Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor

Veronika Dashuber

1 a

and Michael Philippsen

2 b

QAware GmbH, Aschauer Str. 32, Munich, Germany

Programming Systems Group, Friedrich-Alexander University Erlangen-N

urnberg (FAU), Martensstr. 3, Germany

Keywords:

Cloud Infrastructure, Cloud Costs, City Metaphor, Infrastructure Visualization, Cost Visualization.

Abstract:

Many companies are in the process of migrating their entire IT infrastructure into the cloud in order to beneﬁt

from its high elasticity and scalability. While cloud providers offer basic cost visualizations such as line, bar

or pie charts, companies lose track of which part of their infrastructure causes which costs. We adapt the city

metaphor to visualize both the architecture and the costs. We offer a ﬂexible framework to structure and tailor

the visualization as desired. Using the Goal-Question-Metric approach, we identify cost savings potential and

demonstrate for an example cloud infrastructure that the deﬁned metrics are easier to grasp in our visualization

compared to a standard cost dashboard.

1 INTRODUCTION

In the cloud, operational servers, databases, and mid-

dleware components are just a mouse click away.

Compared to conventional IT infrastructure, cloud

infrastructure offers better scalability and elasticity

than conventional IT infrastructure and greatly re-

duces ﬁxed costs and the need to plan ahead. The

commercial success of cloud solutions is testament to

their economic potential. However, the ﬂexibility of

the cloud infrastructure also increases the risk of los-

ing track: which instances are actually running in the

cluster? Which of them are still needed? Most com-

panies only check at the end of the month whether

the accumulated costs are more or less reasonable,

but rarely overlook their infrastructure and the costs

in detail.

Cloud providers offer cost dashboards that show a

summary of costs and, e.g., cost increases compared

to the previous month. These dashboards use sim-

ple two-dimensional graphs such as line, bar and pie

charts to visualize costs. They also provide simple

grouping and ﬁltering options. But all of them only

visualize the costs without connecting them to the in-

frastructure architecture.

This paper presents Cloud Cost City (CCC), a

ﬂexible framework to import, structure, and visual-

ize a cloud infrastructure and its costs using the city

https://orcid.org/0000-0001-8577-5646

https://orcid.org/0000-0002-3202-2904

metaphor. CCC allows to understand both the costs

and the complete cloud landscape. We argue that the

city metaphor that is already used for visualizing large

software systems (Fittkau et al., 2013; Steinbr

uckner

and Lewerentz, 2010; Wettel and Lanza, 2007) can be

adapted to also visualize large cloud landscapes in an

understandable way, i.e., to visualize both costs and

infrastructure. To make this helpful for cloud cost re-

duction approaches known from literature, we use the

Goal Question Metric method to derive metrics that

are relevant for identifying savings potential. Com-

pared to the cloud providers’ cost dashboards, the fea-

tures of our visualization are more useful. To our

knowledge we are the ﬁrst to provide a 3D visualiza-

tion of both the cloud infrastructure and its costs.

2 RELATED WORK

2.1 Cost Dashboards

Most cloud providers offer similar cost dashboards.

The Cost Summary of Google Cloud Platform

(GCP) (Google Inc., 2020) displays costs and trends

with tables, (stacked) line, and bar charts. They pro-

vide basic slice-and-dice operations to group and to

drill down on the data w.r.t. team, service type, and

environment. The x-axis of all cost charts is the time

range, with selectable aggregation (month or week)

and cannot be conﬁgured. Costs can be ﬁltered both

Dashuber, V. and Philippsen, M.

Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor.

DOI: 10.5220/0010254701730180

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP, pages

173-180

ISBN: 978-989-758-488-6

173

with predeﬁned ﬁlters (such as the last 7 days) and

with custom ﬁlters. It is possible to select one group-

ing dimension and an arbitrary number of ﬁlters.

Microsoft’s Azure Cost Analysis (Anderson,

2020) uses the same straightforward lines and bars to

visualize costs. In addition, there are pie charts for an

accumulated view over a selected time range. While

ﬁltering is not restricted, grouping is again only pos-

sible for one dimension.

