Proactive Workload Management for Bare Metal

Deployment on Microservers

Daniel Schlitt

, Christian Pieper

and Wolfgang Nebel

OFFIS - Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany

Carl von Ossietzky Universit

at Oldenburg, Ammerl

ander Heerstraße 114-118, 26111 Oldenburg, Germany

Keywords:

Bare Metal, Data Center, Energy Efﬁciency, Forecast, Microserver, OpenStack, Workload Management.

Abstract:

This paper introduces a concept for an energy-aware workload management (WM) for heterogeneous mi-

croserver environments. Its main focus is on highly dynamic service-driven workloads often coupled to user

requests requiring fast response times. The WM is developed in scope of the M2DC (Modular Microserver

Data Center) project, in which a new server generation of composable microservers is designed. Targeting

an easy industrial applicability, the underlying middleware is based on a turnkey OpenStack platform. As

part of that middleware, the WM makes use of workload/utilization and power data as well as correspond-

ing (prediction) models to deploy applications on the most suitable microservers and temporarily shut down

unused capacities, either proactively or reactively (in case of deviations from forecasts). The WM has been

implemented and simulated within a virtual environment. However, the integration, reﬁnement and evaluation

on the new M2DC hardware is still work in progress.

1 INTRODUCTION

Technically, the workload handled by computer sys-

tems can be separated into two rough types. First,

there are high performance workloads or batch jobs

which are placed once and just need to be completed

in a certain time frame. Besides, there are transactio-

nal or service-driven workloads, which usually con-

sist of smaller requests placed by users expecting a

timely response. In terms of workload management

(WM), the ﬁrst mentioned domain beneﬁts from pop-

ular management tools like SLURM, while the lat-

ter have either limited support or functionality – es-

pecially in commercial off-the-shelf (COTS) manage-

ment SW.

Nonetheless, dynamic management of transac-

tional workloads holds high potentials. The tight user

coupling makes a both effective and efﬁcient static

operation complicated, as the workload and user be-

havior may be highly dynamic. That means the hard-

ware capacity has to be speciﬁed for the maximum

user demand based on experience and estimations.

Additionally, low response times are highly important

from the user’s point of view. These factors make it

impossible to ﬁnd an optimal static allocation, as the

dynamic workload behavior has to be considered for

compute node selection and application deployment.

In contrast to high performance data centers, which

try to fully utilize all of its compute nodes at any time,

in the case of service-based computing the hardware

usage has to be adapted to actual user demand. By dy-

namically adapting the number and selection of active

nodes, the energy efﬁciency of data center operation

can be massively increased.

Therefore, we realize an energy-aware WM based

on a two-folded approach: (1) we optimize the cur-

rent state of operation (active compute nodes, appli-

cation allocation) based on workload/utilization pre-

dictions as well as performance/power models and

(2) in the case of non-matching forecasts we use a

reactive allocation to adjust the amount of deployed

applications, while the deviating prediction models

are re-trained. This currently ongoing work is done

in scope of the EU H2020 project M2DC

(Modular

Microserver Data Center), which aims to provide full

server appliances with a convincing total cost of own-

ership (TCO). The project is based on three pillars

(Oleksiak et al., 2016): It offers freely conﬁgurable

heterogeneous microservers of different architectures

(x86/64, ARM64) and hardware accelerators (GPU,

FPGA). Furthermore, there is a layer of advanced

management strategies e.g. to optimize the overall en-

http://www.m2dc.eu

246

Schlitt, D., Pieper, C. and Nebel, W.

Proactive Workload Management for Bare Metal Deployment on Microservers.

