Setting Priorities
A Heuristic Approach for Cloud Data Center Selection
Ronny Hans, David Steffen, Ulrich Lampe, Bj
¨
orn Richerzhagen and Ralf Steinmetz
Multimedia Communications Lab (KOM), TU Darmstadt, Rundeturmstr. 10, 64283 Darmstadt, Germany
Keywords:
Cloud Computing, Data Center, Quality of Service, Multimedia, Service, Heuristic.
Abstract:
A rising number of multimedia applications with Quality of Service requirements is delivered via cloud com-
puting platforms. To reduce latencies between data centers and customers, providers need to enhance and
utilize their cloud infrastructure by providing resources closer to the consumer. For planning such infras-
tructures and efficiently assigning existing resources, capable algorithms to solve the underlying optimization
problem are required. With our priority-based heuristic approach, we are able to reduce the computation time
by up to 99.99% compared to an exact approach, while retaining a favorable solution quality.
1 INTRODUCTION
Over the past years, cloud computing has developed
into a new paradigm for Information Technology (IT)
service delivery. It enables customers to use resources
according to their demand, independently of location
and time. The amount of services which are provided
via cloud data centers grows rapidly. While in 2012,
the ratio of overall Internet traffic caused by commu-
nication with cloud data centers amounted to 46 %, it
has been predicted to reach a share of 69 % in 2017
(Cisco, 2013).
Beside the increasing quantity in demand, the
Quality of Service (QoS) requirements also grow.
Multimedia applications such as Desktop as a Ser-
vice or cloud gaming require low latencies, for ex-
ample. Such requirements pose new challenges re-
garding the service delivery for cloud infrastructure
providers. Even in industrial countries such as the
United States, with a well-developed cloud infrastruc-
ture, only a portion of users could be serviced with
sufficiently low latencies to enable services such as
cloud gaming (Choy et al., 2012).
Until a few years ago, cloud providers focused on
huge centralized data centers in only a few physical
locations. With the advent of QoS-aware multime-
dia services, data centers and compute resources that
are located closer to the user gain in importance. For
both, the appropriate planning of such extensive com-
pute infrastructures as well as the efficient resource
allocation in existing infrastructures, appropriate al-
gorithms are required.
The remainder of this paper is structured as fol-
lows: In Section 2, we explain the specific problem.
In Section 3, we briefly present our previously pub-
lished solution approaches including the mathemati-
cal model. In Section 4, we introduce our priority-
based heuristic approach, which is subsequently eval-
uated in Section 5. An overview of related work is
given in Section 6. Section 7 concludes the paper with
a brief summary and outlook on future work.
2 PROBLEM STATEMENT
In this work, we consider a cloud provider who aims
to provide the infrastructure for multimedia service
delivery. Therefore, a set of (potential or exist-
ing) data centers in different geographical locations
is assumed. Each data center may provide resource
units between a lower and a upper capacity bound.
The provider can choose between these data centers.
Thereby, for each data center certain fixed costs, e. g.,
for construction or leasing, accrue. In addition, each
provisioned resource unit results in variable costs. For
the provided resource, the provider defines a set of rel-
evant QoS attributes and states a QoS guarantee with
respect to each user cluster and the defined QoS at-
tribute.
The data centers should serve a set of geographi-
cally distributed user clusters. Thereby, a user cluster
represents a set of user with certain demand, which is
expressed in a standardized resource unit, i. e., num-
ber of servers. Regarding the delivered services, a
221
Hans R., Steffen D., Lampe U., Richerzhagen B. and Steinmetz R..
Setting Priorities - A Heuristic Approach for Cloud Data Center Selection.
DOI: 10.5220/0005494702210228
In Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER-2015), pages 221-228
ISBN: 978-989-758-104-5
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
user cluster has certain QoS requirements with respect
to each QoS attribute.
Under the assumption that prices are determined
by external market conditions, the problem of a
provider is the cost-minimal selection of appropriate
resources, as well as setting the respective resource
capacity. For the resource allocation to different user
clusters, the overall service demands of all user clus-
ters and the QoS requirements must be matched by
corresponding guarantees. In our former work, we re-
ferred to this problem as Cloud Data Center Selection
Problem (CDCSP) (Hans et al., 2013).