The Cost Explorer of Amazon Web Service

(AWS) (Amazon Web Services Inc., 2020) shows

similar visualization and exploration capabilities.

All these cost dashboards are mere 2D diagrams

and do not offer any possibility to visualize the in-

frastructure architecture together with the costs. Our

3D visualization allows to explore and analyze both.

Since the dashboards are so similar Sec. 5 only

uses GCP for a detailed comparison to our prototype

on an example cost analyzing project.

2.2 Cloud Architecture Visualizations

There are several commercially available tools to vi-

sualize the cloud infrastructure. Compared to them,

our prototype is not yet as closely integrated into

the commercial cloud platforms. None of the tools

we know of can display costs. They also lack op-

tions to customize the visualization while we pro-

vide ﬂexible ﬁltering and grouping options. While

some tools provide a 2D visualization(Cloudviz So-

lutions SIA, 2020; Lucid Software Inc., 2020), iso-

metric 3D representations are also common(Arcentry

Inc., 2020; Cloudcraft Inc., 2020; UMAknow Solu-

tions Inc., 2020) and closer related to our work.

2.3 Colored TreeMaps and Map-like

Visualizations

To our knowledge there is no work in the broad ﬁeld

of TreeMaps (Schulz et al., 2010) and map-like visu-

alizations (Hogr

afer et al., 2020) that focuses explic-

itly on the visualization of cloud infrastructure and

its costs. Nevertheless, colored TreeMaps might be

useful to visualize cloud infrastructure. E.g., each

cloud resource can be represented by a rectangle and

grouped by a deﬁned hierarchy. The area, color and -

in case of 3D TreeMaps - also the height of the rectan-

gles can visualize additional information such as costs

or usage.

Other visualizations that use the city metaphor

(e.g., source code visualizations (Wettel and Lanza,

2007)) are referred to as 3D TreeMap visualizations

(Schulz et al., 2010). As we also use the city metaphor

in the Cloud Cost City, our approach belongs in this

category as well.

2.4 Reducing Cloud Costs

There is a body of literature on the reduction of

cloud costs. (Beloglazov et al., 2012; Beloglazov

and Buyya, 2010) and (Berl et al., 2010) achieve an

energy-aware resource allocation, (Li et al., 2012;

Shastri and Irwin, 2018) and (Zohar et al., 2011) re-

duce cloud costs by means of resource prediction.

(Fang et al., 2013) and (Vieira et al., 2014) optimize

the utilization of resource units, i.e., the cost-beneﬁt

ratio.

These cost reduction techniques are orthogonal to

our work as they can be applied after our visualization

has been used to identify the main savings potentials.

3 IDENTIFY SAVINGS

POTENTIAL

For the primary goal to identify savings potential in

the cloud infrastructure costs from the point of view of

operations, we examine relevant questions and nec-

essary metrics (according to an adapted Goal Ques-

tion Metric (GQM) approach (Caldiera and Rombach,

1994)). Note that the literature on cloud cost reduc-

tion uses similar questions and metrics.

Sec. 4 later shows how we present the metrics in a

Cloud Cost City. Sec. 5 shows that it is much more

trouble to ﬁnd the answers with a cloud provider’s

typical cost dashboard.

3.1 Which Resource Units Cost the

Most?

The most expensive resources are apparently also

those with the most potential for savings. Since in

general, several projects are provisioned in one cloud

infrastructure, it is not enough to know which re-

source unit is most expensive, rather it must also be

determined to which project it belongs. Literature on

cloud cost analysis and reduction also suggest to iden-

tify the project that is using the infrastructure (Kondo

et al., 2009; Nanath and Pillai, 2013).

To answer this question, the metrics M1: total cost

of each resource unit by project is required. This met-

rics needs a two-fold grouping, ﬁrst by project and

then by resource unit.

While it is reasonable to sort the resource units

w.r.t. their costs, sorting the projects w.r.t. the total

cost of all their resource units may or may not be

IVAPP 2021 - 12th International Conference on Information Visualization Theory and Applications

174

reasonable. A large productive project obviously of-

ten has higher costs than a small development project

with more savings potential. Hence, each of the

grouping levels may beneﬁt from a different sorting.