DOI: 10.5220/0006762202460253

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 246-253

ISBN: 978-989-758-292-9

Intelligent Power

Management

Photo HPC Cloud Your

Data Centre

integration

Figure 1: M2DC project scheme.

ergy efﬁciency. The described WM within this paper

would be part of that layer. In order to enable an easy

industrial applicability, M2DC is designed as turnkey

platform with well-deﬁned interfaces to the surround-

ing ecosystem (see Figure 1). For that purpose, the

middleware is based on OpenStack, which is an open

source software project offering a free cloud comput-

ing architecture. It is backed up by a large commu-

nity, which steadily extends OpenStack’s function-

alities. New functions and features are made avail-

able through components, which implement e.g. sup-

port for networking infrastructure (Neutron), work-

load scheduling (Nova), management of bare metal

resources (Ironic) or a web interface (Horizon). The

WM, introduced in this paper, is part of the middle-

ware and has the objective to dynamically optimize

the server operation to contribute to the overall TCO

reduction. As OpenStack including the Ironic

com-

ponent – its bare metal provisioning module – is used

as a base platform for the middleware, the WM is con-

ceptionalized as an add-on to OpenStack.

In this paper, we describe the conception of the

WM. Following the Introduction, Section 2 gives an

overview on known approaches for WM in data cen-

ters. After that, we present our current development

stage. The implementation is done in two separate

places. On the one hand, we created an intelligent

management component containing required estima-

tion models and forecast algorithms. This component

https://docs.openstack.org/ironic/latest/

initiates the proactive and reactive node management

and is able to access our OpenStack Nova

exten-

sion which was implemented on the other hand. As

Nova is responsible for the actual node scheduling,

we introduce new ﬁlters for an improved server se-

lection based on energy efﬁciency and performance.

Accordingly, Section 3 summarizes all relevant data

sources and models used by the allocation algorithm.

The general structure of the intelligent management

including workload management, the allocation algo-

rithm and the scheduling process will be explained in

Section 4. Afterwards, Section 5 shows preliminary

simulation results before Section 6 presents the con-

clusion and an outlook to the near future.

2 STATE OF THE ART

In research, there are dozens of approaches and im-

plementations for server WM in data centers. Jen-

nings and Stadler did an exhaustive literature sur-

vey and present their results in (Jennings and Stadler,

2015). We will focus on related work with respect to

the most striking aspects of M2DC’s WM, being (1)

management of heterogeneous hardware nodes, (2)

proactive management by using workload forecasts

and server models and (3) setting up on OpenStack

as most widespread cloud management system.

https://docs.openstack.org/nova/latest/

Proactive Workload Management for Bare Metal Deployment on Microservers

247

Management of Heterogeneous Hardware. Jen-

nings and Stadler (Jennings and Stadler, 2015) present

an exhaustive list of WM approaches using dynamic

virtualization. Some of them have also been ap-

plied to industrial products. A good example is the

distributed resource scheduling (DRS) and its exten-

sion distributed power management (DPM) feature

by VMware for its virtualization technology, offer-

ing signiﬁcant energy savings by dynamic VM mi-

gration and minimization of physical machines (Gu-

lati et al., 2012). Applying such solutions requires

fully virtualized data centers, but virtualization tech-

nology is currently limited to x86/64 and ARM64

and it has some considerable overhead on small mi-

croserver nodes. Therefore, it is not the best solution

for M2DC with its heterogeneous hardware philos-

ophy, supporting a wide spectrum of different com-

pute nodes ranging from low power to high perfor-

mance. Still, there are some investigations using other

technologies for WM. Piraghaj et al. (Piraghaj et al.,

2015) as well as Kang et al. (Kang et al., 2017) pro-

pose WM based on Docker containers to additionally

reduce the virtualization overhead, which would also

be suitable for low-power microservers. De Assunc¸ao

et al. (de Assunc¸ao et al., 2016) implement dynamic

server provisioning using bare metal provisioning via

OpenStack Nova and an extension of its built-in ﬁl-

ter and weighing scheduler. However, these alterna-

tive approaches still do not include accelerators like

FPGAs or GPUs.

