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A Hash-Based Naming Strategy for the Fog-to-Cloud Computing Paradigm


Abstract and Figures

The growth of the Internet connected devices population has fuelled the emergence of new distributed computer paradigms; one of these paradigms is the so-called Fog-to-Cloud (F2C) computing, where resources (compute, storage, data) are distributed in a hierarchical fashion between the edge and the core of the network. This new paradigm has brought new research challenges, such as the need for a novel framework intended to controlling and, more in general, facilitating the interaction among the heterogeneous devices conforming the environment at the edge of the network and the available resources at cloud. A key feature that this framework should meet is the capability of uniquely and unequivocally identify the connected devices. In this paper a hash-based naming strategy suitable to be used in the F2C environment is presented. The proposed naming method is based on three main components: certification, hashing and identification. This research is an ongoing work, thus, the steps to follow since a device connects to the F2C network until it receives a name are described and the major challenges that must be solved are analyzed.
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A Hash-Based Naming Strategy for the Fog-to-Cloud
Computing Paradigm
Alejandro Gómez-rdenas, Xavi Masip-Bruin, Eva Marín-Tordera
Sarang Kahvazadeh, Jordi Garcia
Advanced Network Architectures Lab (CRAAX)
Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
{alejandg, xmasip, eva, skahvaza, jordig}
Abstract. The growth of the Internet connected devices population has fuelled
the emergence of new distributed computer paradigms; one of these paradigms
is the so-called Fog-to-Cloud (F2C) computing, where resources (compute,
storage, data) are distributed in a hierarchical fashion between the edge and the
core of the network. This new paradigm has brought new research challenges,
such as the need for a novel framework intended to controlling and, more in
general, facilitating the interaction among the heterogeneous devices conform-
ing the environment at the edge of the network and the available resources at
cloud. A key feature that this framework should meet is the capability of
uniquely and unequivocally identify the connected devices. In this paper a hash-
based naming strategy suitable to be used in the F2C environment is presented.
The proposed naming method is based on three main components: certification,
hashing and identification. This research is an ongoing work, thus, the steps to
follow since a device connects to the F2C network until it receives a name are
described and the major challenges that must be solved are analyzed.
Keywords: Naming, Identification, Fog-to-Cloud, Internet of Things.
1 Introduction
In simple words, the Internet of Things (IoT) is a communication paradigm where all
kind of everyday objects are capable to connect to the Internet network with different
purposes. This paradigm allows the creation of a range of new services and applica-
tions in diverse areas like smart homes, buildings and cities, eHealth, vehicular net-
works, wearables, monitoring and surveillance, etcetera. It is estimated that by 2020
the worldwide population of Internet connected objects will reach 50 billions [1].
Taking advantage of the large number of devices with network connectivity and in
consideration of the expected growth, new computer paradigms have emerged, one of
them is the Fog-to-Cloud (F2C) computing.
F2C is a collaborative and distributed compute model where resources (like stor-
age, compute or data) are located in a hierarchical fashion not only at the core of the
network but also at the edge [2]. In many cases, the resources conforming the F2C at
the edge of the network are supplied by the end users, thus, users can not only access
to the service provider or third parties resources but also share their own resources.
Being a hierarchical model, the resources are deployed in a bottom-up fashion,
usually with the most constrained devices in the lower layer (very basic sensors and
actuators) and in the top of the hierarchy a virtually unlimited resource data center:
the cloud.
Many research efforts are focused in the design of a suitable F2C architecture [3]
for managing the distributed storage, compute, data, control and networking func-
A key functionality that any F2C architecture must meet is the capability to identi-
fy uniquely and unequivocally every device connected to the F2C network, thus, the
adoption of a naming strategy is required.
The list of available naming schemes is not short [4] [5] and ranges from the use of
existing services like the Domain Name Service (DNS) to the redesign of the comput-
er networks as are known nowadays to a not host-based-centric network. The problem
with those naming schemes is that most of them doesn’t meet the inherent F2C re-
quirements (such as interoperability, mobility, uniqueness and scalability) or the ef-
fort to implement them is far beyond the scheme itself.