3.2 Which Resource Units Are

Provisioned in Expensive Regions?

Depending on the region a resource is provided in,

the prices differ. At the end of 2020, a speciﬁc virtual

CPU located in Europe costs $20.81 per month, while

the same resource unit located in the USA is cheaper

($16.15). Other research also highlights cost differ-

ences between regions (Martens et al., 2012; Shastri

and Irwin, 2018). To optimize costs based on the re-

gion, we must therefore measure which project uses

resources in which region. Since the regional prices

also vary depending on the type of resource, e.g., vir-

tual machines are more expensive than SSD storage,

a breakdown by resource type can also help identify

the highest savings potential.

This results in the necessity of the metrics M2:

total costs of each resource unit per project per/within

a region, that needs a multi grouping on region (ﬁrst),

project, and resource type (last).

For the region level of the grouping hierarchy it

again is obvious that for ﬁnding cost savings poten-

tial, it is more relevant to sort according to where re-

sources are cheap than to where the IT infrastructure

actually spends the money.

3.3 Which Projects Use Too Expensive

Resources for Their Use Case?

For each resource unit providers usually offer differ-

ent ﬂavors that vary w.r.t. their speciﬁcations, e.g, re-

garding speed or storage capacity. The premium ver-

sions are more expensive than the basics. When a

project shows a low usage of an expensive resource

this often indicates a savings potential.

This leads to the metrics M3: ratio of cost and

usage of each resource unit per project. Other re-

search on cloud cost reduction also uses the utiliza-

tion of cloud components (Fang et al., 2013; Li et al.,

2012; Vieira et al., 2014; Zohar et al., 2011).

Again, instead of sorting w.r.t. where the money is

spent, it may be better to sort according to the price

tags (premium vs. basic) of resource units, i.e., the

savings potentials.

4 CLOUD COST CITY

Let us describe how we apply the city metaphor,

known from visualizing software systems, and turn it

into a Cloud Cost City that eases answering the above

questions on cost saving potentials.

4.1 3D Visualization

The result of the GQM approach leads to the follow-

ing design requirements in order to provide all metrics

in one visualization: multi grouping, display of costs

and display of usage. The cloud providers’ cost dash-

boards use 2D stacked bar charts, that are not sufﬁ-

cient to meet the design requirements. Even if we as-

sume that the x-axis of these charts can be arbitrarily

chosen, it is impossible with the three degrees of free-

dom (x-, y-axis and color) to group by ≥ 2 properties

as well as to show cost and usage at once. Therefore,

in contrast to our approach, several views are neces-

sary to obtain the metrics.

As we already explained in Sec. 2.3, the Cloud

Cost City can be seen as a 3D TreeMap visualization.

We decided to use a 3D representation to (a) coun-

teract the problem that the ﬂat layout makes it more

difﬁcult to truly understand the hierarchical structure

(Bladh et al., 2004; Long et al., 2017) and (b) support

the understanding by using a well-researched visual-

ization metaphor (Averbukh et al., 2007; Duit, 1991).

In our example analysis, we could not observe the

known weaknesses of 3D visualizations, namely oc-

clusion and the difﬁculty of performing actions (Tey-

seyre and Campo, 2008), probably because our proto-

type relies on familiar navigation principles from PC

games.

4.2 City Metaphor

The familiar city metaphor is widely used to visualize

and to help in grasping a complex software systems.

Traditionally it is used for code written in object-

orientated languages and represents classes as build-

ings. As classes belong to (sub-)packages, buildings

are placed into districts that represent the correspond-

ing package. Districts can be nested just like pack-

ages. Metrics are incorporated into the visualization

by mapping them to the heights, widths, and depths

of the buildings to provide further information about

them. For instance, the metrics lines of code can be

mapped to the height of a building.