Proactive Workload Management. Some ap-

proaches consider workload forecasts in order to

achieve better consolidation rates with less buffer ca-

pacities. Zhang et al. (Zhang et al., 2014) propose an

algorithm which schedules work in a hybrid cloud be-

tween on- and off-premises compute capacities. On-

premises in terms of software or workload which

is processed on company owned or rented servers,

whereas off-premises means workload processing on

remote facilities like cloud computing or Software as

a Service (SaaS). The researcher divide workload in

’base’ load and ’ﬂash crowd’ load, which are man-

aged individually. Base load is managed proactively

on-premises (90 % of time within 17 % prediction er-

ror) and ﬂash crowd load is managed reactively off-

premises. Currently, their approach does not allow for

dynamic workload scheduling and it is restricted to

certain workload types, which impedes an automatic

usage on unknown applications. Herbst et al. (Herbst

et al., 2014) use time series analysis to forecast work-

loads and use this information for proactive resource

scheduling via virtualization. They present an ex-

haustive comparison of diverse statistical methods.

The most suitable time series model is selected at run

time based on the given context. By dynamically se-

lecting the model, the relative error could be reduced

by 37 % on average compared to statically selected

ﬁxed forecasting methods. A similar approach is uti-

lized in M2DC.

Cloud Management based Realization. The

adaptability in real, productive data centers is an

important criterion for the success of novel WM

methods. The best approach would be to set up

on broadly known and accepted server/cloud man-

agement tools to have a potential user base with

fewer contraints compared to proprietary approaches.

There is only little work in research, which already

use common management tools as a base for imple-

menting their own solutions. The most promising

one is from Beloglazov and Buyya (Beloglazov and

Buyya, 2015), who use OpenStack for their dynamic

consolidation module called Neat. It is designed

as a transparent add-on not requiring modiﬁcations

on OpenStack installations. Neat makes use of

OpenStack’s management functions via public APIs

to perform the VM migrations planned by the Neat

allocation algorithm. However, Neat is restricted

to virtualization and therefore not the optimal so-

lution for M2DC’s smaller microservers. Fujitsu

is also working on a management tool called FU-

JITSU Software ServerView Resource Orchestrator

(ROR) (Yanagawa, 2015) building up on OpenStack.

In contrast to Neat, autonomic functions for WM are

missing, as the focus rather is on increasing the ease

of operation to make management functions usable

for business purposes. Then again it is planned to

integrate OpenStack Ironic in the future to support

bare metal deployment next to virtualization.

M2DC’s energy-aware WM combines the advan-

tages of several referenced approaches which each

focus only on limited aspects. By applying a com-

bined proactive and reactive allocation algorithm, the

M2DC WM is able to optimize aggressively while

also providing emergency measures for sudden spikes

in workload. However, the most appealing advantage

of M2DC’s approach is the strict focus on future ap-

plicability. While the usage of OpenStack as base

platform should help spreading the solution due to its

broad community and popularity, the consideration of

alternative compute nodes in the modeling and man-

agement process guarantees a future relevance when

GPUs and FPGAs become more popular in general-

purpose computing.

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248

3 MODELS

The WM improves the overall application-to-node al-

locations. For the decision making it requires data

sources of directly measured operational parameters

as well as models, which make estimations based on

collected data. This section brieﬂy introduces re-

quired parameters, but does not go into details. It is

rather an overview for further WM explanations.

Data Sources. Data sources are separated in ﬁve

parts: The workload is the amount of work a com-

pute node has to tackle. Workload, in the form of

jobs, may either be placed once for direct processing

or may come over time based on current demand or

user requests. Performance is deﬁned by the work-

load processed per time for a given compute node.

That means performance is also application speciﬁc

and can therefore only be measured for adapted appli-

cations. Benchmarks help to measure hardware per-

formance for application workloads in order to com-

pare capabilities of compute nodes. This must be

done prior to productive operations. Utilization rep-

resents the usage of a compute node’s subsystems by

the applied workload and can be used as objectively

determinable proxy for workload data, when access

to application data is not available. The power will

be measured for each compute node, e.g. to deter-

mine the energy efﬁciency. In conjunction with re-

source utilization data, power models can be used to

estimate power demand for arbitrary node utilization.

The thermal and resource management aggregates the

thermal state of compute nodes in a thermal met-

ric. For this purpose it scores available temperature

sensors or temperature affecting components like fan

speed into a ratio, which can be used for later place-

ment decisions.