3 IP-/LP-BASED OPTIMIZATION
APPROACHES
In this section, we briefly describe the mathematical
model for the CDCSP and previously published solu-
tion approaches.
3.1 Mathematical Model
The presented mathematical model is part of our for-
mer work (Hans et al., 2013). For the model several
formal notations are required. To begin with, we de-
fine the basic entities:
D = {1, 2,...,D
#
}: Set of (potential or existing)
data centers
U = {1, 2, ...,U
#
}: Set of user clusters
Q = {1, 2, ..., Q
#
}: Set of considered QoS at-
tributes
Based on these basic entities, the associated parame-
ters can be defined as follows:
S
u
: Service demand of user cluster u
K
min
d
R: Minimal capacity of data center d
K
max
d
R: Maximal capacity of data center d
CF
d
R: Fixed costs of selecting data center d
CV
d
R: Variable costs for per server unit in data
center d
QG
d,u,q
R: QoS guarantee of data center d w.r.t.
user cluster u for QoS attribute q
QR
u,q
R: QoS requirement of user cluster u
w.r.t. QoS attribute q
Finally, in order to model the CDCSP as optimization
problem, we use the following decision variables:
x
d
: Selection of a data center d
y
d,u
: Number of resource units provided by data
center d to user cluster u
Model 1: Cloud Data Center Selection Problem.
Min. C(x,y) =
dD
x
d
×CF
d
+
dD,uU
y
d,u
×CV
d
(1)
dD
y
d,u
S
u
u U (2)
uU
y
d,u
x
d
× K
max
d
d D (3)
uU
y
d,u
x
d
× K
min
d
d D (4)
y
d,u
p
d,u
× K
max
d
d D, u U (5)
p
d,u
=
(
1 if QG
d,u,q
QR
u,q
q Q
0 else
(6)
x
d
{0,1} ∀d D
y
d,u
N d D, u U (7)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
x
d
R,0 x
d
1 d D
y
d,u
R,y
d,u
0 d D, u U (8)
The described objective of the CDCSP constitutes a
linear, mixed-integer program, which is formalized in
Model 1. In the model, Eq. 1 defines the objective of
the problem. Thereby, the total cost C depends on the
decision variables x
d
and y
d,u
(Eq. 7) The binary vari-
ables x
d
indicate if data center d will be constructed
or leased. y
d,u
are integer variables that denote the
number of resource units a data center d provides to a
user cluster u. Eq. 2 represents the constraint that the
service demand of each user cluster needs to be satis-
fied by the provided service units. Eqs. 3 and 4 assure
that the provided capacity of each data center lies be-
tween the given lower bound K
min
d
and the given upper
bound K
max
d
. Further, they functionally link the deci-
sion variables x and y. In Eq. 5 and Eq. 6 the variables
p
d,u
restrict the resource allocation between data cen-
ters and user clusters, depending on the fulfillment of
the QoS requirements. In Eq. 8 the binary and inte-
ger decision variables from Eq. 7 are substituted by
corresponding natural variables, which is required for
the LP-relaxed approach (cf. Section 3.2).
3.2 Solution Approaches
As stated earlier, the described model constitutes an
Integer Program (IP) and was published as Cloud
Data Center Selection Problem (Hans et al., 2013).
Such IPs can be solved using off-the-shelf algorithms,
such as branch-and-bound (Domschke and Drexl,
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222
2004). This results in an exact (i. e., optimal) so-
lution. However, since branch-and-bound is based
on the principle of enumeration (Hillier and Lieber-
man, 2005), the computation time grows exponen-
tially with the number of decision variables in the
worst case. To overcome this drawback, we intro-
duced an initial heuristic approach based on the com-
mon concept of LP relaxation (Hans, 2013). Al-
though this approach significantly reduces the com-
putation time, is still needs minutes for large problem
instances, which makes it inapplicable for on-demand
resource assignments.