To apply the well-researched city metaphor to vi-

sualize cloud costs instead of software, we use a dif-

ferent mapping to city artefacts. Buildings no longer

represent classes, but resource units (or aggregated

Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor

175

bundles thereof). Instead of source code related met-

rics, we map cost metrics to building properties like

height, width and depth, see Sec. 4.3. Districts no

longer reﬂect (sub-)package relations, but visualize

hierarchically structured aspects of the resource units,

e.g., the region of the world in which a resource unit

is actually running, see Sec. 4.4. A third major dif-

ference to traditional Software Cities is the layouting

of city artefacts. While traditionally a TreeMap lay-

out is used, Sec. 4.5 shows how we give the possibil-

ity to also use a sorted grid layout to order buildings

and/or districts so that the best cost savings potential

is placed upfront.

4.3 City Properties

As has been discussed in Sec. 3, to identify potential

for cloud cost savings, one needs to know both the

costs per resource unit (metrics M1 and M2) and the

cost-usage ratio per unit (M3).

Hence, the Cloud Cost City maps the cost of a re-

source unit to the height of its building. The tallest

buildings are easy to spot and point out the most

costly resource units. If several resource units are

grouped/bundled together in a building, its height re-

ﬂects the aggregated costs.

The Cloud Cost City does not directly map the

cost-usage ratio to a property of a building. Instead,

it maps the usage of a resource unit (bundle) to the

area of its building. This allows users to easily spot

anomalies/bad cost-usage ratios, because they show

up as tall buildings with a tiny area.

Fig. 1 shows the Cloud Cost City visualization of

a real cloud infrastructure. It is easy to spot the tall

buildings of costly resource units (metrics M1 and

M2). The blue cubic building in Fig.1(a) has a more

reasonable cost-usage ratio (M3) than the thin needle-

style building in Fig. 1(b).

(a) Blue resource unit with a

good cost-usage-ratio.

(b) Blue resource unit with a

bad cost-usage-ratio.

Figure 1: Cloud Cost City visualization of an example IT

infrastructure. Cost of resource units grouped by region and

within a region by project.

Figure 2: Cloud Cost City visualization of the example

from Fig. 1 with project costs aggregated across all resource

types.

4.4 Custom Hierarchies and Aggregates

All the metrics from Sec. 3 needed a grouping either

by different features or a different number of features.

Both metrics M1 and M3 need a ﬁrst-level grouping

by project, i.e., for each resource type (second level)

the costs are accumulated across all regions. Metrics

M2 uses a grouping according to region → pro ject →

resourceType. Some cloud infrastructures have other

additional custom features, e.g., resource units can

belong to different environments like production, in-

tegration, or development.

CCC uses nested districts to represent grouping.

For full ﬂexibility, in the Cloud Cost City the user can

pick an arbitrary ordered set of n such features, in-

cluding custom ones. The CCC maps the n selected

features to a hierarchy of districts. The costs of all re-

source units that have the same values in the n selected

features are bundled/aggregated in a single building,

regardless of their other features.

For grouping, Fig. 1 uses the feature hierarchy

region → pro ject → resourceType. All buildings in

the district on the right belong to the same region US.

A bit hard to see are the orange lines on the ground

that embrace projects. Fig. 2 uses a more shallow hi-

erarchy and a coarser granularity region → pro ject.

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The nested project districts of Fig. 1 vanish, buildings

no longer represent resource units, but aggregate costs

per project across all resource units.

4.5 Custom Sorted Layout

For each of the levels of a grouping hierarchy per de-

fault CCC orders the components according to their

total costs, i.e., buildings line up in a district ac-

cording to their heights; neighboring districts are or-

dered with respect to the sum of the cost of their sub-

districts or buildings. As we already have discussed

in Sec. 3, this sorting order may or may not help in

identifying the highest savings potential.

For instance, recall that prices vary between re-

gions. Sorting the buildings w.r.t. the price niveau

makes it easier to see that projects or resources should

be shifted to cheaper regions, than with the default or-

der that tells the user where the money is spent.