Models. Using the collected data sources, mod-

els are able to make calculations for deﬁned time

points, even in the future. The WM requires models

for power, performance, energy efﬁciency and work-

load/utilization. Power models are able to calculate

the power demand based on historic and future work-

load or utilization trends. Power data is required

for other models like the energy efﬁciency. Perfor-

mance models determine the performance of M2DC

components. In general, the objective is to use pub-

lished benchmark results in order to avoid measure-

ments on productive systems, cf. (Schlitt and Nebel,

2016). As the server benchmarks cannot be applied

to every hardware type of M2DC compute nodes, an

application-speciﬁc method will be required. There-

fore use case applications must be designed con-

sidering external monitoring of performance factors.

The energy efﬁciency model is deﬁned as relation be-

tween performance and the amount of required en-

ergy. Workload modeling is a main selling proposition

of M2DC’s WM. It utilizes workload or utilization

forecasts for the scheduling process, taking into ac-

count the user behavior to enable proactive actions for

regular peaks. For this, the WM uses a number of uni-

variate, statistical procedures to automatically model

unknown, generic time series solely based on historic

values. These are Seasonal and Trend decomposition

using Loess (STL), Holt-Winters (HW) ﬁltering and

Seasonal AutoRegressive Integrated moving Average

(SARIMA), which results are compared to choose the

best approach for the speciﬁc situation.

Storage. Measured values are stored in a central

database using Gnocchi

of the underlying Open-

Stack, while models required for the estimation of

values are placed within the intelligent management.

4 WORKLOAD MANAGEMENT

The WM component is embedded in the intelligent

management (IM) layer of M2DC’s middleware. The

IM layer is depicted in Figure 2. The central com-

ponent is the IM component, which acts as an in-

terface combining dataﬂows and workﬂows between

M2DC’s management components, the (model) data

and OpenStack.

The IM is the connection between the resource

and thermal management (RTM), the Power Man-

agement (PM) and the WM components. There are

two different PM solutions, one of them switching

host states by using the OpenStack API and the other

using directly the server controller API in case fur-

ther power management functionality is needed (e.g.

power capping) or if usage of OpenStack and WM is

not desired. Basically, WM and RTM could have dif-

ferent constraints, which have to be negotiated before

instructions are sent to the PM. Access to the server

models (power, performance, ...), thermal and work-

load models as well as measured data in the Gnocchi

database is realized through references to the com-

ponents, gathered in the IM class. Thereby, RTM

and WM can access each kind of model and use ev-

ery information available for management decisions.

Moreover, the IM represents the interface to Open-

Stack Nova regarding the dynamic scheduling. While

the allocation algorithm itself is part of the WM class,

connection to the Compute API as well as the ﬁlter

http://gnocchi.xyz/

Proactive Workload Management for Bare Metal Deployment on Microservers

249

Efficient Management

OpenStack

Models

Management

Support

Thermal and

Resource

Management

Server Model

Storage

Workload

Forecasting

Intelligent

Management

Power

Management

Gnocchi API

Compute API

M2DC

BaseHostFilter

Workload

Management

Thermal Filter

Perf Filter

EE Filter

Figure 2: Overview on intelligent management component.

classes are established via IM. The Compute API is

used for triggering application deployment and set-

ting host power states.

The WM’s main objective is to optimize the

application-to-node allocation in order to increase the

overall energy efﬁciency, i.e. the WM tries to se-

lect the most suitable compute nodes for the applica-

tions regarding current and future workload demands

– e.g. using low power ARM nodes if utilization is

low at night. Therefore, the current and future state

is analyzed and corresponding actions are triggered

by an allocation algorithm. Suitable actions are ex-

ecuted by the corresponding components (e.g. power

management for host power actions), which are ac-

cessed via IM. Moreover, the WM provides general

methods for creating, deploying, moving and destroy-

ing application instances, which are called, when the

user takes the corresponding actions in OpenStack.

Also, the WM has means to read the current state

of hosts/compute nodes and instances inside Open-

Stack, which are analyzed by the allocation algorithm.

These methods ensure that the allocation states be-

tween WM and OpenStack are consistent.