4 PRIORITY-BASED HEURISTIC
APPROACH
The described CDCSP forms an extension of a ca-
pacitive facility location problem. Such problems can
be solved by using priority based approaches (An-
gelopoulos and Borodin, 2002). To efficiently find
solutions for the CDCSP, we developed a priority-
based heuristic approach, where the user demand is
assigned to potential data centers in a stepwise man-
ner, following specific rules regarding user cluster
and data center selection. Since the approach cal-
culates an initial solution of the optimization prob-
lem, we named it Priority-based Start Heuristic, in
short CDCSP-PBSH. Our approach consists of sev-
eral phases, which are described in the subsequent
sections. Later on, we present a set of prioritization
and cost allocation rules in detail. Since we use a
generic approach, new rules can be easily added. Fi-
nally, we describe the conduction of concrete heuris-
tic approaches.
4.1 Generic Optimization Approach
Our approach is divided into five phases, as illustrated
in Figure 1. In the Selection Phase, the used data cen-
ters are determined. In the Allocation Phase, the fi-
nal resource assignment is done. The purposes of the
other phases, namely the Initialization Phase, the Up-
date Phase, and the Finalization Phase, are primarily
the preparation of the required data structures and the
processing of interim as well as the final results.
4.1.1 Initialization Phase
At the beginning of our procedure, a specific problem
instance of the CDCSP is analyzed and the required
data structure for the subsequent phases is created.
Algorithm 2 shows the corresponding pseudo code.
First of all, user clusters U are added to the list for
the residual user clusters U
res
. For each user clus-
ter appropriate data centers are determined, which are
able to provide QoS guarantees QG
d,u,q
according to
the QoS requirements QR
u,q
of the user cluster for all
QoS parameters q Q (cf. line 7 - 11). The result is
stored in a binary variable p
d,u
= {0,1} (cf. line 9),
which corresponds to the constraint in Eq. 6 in our
model. The permitted data centers for each user clus-
ter are stored in the list D
per
u
(cf. line 12). Further,
variables for the residual demand of the user clusters
S
res
u
and for the residual capacities of the data centers
K
res
d
are set (cf. line 3 and 16).
Algorithm 2: Initialization.
Start:
1: U
res
U
2: for all u U do
3: S
res
u
S
u
4: D
per
u
/
0
5: for all d D do
6: p
d,u
true
7: for all q Q do
8: if QR
u,q
< QG
d,u,q
then
9: p
d,u
false
10: end if
11: end for
12: if p
d,u
is true then D
per
u
D
per
u
{d} end if
13: end for
14: end for
15: for all d D do
16: K
res
d
K
max
d
17: end for
4.1.2 Selection Phase
In this phase a first feasible solution for the CDCSP is
determined in a stepwise manner. Algorithm 3 shows
the corresponding pseudo code. At the beginning of
each selection step, a user cluster u U
res
with a
residual service demand S
res
u
> 0 as well as a data cen-
ter d D
per
u
with a residual capacity K
res
d
> 0 are se-
lected (cf. line 3 and 4). The selection of a user cluster
depends on the priority rule (cf. Section 4.2), which
is set at the beginning of this phase. From the set of
possible data centers, the one with the lowest cost per
service unit depending on the cost allocation rule
(cf. Section 4.3) is selected (cf. Section 4.3). The
assignment of capacities y
d,u
depends on the resid-
ual demand of the selected user cluster S
res
u
and the
residual capacity K
res
d
of the selected data center (cf.
line 5).
Within this phase, a made assignment decision is
final and will not be changed in later iterations. Ac-
cording to the assigned capacities, the residual de-
SettingPriorities-AHeuristicApproachforCloudDataCenterSelection
223
mand and the residual capacity are reduced (cf. line 6
and 7). All selected data centers are stored in the list
D
open
(cf. line 8). If the demand of a user cluster is
met or the capacity of a data center is exhausted, it
will not be taken into account in the subsequent itera-
tions (cf. line 9 and 12).
Algorithm 3: Determination of an Initial Solution.
Start: D
open
/
0
1: while |U
res
| > 0 do
2: if |D
per
u
| = 0 then exit without solution end if
3: u SelectUserCluster(U
res
)
4: d SelectDataCenter(D
per
u
)
5: y
d,u
min(K
res
d
,S
res
u
)
6: K
res
d
K
res
d
y
d,u
7: S
res
u
S
res
u
y
d,u
8: D
open
D
open
{d}
9: if S
res
u
= 0 then U
res
U
res
\{u} end if
10: if K
res
d
= 0 then
11: for all u
0
U
res
do
12: D
per
u
0
D
per
u
0
\{d}
13: end for
14: end if
15: end while
4.1.3 Update Phase
In the previous phase, a set of data centers was opened
and stored in the list D
open
. This list serves as an im-
proved information base and is used instead of the ini-
tial list D, which included all possible data centers. In
the Update Phase, all assignments are reset and the
required data structure is recreated, whereby the pro-
cedure corresponds to the Initialization Phase.