Consider Figs. 1 and 2 again. The former uses

the default ordering for the regions. The latter uses

the ordering w.r.t. the price niveau and makes it a lot

easier to see that it may be a good idea to shift the

costly resources that currently are running in Europe

to the US or even to Asia.

To open up this ﬂexibility to the user, the CCC al-

lows to specify a custom sorting order per dimension

of the hierarchy. The example uses EU  U S  AS

for the regions in metrics M2. For specifying an or-

der, users can also use wildcards instead of a concrete

feature values like AS.

5 EXAMPLE ANALYSIS

This section reports on a cost saving project for a real

cloud infrastructure that hosts 10 productive projects

that operate autonomously. The projects are of vary-

ing sizes and have different infrastructure require-

ments. In addition, the cloud platform is used for 4

test projects that are started up and shut down within

hours or days and therefore cause low costs. The total

fee in April 2020 was 5.473C.

To gauge the advantages of CCC, we performed

the cost analysis both with the dashboard of the

Google Cloud Platform (GCP), as a representative of

the state-of-the-art, and with our CCC visualization.

5.1 Which Resource Units Cost the

Most?

GCP. Out of the box, the dashboard shows costs per

resource type, but only aggregated over all projects.

Finding the costly resources for each project requires

Figure 3: GCP view for 2 projects: a stacked bar chart

shows the costs per resource type (different color per type).

Figure 4: Cloud Cost City viewed from 2 angles; custom

grouping pro ject → resourceType. Top: Look up to eas-

ily spot tallest towers. Bottom: Look down to identify the

projects to which the tall towers belong.

several manual steps because of two reasons: (a) the

x-dimensions of the dashboard’s line or bar charts al-

ways is the time, either on a daily or monthly basis,

and (b) there are no multi grouping options in GCP.

The manual steps are:

1. Group the costs by resource type.

2. For each of the 10 projects do:

(a) Filter/select the cost data of that project.

(b) Identify the most expensive resource units (the

largest areas in the stacked bar chart) and note

them and their costs.

Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor

177

3. Determine the most expensive resource units

among all projects.

This process is tedious and time consuming. Neither

are the resulting 10 charts (examples in Fig. 3) dis-

played side-by-side in the cost dashboard, nor do they

use the same scaling of the cost-axis. There is no sup-

port to ﬁnd the most expensive resource types across

the projects. Hence, due to the multiple manual steps

and the necessity to note and compare the costs across

projects, the metrics needed to answer this question is

not easy to obtain.

CCC. The only thing that needs to be done

is to specify the custom hierarchy pro ject →

resourceType. Then CCC automatically maps the

costs of the resource units to the heights of the build-

ings and groups them in a district per project. There

is no need for a custom sorting. Fig. 4 shows the vi-

sualization from two angles. When the user looks up,

it is easy to spot the tallest towers, see Fig. 4(top).

A click on a buildings reveals its actual cost value.

Going down on a tall tower to the ﬂoor reveals the

district/project as shown in Fig. 4(bottom).

While GCP requires a ﬁltering step per project,

CCC shows all projects in one view and with a com-

mon scaling. Both the number and cost of resource

types across projects can be grasped at once and with

less manual work than in the GCP dashboard.

5.2 Which Resource Units Are

Provisioned in Expensive Regions?

GCP. To retrieve this metrics also several manual

steps are needed due to the same reasons (ﬁxed x-axis

and lack of multi grouping).

The manual steps are:

1. Set a ﬁlter to extract the data of the region with

the highest price niveau and identify the m(≤ 10)

projects running in this region.

2. For each of the m projects do:

(a) Filter/select the cost data to that project.

Figure 5: Left: Projects running in the region with the high-

est price niveau (Europe), different color per project. Mid-

dle and right: resource types of two projects from Europe

(same color for different resource types).

(b) Group the costs by resource type.

units (the largest area in the stacked bar chart).

3. Determine the most expensive resource units

among all projects.

Note that all steps must be repeated to also apply

the analysis to the second most expensive region.

In the example m = 3 projects ran in costly Eu-

rope, see Fig. 5(left). Only 2 of them have noticeable

costs that step 2 of the manual analysis has to look

at (manually one after the other). Again the up to

(a) Overview of all regions.