The allocation algorithm is the centerpiece of

WM. Generally, it distinguishes between two opera-

tional states: proactive and reactive allocation. The

operational state is constantly determined by compar-

ing current, actually measured utilization/workload

with forecast values. If the deviation is within a tol-

erance range (e.g. 20 %), allocation optimization is

done proactively based on forecasts. If the actual

utilization is below the threshold, the allocation will

still be optimized proactively except that the workload

modeling process for the affected compute nodes will

be repeated beforehand (maximum once per day). In

case the tolerance thresholds and the node utilization

thresholds (e.g. 80 %) are exceeded, allocation opti-

mization is interrupted and a high priority reactive

allocation will be performed. Afterwards, workload

models of concerning nodes will be reprocessed and

the proactive allocation optimization continues.

In proactive operation, the allocation algo-

rithm analyzes the current and future host utiliza-

tions/workloads and decides for each node (1) if ad-

ditional compute capacities have to be provided on

short-term, (2) the state may be optimized or (3) if it

should remain constant. As booting and deployment

activities take several minutes, a time window of at

least 30 minutes should be chosen for workload anal-

yses. Then again, the window must not be too long, as

that would reduce the optimization potential because

the allocation has to be valid (not exceeding utiliza-

tion thresholds) for the whole time window.

The algorithm starts to examine the provisioning

of more compute capacities. This is necessary, if the

expected utilization of a single node will exceed its

utilization threshold (e.g. 80 %) in the investigated

time window. This will initiate the deployment of the

affected application on another node, as every node is

expected to maintain a certain predetermined buffer.

The scheduling of a new node will be triggered and

executed in time to ensure the necessary capacities are

available as soon as they are needed. Subsequently,

the original node will be powered down, if the new

node has enough computing capacities. After provi-

sioning additional capacities, the utilization trend is

analyzed for the new allocation and the process is re-

peated, if necessary.

When all cases for capacity provisioning have

been resolved, the algorithm looks into potential en-

ergy efﬁciency optimization, which is often achieved

by utilization decreasing over time (e.g. after-work

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250

hours, night). The algorithm starts with the currently

least energy efﬁcient node (determined by energy ef-

ﬁciency model) and looks (1) for potential consolida-

tion of the running application instance or (2) for po-

tential alternative nodes capable of running the same

application more efﬁciently. Instance consolidation is

possible, if all other nodes running the same appli-

cation are able to provide enough capacities to take

over the workload of the instance which will be shut

down. Thus, an instance and node shutdown will be

triggered and called in OpenStack Nova via IM and

the Compute API. If this approach is not feasible, the

algorithm checks the availability of a more energy ef-

ﬁcient node for given utilization. If energy would

be saved in the investigated interval despite deploy-

ment overhead and buffer utilization is still met, the

scheduling as well as a subsequent shutdown is trig-

gered for the selected node/application. Anyway, the

next node is examined in the same way until every

node was examined. The proactive allocation process

will be restarted when half of the investigated time

window is over, thereby ensuring that future utiliza-

tion increases can be handled in time.

A reactive allocation is done for each compute

node, whose actual utilization exceeds its forecast by

the predetermined threshold. Thereby, the scheduling

of an additional compute node for the affected appli-

cation will be triggered before doing anything else, as

one can no longer rely on the utilization forecasts and

it is not known how the current trend will continue.

Moreover, the scheduling will be conﬁgured to pick

preferably high performance nodes in order to coun-

tervail a further increasing utilization. After all nec-

essary reactive operations have been performed and

workload modeling has been rerun, the allocation al-

gorithm tries to proactively optimize the current state.

All mentioned conﬁguration parameters (thresh-

olds, percentages, times) are preliminary and have to

be adapted based on empirical evaluations.