4.1.4 Allocation Phase
The Allocation Phase is comparable to the the previ-
ously described Selection Phase. Again, the solution
is determined in a stepwise manner based on the prior-
itization and cost allocation rules. Since all data cen-
ters were determined in the Selection Phase, at least
the fixed costs arise. Thus, the allocation of resources
can be improved by focusing on different goals, as
implemented by different priority and cost allocation
rules.
4.1.5 Finalization Phase
During the Selection Phase, the relevant data centers
were stored in the list D
open
. Based on its content, val-
ues need to be assigned to the decision variables x
d
.
Thereby, x
d
assumes the value one for all data cen-
ters in D
open
. The amount of assigned service units
is stored in y
d,u
. It assumes the value zero if no ser-
vice units were assigned between a data center and the
corresponding user clusters (cf. Algorithm 4).
Algorithm 4: Finalization of the Approach.
Start:
1: for all d D do
2: if d D
open
then
3: x
d
1
4: else
5: x
d
0
6: end if
7: for all u U do
8: if y
u,d
= null then y
u,d
0 end if
9: end for
10: end for
4.2 Priority Rules
A major challenge of priority based procedures is
the determination of the sequence in which the de-
manders are assigned to the supply locations (B
¨
olte,
1994). Appropriate priority rules are required to sort
the demanders in a specific sequence. Beside the
demander, the selection of the supply locations can
also be supported by priority rules (Angelopoulos and
Borodin, 2002). For the CDCSP we focus on the fol-
lowing three quantity based prioritization rules which
sequences the used clusters w.r.t. the demand, the
available capacities, or both.
The Demand Priority Rule is used to order the
user clusters u U
res
according to their residual ser-
vice demand S
res
u
> 0. The basic idea behind this rule
is to prefer user clusters with a higher service demand
to ensure a valid solution. Since the assignment takes
place in every single step of the procedure, the priori-
tization of the residual user clusters may change.
In contrast to the previous rule, the Capacity Pri-
ority Rule focuses on residual capacities K
res
d
of the
suitable data centers d D
per
u
. Thereby, user clusters
with a lower total service supply are preferred.
Both rules have a low complexity and those may
be able to find solutions with low computational ef-
fort. Nevertheless, they may lead to solutions with
a lower quality since they take only demand or sup-
ply into consideration. Thus, the third quantity based
prioritization rule, Buffer Priority Rule, combines
both preceding rules to overcome their disadvantages.
Thereby, a service buffer as the margin of residual
capacities and residual service demand of each user
cluster is calculated. User clusters with a lower ser-
vice buffer assume a higher priority.
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Initialization
Phase
Selection
Phase
Choose Resources
Accounting
Solving Selection Problem Solving Allocation Problem
Initialization
Finalization
Initial Feasible
Solution of CDCSP
Specific CDCSP
Problem Instance
Allocation
Phase
Update
Phase
Finalization
Phase
Update
Choose Resources
Accounting
Figure 1: Phases of the Heuristic Approach.
4.3 Cost Allocation Rules
The selection of the data center is based on its costs,
which consist of variable and fixed costs. Since the
total amount of delivered resources is unknown until
the end of the whole assignment procedure, the spread
of fixed costs is a challenging task. The individual
costs of a service unit y
u,d
is given by the following
function.
C(u, d, base) = CV
d
+CF
d
(1/base) (9)
Thereby, the spread of the fixed cost depends on the
value of the artificial base parameter. With a larger
value of this parameter, the share of fixed costs per
service unit decreases. Thus, the strategy for the de-
termination of the base parameter directly influences
the service assignment and is given by the cost alloca-
tion rules. In the subsequent section, we present two
main classes of cost allocation rules.