Regardless the application specific requirements, according with [6] a good nam-
ing service should meet the three characteristics described in the “Zooko’s Triangle”:
decentralization, human-meaningful names and secure mapping of names. These three
design goals are represented as a side of the triangle and each side represents a design
tradeoff, so according with the original author, it isn’t possible to have the three char-
acteristics at the same time.
In this paper a new distributed hash-based naming strategy that meets the afore-
mentioned F2C requirements is presented. The proposed strategy is based in three
support modules which are: certification, hashing and identification.
The remainder of this paper is organized as follow: In section 2 the hashing tech-
nique is discussed and similar works are reviewed. In section 3 the proposed hash-
based naming strategy and its support modules are explained in detail. In section 4 the
key advantages of the proposal are studied. Finally, in section 5 the research conclu-
sions are exposed.
2 State of the Art
In this section the hash functions and its properties are briefly described, also an ex-
ample of a hash string value is shown and three distinct works where authors have
used a hash-based method for naming entities (virtual or physical) are analyzed.
2.1 Background
The hashing is a cryptographic technique widely used to map a data block of variable
size to a fixed-length output. It means that it does not matter whether the input is 1
byte or 1 terabyte, the output will be a string with a predefined size length. In [7] the
hash technique is described in function of its main properties, which are:
Variable input size. In the hash function h(x) where x is the input, the size
of x does not matter at all.
Fixed length output. As said before, the output size isn’t in function of the
input size.
Compute facility. It is relatively easy to compute h(x) for any given x.
One-way. For any given y, it is computationally infeasible to find x such
that h(x) = y, what means that it cannot be “unhashed”.
Collision resistance. It is computationally infeasible to find y != x such
that h(x) = h(y). It means that two different inputs always produce two
different outputs and vice versa, two different outputs always belong to
two different inputs.
There are many algorithms designed to implement the hash function, the most
popular are briefly reviewed in [8]. In the United States as well as much of the world,
the MD5 and SHA algorithms are the most widely used [7], nevertheless any other
algorithm that fulfill the listed properties is suitable for generating hash values.
In order to illustrate better the properties of the hash function three examples are
presented below (table 1). In the examples, the SHA-1 algorithm is used to hash three
different strings.
Table 1. Hash transformation examples using the SHA-1 algorithm.
First Example
Hash value
Second Example
Hash value
Third Example
Hello World! This is a test
Hash value
The input in every example is different. While in the first two examples the inputs
difference is very subtle (only changes the first letter, from capital “H” to “h”), in the
third example the input is not a word but a phrase. In the three cases the hash value
output always is a totally different 160-bits string.
The hashing is a destructive process, what means that there is not a way to return to
the original input starting from the hash output value.
2.2 Related Work
Some researchers have found in the properties of the hash function an opportunity for
the creation of new naming schemes. In [9] the authors review extensively the use of
the hash function for naming objects. They claim that in order to avoid collisions the
SHA-256 algorithm must be implemented. However, in constrained environments or
in scenarios where a higher collision probability can be tolerated, the system adminis-
trator can opt for using a truncated version of the hash function output. In no case they
recommend the use of names with less than 100-bits; they assert that in those scenari-
os the collision resistance property cannot be guaranteed.
In the previously cited publication the authors use the SHA-256 algorithm to in-
clude a hash string as a segment in Universal Resource Locators (URL). With the
purpose of standardize the uses of hash outputs in URLs, they specify a new URI
scheme and a way to map these to URL’s, however, their proposed method lacks of a
clear hash input proposal. Although they mention that public keys are a good hash
function input candidate, they let the users to choose the input value, so in scenarios
where the user not only select an inappropriate input but also decide to use the trun-
cated hash function output, the collision probability could be very high.
