

       regularly and balancing at wake-up is sufficient.

As soon as one CPU goes above the 80% tipping point, at least one of the three

assumptions above becomes incorrect. In this scenario, the 'overutilized' flag

is raised for the entire root domain, EAS is disabled, and the load-balancer is

re-enabled. By doing so, the scheduler falls back onto load-based algorithms for

wake-up and load balance under CPU-bound conditions. This provides a better

respect of the nice values of tasks.

Since the notion of overutilization largely relies on detecting whether or not

there is some idle time in the system, the CPU capacity 'stolen' by higher

(than CFS) scheduling classes (as well as IRQ) must be taken into account. As

such, the detection of overutilization accounts for the capacity used not only

by CFS tasks, but also by the other scheduling classes and IRQ.

6. Dependencies and requirements for EAS

----------------------------------------

Energy Aware Scheduling depends on the CPUs of the system having specific

hardware properties and on other features of the kernel being enabled. This

section lists these dependencies and provides hints as to how they can be met.

  6.1 - Asymmetric CPU topology

As mentioned in the introduction, EAS is only supported on platforms with

asymmetric CPU topologies for now. This requirement is checked at run-time by

looking for the presence of the SD_ASYM_CPUCAPACITY flag when the scheduling

domains are built.

The flag is set/cleared automatically by the scheduler topology code whenever

there are CPUs with different capacities in a root domain. The capacities of

CPUs are provided by arch-specific code through the arch_scale_cpu_capacity()

callback. As an example, arm and arm64 share an implementation of this callback

which uses a combination of CPUFreq data and device-tree bindings to compute the

capacity of CPUs (see drivers/base/arch_topology.c for more details).

So, in order to use EAS on your platform your architecture must implement the

arch_scale_cpu_capacity() callback, and some of the CPUs must have a lower

capacity than others.

Please note that EAS is not fundamentally incompatible with SMP, but no

significant savings on SMP platforms have been observed yet. This restriction

could be amended in the future if proven otherwise.

  6.2 - Energy Model presence

EAS uses the EM of a platform to estimate the impact of scheduling decisions on

energy. So, your platform must provide power cost tables to the EM framework in

order to make EAS start. To do so, please refer to documentation of the

independent EM framework in Documentation/power/energy-model.txt.

Please also note that the scheduling domains need to be re-built after the

EM has been registered in order to start EAS.

  6.3 - Energy Model complexity

The task wake-up path is very latency-sensitive. When the EM of a platform is

too complex (too many CPUs, too many performance domains, too many performance

states, ...), the cost of using it in the wake-up path can become prohibitive.

The energy-aware wake-up algorithm has a complexity of:

	C = Nd * (Nc + Ns)

with: Nd the number of performance domains; Nc the number of CPUs; and Ns the

total number of OPPs (ex: for two perf. domains with 4 OPPs each, Ns = 8).

A complexity check is performed at the root domain level, when scheduling

domains are built. EAS will not start on a root domain if its C happens to be

higher than the completely arbitrary EM_MAX_COMPLEXITY threshold (2048 at the

time of writing).

If you really want to use EAS but the complexity of your platform's Energy

Model is too high to be used with a single root domain, you're left with only

two possible options:

    1. split your system into separate, smaller, root domains using exclusive

       cpusets and enable EAS locally on each of them. This option has the

       benefit to work out of the box but the drawback of preventing load

       balance between root domains, which can result in an unbalanced system

       overall;

    2. submit patches to reduce the complexity of the EAS wake-up algorithm,

       hence enabling it to cope with larger EMs in reasonable time.

  6.4 - Schedutil governor

EAS tries to predict at which OPP will the CPUs be running in the close future

in order to estimate their energy consumption. To do so, it is assumed that OPPs

of CPUs follow their utilization.

Although it is very difficult to provide hard guarantees regarding the accuracy

of this assumption in practice (because the hardware might not do what it is

told to do, for example), schedutil as opposed to other CPUFreq governors at

least _requests_ frequencies calculated using the utilization signals.

Consequently, the only sane governor to use together with EAS is schedutil,

because it is the only one providing some degree of consistency between

frequency requests and energy predictions.

Using EAS with any other governor than schedutil is not supported.

  6.5 Scale-invariant utilization signals

In order to make accurate prediction across CPUs and for all performance

states, EAS needs frequency-invariant and CPU-invariant PELT signals. These can

be obtained using the architecture-defined arch_scale{cpu,freq}_capacity()

callbacks.

Using EAS on a platform that doesn't implement these two callbacks is not

supported.

  6.6 Multithreading (SMT)

EAS in its current form is SMT unaware and is not able to leverage

multithreaded hardware to save energy. EAS considers threads as independent

CPUs, which can actually be counter-productive for both performance and energy.

EAS on SMT is not supported.