4.1 Scheduling via Nova

The actual scheduling of applications to compute

nodes is done via the OpenStack Nova FilterSched-

uler

, using several host ﬁlters and weighers. This

scheduler is extended by an M2DCBaseHostFilter,

which is derived from the Nova BaseHostFilter. It

has three child classes representing the considered as-

pects in WM and RTM. With regard to WM there is an

energy efﬁciency and a performance ﬁlter. The energy

efﬁciency ﬁlter passes through only nodes/hosts that

have at least the minimum speciﬁed energy efﬁciency

https://docs.openstack.org/nova/latest/user/ﬁlter-

scheduler.html

threshold, which was assigned by WM. The utiliza-

tion dependent node energy efﬁciency is computed

by using the models suggested in Section 3, which

are accessed via IM. The performance ﬁlter works

analogous. Regarding RTM, there is a thermal ﬁlter

passing through nodes/hosts based on a thermal met-

ric, which indicates if the corresponding node should

be considered for deployment or if the thermal en-

vironment is currently not suitable (e.g. thermal hot

spots). After the ﬁltering process, the most suitable

host is selected by weighed sorting of all passed com-

pute nodes by using adapted M2DCBaseHostWeigher.

The weights are again given by the WM and depend

generally on expected utilization/workload levels, e.g.

if the expected utilization is low, the more energy ef-

ﬁcient nodes are preferred and would beneﬁt from a

higher weighing.

As the allocation algorithm examines the opera-

tional state of compute nodes and instances, it trig-

gers scheduling processes for a given application with

state-speciﬁc parameters deﬁning energy efﬁciency,

performance and thermal requirements. In case there

is no suitable host for the triggered scheduling process

(i.e. no host passed all ﬁlters), the allocation algorithm

will get an appropriate response and it will ignore this

node as optimization target until the next power man-

agement operation was resolved. If the scheduling

was triggered due to reactive allocation, the admin-

istrators will be notiﬁed that there is no suitable host

to provide the needed capacity.

4.2 Microserver Support

M2DC’s main unique selling point is the support

of a high variety of different hardware architectures

in the microserver format. Within M2DC, Open-

Stack is extended to support dynamic composition

and provisioning of diverse microserver nodes includ-

ing FPGAs and GPUs. As WM is part of the base

M2DC installment, all available microserver types

have to be supported. This mainly inﬂuences whether

an application is deployable on a certain microserver

or not, depending on available implementations and

(boot) images.

The allocation algorithm itself does not consider

whether an application can be deployed on a certain

microserver. This aspect is checked in the destination

host selection of OpenStack Nova ﬁlter scheduler via

the ImagePropertiesFilter. This ﬁlter only passes

hosts satisfying the requirements given with the ap-

plication image to deploy. If multiple images (e.g. for

different architectures) are available, several requests

will be sent consecutively.

Proactive Workload Management for Bare Metal Deployment on Microservers

251

Table 1: First simulation results for static vs. dynamic (optimized) allocation.

Scenario Conﬁguration Energy (static) Energy (optimized) Savings

1 2 x86 (2013) + 2 ARM64 (2015/16) 3372 Wh 2082 Wh 38.2%

2 4 x86 (2013) 3234 Wh 2382 Wh 26.2%

3 2 x86 (2017) 1710 Wh 1710 Wh 0.0%

4 4 ARM64 (2016) 1860 Wh 1440 Wh 22.5%

4.3 Power Management

The power management is an essential part of

M2DC’s IM in order to control the overall energy

demand of the system. Realized as support compo-

nent, it encapsulates access to different power actions,

which comprise changes to power states but also

functions like power capping or frequency scaling.

M2DC’s power management components use two dif-

ferent approaches. On the one hand, OpenStack needs

to control nodes for its scheduling process, where e.g.

hosts need to be turned on prior to deploying appli-

cation instances. For the M2DC server, extensions

will take care to implement host-actions function-

ality of Nova, respectively node-set-power-states of

Ironic, using the provided M2DC server API. On the

other hand, it is necessary to execute basic PM func-

tions without using the OpenStack platform. There-

fore, there is an additional power management imple-

mentation that accesses the M2DC hardware directly.

These functions include fan management, power cap-

ping or frequency scaling demanded by RTM, which

are accessed via M2DC server API or directly on the

microserver.