4.3.1 Static Cost Allocation Rules
Within these rules, the value of the base parameter is
determined once at the beginning of a heuristic ap-
proach and will not be changed any more. Within the
Max Capacity Cost Allocation Rule, the maximum ca-
pacity of a data center K
max
d
is considered. This rule
is based on the assumption that a data center is nearly
completely utilized. If this is not the case, the total
costs of a data center may be underestimated.
If a provider expects a utilization near the mini-
mum capacity of a data center K
min
d
, the Min Capacity
Cost Allocation Rule is more appropriate. Thereby,
the minimum capacity serves as the base parameter.
In case of a higher utilization, the costs per service
unit are overestimated.
To strike a balance, the Med Capacity Cost Allo-
cation Rule uses the medium value between the mini-
mum and the maximum capacity of a data center.
For a given set of already opened data centers, an-
other option is to neglect the fixed costs completely.
This could be appropriate if the fixed costs arise in
any case or if the number of provided service units is
very high. In this case, a sufficiently large value for
the base parameter is chosen. The corresponding rule
is named No Fixed Cost Allocation Rule.
4.3.2 Dynamic Cost Allocation Rules
In contrast to the static rules, the dynamic rules in-
clude the already existing assignments in the calcula-
tions. The value of the base parameter is calculated in
each iteration of our heuristic approach and considers
the current utilization of a data center.
The first of our dynamic rules, Penalize First Cost
Allocation Rule, penalizes a user cluster which tends
to open a new data center d 6⊂ D
open
. In such a case,
the full fixed costs are added to the cost function. If a
user cluster gets its services from an already opened
data center, only the variable costs are included into
the calculation. Thus, there is an incentive to use ex-
isting data centers, which is especially important dur-
ing the selection phase.
Another strategy is pursued by the Prefer Minimal
Utilization Cost Allocation Rule. This rule is based
on the assumption that the opened data centers need
to reach their minimum capacity constraint. Thus,
data centers with an utilization lower than the mini-
mum value get a higher priority. The cost function
of such data centers only includes the variable costs,
while cost function for data centers with an utiliza-
tion high than a minimum capacity includes addition-
ally the fixed costs. Especially a scenario with a given
set of data centers, like the Allocation Phase, benefits
from this rule.
The Current Utilization Cost Allocation Rule cal-
culates the fixed costs based on the current utilization
of a data center. Thereby, the allocation of service
units between a data center and an user cluster re-
sults from the minimum of the residual demand and
the residual capacity. In contrast to the previous two
dynamic rules, the value of the fixed costs which is
added to the cost function decreases with a higher uti-
lization.
SettingPriorities-AHeuristicApproachforCloudDataCenterSelection
225
Rules for
Selection Phase
Demand Priority Rule
Capacity Priority Rule
Buffer Priority Rule
Max Capacity Cost Allocation Rule
Min Capacity Cost Allocation Rule
Med Capacity Cost Allocation Rule
No Fixed Cost Allocation Rule
Penalize First Cost Allocation Rule
Current Utilization Cost Allocation Rule
Max Capacity Cost Allocation Rule
Min Capacity Cost Allocation Rule
Med Capacity Cost Allocation Rule
No Fixed Cost Allocation Rule
Penalize First Cost Allocation Rule
Current Utilization Cost Allocation Rule
Rules for
Allocation Phase
Specific Heuristic Procedute
Abstract Heuristic Procedure
Demand Priority Rule
Capacity Priority Rule
Buffer Priority Rule
Figure 2: Deduction of Specific Heuristic Approaches.
4.4 Deduction of Specific Heuristic
Approaches
In Section 4.1, we presented a heuristic approach as
an abstract solution approach for the CDCSP. For both
phases, the Selection Phase and the Allocation Phase,
we use priority rules for the user cluster selection
and cost allocation rules to choose the correspond-
ing data centers.For both phases, all described rules
are equally available. Nevertheless, some of them
are more suitable than others, e. g., the selection of
data centers with ignoring the fixed costs very likely
leads to poor solutions. Figure 2 gives an overview of
the previously described rules and shows the deduc-
tion of a specific heuristic approach, i. e., the CDCSP-
PBSH[1], which is described in detail within the eval-
uation (cf. Section 5.2).