(b) Zoomed-In view into Europe.

Figure 6: Cloud Cost City visualization with custom hierar-

chy region → pro ject → resourceType and custom sorting

order EU  US  AS for the region level.

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Table 1: Accessibility of metrics in the visualizations.

Metrics GCP CCC

M1 (3) multiple steps required 3

M2 (3) multiple steps required 3

M3 7 3

m charts are neither displayed side-by-side nor with

the same scale. For project

, Fig. 5(middle) reveals

the costs of its resource Log Volume. For project

Fig. 5(right) shows the costs of its resource Stor-

age PD Capacity. The different scaling between the

project charts make it difﬁcult to visually compare ab-

solute costs and to ﬁnd global maxima.

CCC. Whereas with the GCP dashboard a user needs

several steps to retrieve the information, in CCC it is

available at a glance.

The following custom hierarchy does the work:

region → pro ject → resourceType. This leads to

project districts that are nested within region districts.

CCC maps the costs of resource types to buildings.

In addition, it is beneﬁcial to use the custom sort

EU  U S  AS for the ﬁrst level of the hierarchy.

Fig. 6(a) holds the result. The most expensive region

comes ﬁrst. In contrast to what GCP can do, cheaper

regions are also visible. Fig. 6(b) zooms into Europe.

The three European projects are clearly visible as dis-

tricts. It is obvious that there is one resource in each

of the projects l and k that causes costs. The heights

of the two buildings use a common scale.

5.3 Which Projects Use Too Expensive

Resources for Their Use Case?

Recall that there are different versions for each re-

source type that vary w.r.t. speed or storage capacity.

Premium variants are more expensive than the basics.

Metrics M3 helps identify bad cost-usage ratios, i.e.,

low usages of expensive variants.

GCP. Since the dashboard does not show the usage

of the resources at all, it is impossible to extract this

metrics from the visualization.

CCC. The usage of the resource units corresponds to

the area of the buildings in CCC. A building with an

extreme shape (needle or sheet) is easy to spot. It indi-

cates a potential misselection of the resource variant,

i.e., a bad cost-usage ratio. Some example anomalies

can be found in Figs. 4 and 6.

5.4 Summary

Table 1 summarizes the questions to be asked for

identifying saving potentials, the metrics needed to

Table 2: Comparison of features.

Feature GCP CCC

Single grouping 3 3

Multi grouping 7 3

Ordering 7 3

Filtering 3 7, use grouping

and navigation

instead

Zooming /

Navigation

7 3

Costs over time 3 7

answer them, and whether they can be found in GCP

and CCC. All in all, it is easier to derive the metrics

in a Cloud Cost City visualization than with the GCP

dashboard. The main reason are differences in the fea-

ture set given in Table 2.

CCC lacks two features of the providers’ dash-

boards. First, there is no way to ﬁlter/select parts of

the data. But Fig. 6 has shown that since users can

multi group and order their buildings and then zoom

into the area of the CCC that they need to select.

Second, CCC does not attempt to show the devel-

opment of cost over time. However, this did not hurt

at all in ﬁnding potential savings in the current costs.

It is in our future work to let the user work with the

mouse wheel to roll back and forth in time and watch

how the CCC changes over time.

6 CONCLUSION

The Cloud Cost City is a novel visualization that is

based on the city metaphor. We have demonstrated

the framework’s ﬂexibility by using a CCC to identify

savings potential in cloud infrastructure costs. The

key ideas are that users can provide custom hierar-

chies to group and aggregate resource costs w.r.t. their

projects, regions, types, environments, etc. They can

also use custom sorting orders per hierarchy level. By

mapping the cost of resources to the heights of their

buildings and the usage to their area, cost anoma-

lies become obvious. CCC pairs this with both cus-

tom viewing angles in 3D and navigation/zooming to

make answering of cost saving questions easier than

with traditional dashboards. Furthermore, the CCC

provides both, an overview of the infrastructure ar-

chitecture and its costs.

Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor

179

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