5 PRELIMINARY RESULTS

So far, the IM component, the WM algorithms and the

OpenStack Nova extensions have been implemented

in a virtual environment based on DevStack. To test

the functional capability as well as the effectiveness

of the algorithms, we expanded the DevStack envi-

ronment with a simulation. As simulation data we

modeled several server systems (x86 servers from

2013 and 2017) and microservers (ARM64 from 2015

and 2016) with regard to performance, power and en-

ergy efﬁciency – each dependent on a given utiliza-

tion. The models base to some extent on own mea-

surements but for the most part on publicly available

benchmark results such as SPECpower ssj2008

and

are deﬁned by polynomial functions with utilization

as variable. As input data we used utilization forecasts

for two real application workloads measured in a pro-

ductive data center. In our simulation, both applica-

https://www.spec.org/power ssj2008/

tions may be run in several instances distributed over

different compute nodes, but not more than one appli-

cation instance per node (bare metal approach). We

simulated several small node conﬁgurations each with

a static allocation of application instances to compute

nodes as well as a dynamic workload management.

The simulation was accelerated by factor 60 so that

the run-time of 10 minutes represented 600 minutes

in reality. The results for running each conﬁguration

can be found in Table 1.

In scenario 3, there was no optimization feasible

because the two applications have to run on different

server nodes due to the bare metal approach (opposed

to virtualization, which can potentially operate both

applications on a single node). However, scenarios 1,

2 and 4 show signiﬁcant savings even in small conﬁg-

urations with few optimization possibilities. It is in-

teresting that the mixed conﬁguration in scenario 1 is

more efﬁcient than the homogeneous conﬁguration in

scenario 2, if workload management is applied, while

it needs more energy than scenario 2 in a static opera-

tion. This is an indicator for efﬁciency gain potential

by using heterogeneous computing nodes. Another

interesting result is the comparison of scenarios 3 and

4. While the modern x86 systems are better in a static

operation, the ARM64 conﬁguration provides a more

ﬁne-granular optimization potential, although this is

somewhat impaired by the small scenario conﬁgura-

tions.

We also simulated the productive server environ-

ment of one of M2DC’s partners as of the year 2013

(which is the base year for comparisons in M2DC

project) consisting of 40 x86 server systems. There-

fore, we modeled the server systems from 2013 re-

garding performance, power and energy efﬁciency.

We also measured the workload of our target appli-

cation in 2016 and used the corresponding forecast

model as input for the simulation. The result was

that the application of WM could reduce the energy

demand by about 800 kWh per month (> 40%) by

simply taking advantage of the high daily variance in

workload proﬁles.

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252

6 CONCLUSIONS

The M2DC project consortium is working on bring-

ing alternative server technologies like ARM based

processors, GPUs and FPGAs into commercial data

centers. The aim is to decrease capital and opera-

tional expenses by using microservers, which are both

cost- and energy-efﬁcient as they can be speciﬁcally

selected and conﬁgured for given tasks. As partner in

M2DC, we are speciﬁcally working on WM mecha-

nisms which make use of workload/utilization, per-

formance and power data to place applications on

the most suitable microservers and temporarily shut

down unused capacities. A ﬁrst simulation of the al-

gorithms show promising results of about 40% en-

ergy savings. The related work section shows that

there is already much work in this ﬁeld. However,

the M2DC approach is the only one (to best of our

knowledge) which combines the advantages of using

workload/utilization forecasts, setting up on a com-

monly known platform (OpenStack) and supporting

alternative (modern) server architectures.

The next steps on the M2DC WM include the

automatic modeling of server power, performance

and energy efﬁciency models as well as automatic

model selection and modeling of application work-

load/utilization forecasts. After ﬁne tuning and fur-

ther simulations in the virtual environment the IM in-

cluding its interface extensions to OpenStack will be

integrated in the M2DC testbed containing the newly

developed microserver nodes. Evaluations on this

testbed with use cases deﬁned in M2DC will then be

published in a future research paper.

ACKNOWLEDGEMENTS

This scientiﬁc work has received funding from the

European Union’s Horizon 2020 research and inno-

vation programme under grant agreement No. 688201

(M2DC).

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