5 EVALUATION
5.1 Setup
In order to assess the capability of our heuristic ap-
proach, we prototypically implemented it in Java 8.
As the solver for the exact and the LP-relaxed ap-
proach, we used IBM ILOG CPLEX 12.5
1
, which
was accessed through the JavaILP middleware
2
.
Our evaluation focused on dependent variables,
computation time and solution quality, i. e., total
costs. As independent variables, we considered the
number of data centers and the number of user clus-
ters. These variables directly influence the number of
decision variables, and hence, the size of the solution
space.
1
http://www.ibm.com/software/integration/
optimization/cplex-optimizer/
2
http://javailp.sourceforge.net/
According to our former work (Hans et al., 2013),
the problem instance generation was based on the
2010 United States census
3
. Thereby we set the ser-
vice demands and different cost parameters according
the population of a randomly selected county and its
median income. We focused on latency as our sole
QoS parameter and set it corresponding to the require-
ments of multimedia services. For each test case, we
created 100 problem instances.
Based on the samples, we subsequently computed
the observed mean absolute computation times and
the macro-averaged ratio of total cost along with the
respective 95% confidence intervals based on a t-
distribution (Kirk, 2007). The evaluation was con-
ducted on a workstation, equipped with a Intel Xeon
CPU E5-1620 v3 with 3.50 GHz and 16 GB of mem-
ory, operating under Microsoft Windows 7.
5.2 Results and Discussion
At the beginning we analyzed the performance and
the solution quality, i. e., the ratio cost. We used
the exact approach (CDCSP-EXA.KOM) and the LP-
relaxed approach (CDCSP-REL.KOM) of our for-
mer works and the proposed heuristic approaches
(CDCSP-PBSH.KOM) with all combinations of the
prioritization and cost allocation rules described in
this paper. Due to the large amount of evaluation re-
sults, we decided to present only two of them within
this paper. The first approach was selected due to its
superior solution quality for a large set of test cases,
whereas the second was chosen due to its favorable
computation time.
CDCSP-PBSH.KOM [1]: Selection: Buffer Prior-
ity Rule, Max Capacity Cost Allocation Rule; Al-
location: Buffer Priority Rule, Penalize First Cost
Allocation Rule
3
http://www.census.gov/geo/maps-data/data/
gazetteer.html
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0.001
0.01
0.1
1
10
100
1000
10000
10 / 150 20 / 300 30 / 450 40 / 600
Mean Computation Time [s]
Test Case (|D| / |U|)
CDCSP-EXA.KOM
CDCSP-REL.KOM
CDCSP-PBSH.KOM [1]
CDCSP-PBSH.KOM [2]
Figure 3: Computation Time (Small Test Cases).
CDCSP-PBSH.KOM [2]: Selection: Demand
Priority Rule, Max Capacity Cost Allocation
Rule; Allocation: Demand Priority Rule, Penalize
First Cost Allocation Rule
Figure 3 shows the computation time of the four ap-
proaches. The comparison between the exact and the
second heuristic approach (CDCSP-PBSH.KOM [2])
shows a statistical significant improvement of 98.75%
for the first test case (|D| = 10 / |U | = 150) and up
to 99.99% for the last test case (40 / 600). The so-
lution quality of the approaches is depicted in Fig-
ure 4. The chart shows the ratio of cost compared to
the exact approach. In the last test case (40 / 600),
the LP-relaxed approach causes 5.27% higher costs
compared to the exact approach and our first heuristic
approach (CDCSP-PBMH.KOM [1]) causes 5.68%
higher costs. The fastest approach delivered the poor-
est solution quality with a cost increase of 20.68%.
In a second step, we used test cases with a larger
amount of potential data centers and user clusters to
evaluate the algorithms in large scale environments.
Due to the high computational effort, the exact ap-
proach is not feasible in such scenarios. Again, we
include the previously described heuristic approaches
in this setup.
Especially the results for the heuristic approach
CDCSP-PBSH.KOM [1] are very interesting. For the
chosen number of data centers and user clusters, we
are able to reduce the computation time by about 99%
compared to the LP-relaxed approach (cf. Figure 5),
while retaining the same solution quality, i. e., a cost
ratio of one.
Further, for the heuristic approach CDCSP-
PBSH.KOM [2], with a less complex prioritization
rule, we achieve an even better computation time.
However, the solution quality is significantly worse
compared to the other approaches, with cost increases
ranging from 0.95% to 3.53%.
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
10 / 150 20 / 300 30 / 450 40 / 600
Ratio of Cost
Problem Size (|D| / |U|)
CDCSP-REL.KOM / CDCSP-EXA.KOM
CDCSP-PBSH.KOM [1] / CDCSP-EXA.KOM
CDCSP-PBSH.KOM [2] / CDCSP-EXA.KOM
Figure 4: Solution Quality (Small Test Cases).
0.01
0.1
1
10
100
1000
100 / 500 200 / 1000 300 / 1500 400 / 2000 500 / 2500
Mean Computation Time [s]
Test Case (|D| / |U|)
CDCSP-REL.KOM
CDCSP-PBSH.KOM [1]
CDCSP-PBSH.KOM [2]
Figure 5: Computation Time (Large Test Cases).
6 RELATED WORK
A lot of work focus on data center placement and re-
source allocation with different optimization goals,
such as the reduction of network latency or the re-
duction of total cost. Thereby different solution ap-
proaches like exact approaches or heuristic such as
Tabu Search so Simulated Annealing are used. In this
section, we present a set of selected papers, which are
most relevant regarding the work at hand.
(Chang et al., 2007) investigated in the consolida-
tion of the server infrastructure for the US army. The
authors formulated a optimization problem to mini-
mize the weighted distances between the data centers
and users. (Goiri et al., 2011) also analyze the place-
ment of data centers. Thereby, the objective is the
reduction of total costs under consideration of quality
requirements. The authors formulate an optimization
model and solved it with LP relaxation and a simu-
lated annealing heuristic. Both papers focus on data
center placement and do not provide algorithms for
run time resource allocation.
(Larumbe and Sans
`
o, 2012) formulated an op-
timization problem for cloud computing which in-
SettingPriorities-AHeuristicApproachforCloudDataCenterSelection
227
cludes: Location data centers, location of software
components, and routing. Therefore, the authors also
consider an exact optimization approach, which is pri-
mary appropriated for planing aspects. (Wang et al.,
2012) focus on mobile cloud gaming and propose an
approach for the minimization of the total costs of a
cloud provider taking the individual quality require-
ments of the users into account. The authors develop
a scheduling algorithm for assigning computation and
networking resources during run time. In contrast to
this work the authors do not formulate an optimiza-
tion problem.
(Choy et al., 2012) focuses in their work on the
availability on cloud gaming in the US. Therefore, the
authors analyze the cloud infrastructure provided by
Amazon and show that only 70 percent of the popula-
tion can use services. They propose the use of addi-
tional data centers or Edge Server to increase the cov-
erage. In contrast to our work, they does not propose
an optimization approach for the efficient placement
of such data centers and servers.
In summary, to the best of our knowledge, our
work is the first to include a detailed analysis of a
priority-based heuristic approach for cost-efficient se-
lection of cloud data centers for QoS-aware services
provisioning. In this context, this paper provides a
generic heuristic approach, which allows substantial
reduction of computation time compared to previ-
ously presented approaches.
7 SUMMARY AND OUTLOOK
In this paper, we presented a heuristic approach to
a previously introduced optimization problem, the
Cloud Data Center Selection Problem. From this
generic approach, a variety of specific heuristic ap-
proaches can be deduced. Depending on the selected
prioritization and cost allocation rules, either very fast
heuristics approaches or heuristics with an outstand-
ing solution quality can be configured.
Based on the presented approach, we plan two ma-
jor enhancements in the future. First, we plan to de-
velop a best-of-breed approach, which combines the
benefits of multiple heuristics. Second, we plan to de-
velop improvement procedures, such as tabu search or
simulated annealing, to further enhance the solution
quality of our approach.
ACKNOWLEDGEMENTS
This work has been sponsored in part by the German
Federal Ministry of Education and Research (BMBF)
under grant no. 01IS12054, by E-Finance Lab e.V.,
Frankfurt a.M., Germany (www.efinancelab.de), and
by the German Research Foundation (DFG) in the
Collaborative Research Center (SFB) 1053 MAKI.
The authors are fully responsible for the content of
this paper.
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