In [10] a hybrid naming scheme for vehicular content centric networks is present-
ed. In the proposal the authors divide the content name (CN) into three parts: the
scheme, the prefix and the hash. The first part is the naming scheme identifier that is
used to represent CN. This field can take two different values in function of the used
The prefix is the hierarchical part of the name scheme and is used to identify the
content originating node that is a vehicle and the content itself in a human-readable
format. The distinct parts of the prefix are separated by a slash (“/”) and the firsts four
fields are reserved for the publisher vehicle’s information. The rest of the hierarchical
section signifies the information about the digital content (e.g. text, video, image, or
any other digital content).
Finally, the hash section of the CN corresponds to the full or truncated hash value
generated using the digital content, the content attributes or the public key of the in-
formation related to it.
According with the authors, the last field is used to uniquely identify the content
item. Nevertheless, in a scenario in which two or more vehicles are sharing distinct
contents but with the same attributes, if the hash function input are those attributes,
the probability of having duplicated records making reference to different contents
will be high. The solution to this problem could be to increase the number of attrib-
utes in the prefix section, but this decision will impact the lookup throughput, what in
content centric networks is critical.
Another similar work is the presented in [11]. In their work the authors does not
propose a naming strategy but a name resolution scheme consisting of two parts:
name mapping and name resolution.
Basically, what they do is to use a hash function to translate the heterogeneous de-
vice name to a fixed-length string, hiding like this the original name from the outside
Internet for security and privacy reasons.
In the name mapping the object name is received and translated to a 160-bits string
using the SHA-1 hash function algorithm. A notable drawback that this strategy pre-
sents is that to be recognized by the system the user has to hash the object name and
register the resulted string to the resolution system in advance. This could be a tedious
task for users owning multiple devices.
Another weak point of this strategy is that two devices with the same name will
have exactly the same hash output value and as result, duplicate register may exist in
the resolution adopted scheme (DNS or DHT).
3 Proposed Naming Strategy
In this section the naming strategy is presented. The proposed scheme consists of
three main components: a certification, a hash function and the identification module.
The technique described in the next lines aims to be a part of the resource manage-
ment functions (figure 1) in a F2C environment.
Fig. 1. Resource Identification strategy for the F2C Architecture.
3.1 Certification
The certification is the very first step that users must complete to have their device(s)
connected to the F2C network. In this phase the users register his personal infor-
mation in the system to get a secret key.
This registration process must to be done once per entity (person, institution or
company) regardless the number of devices the entity wants to use in the F2C ecosys-
tem. For example, if a government department needs to deploy thousands of devices
through a specific area in the city, the institution only have to register once to get the
secret key. This process is shown in the figure 2.
The implementation of this first phase will bring new challenges that must to be
solved. The major challenges are related with the system security. There is a lot of
attacks that the system must not only to resist but also to detect.
In the certification phase as well as in the other two components of the identifica-
tion strategy to provide a secure communication channel that discard the risk of inter-
ception is a crucial requirement.
Fig. 2. Certification phase in the Resource Identification strategy
Apart from security, other considerations must to be taken into account in the certi-
fication, for example, due the key assignation will be a distributed process, a mecha-
nism to disallow secret key overlapping will be necessary. Also, the system must to
be able to suspend, revoke and update the user secret key.
3.2 Hash Function
Being the module that stores the naming scheme, the hash function is the core of the
identification strategy. This function is the responsible of transforming the device
identification input into a hash string.
The device identification input is composed by two concatenated string. The first
string is the user secret key obtained during the registration phase while the other is an
“optional” user string (figure 3).
Due the purpose of the second part of the string is to differentiate among the user
devices, it will be optional only in those cases where the user owns or wants to use
only one of his devices in the F2C network, otherwise it will be a mandatory field.
In [12] the author explains that it is nearly impossible to have one global naming
convention mainly because industries have been using their own proprietary naming
conventions for long time and migrating to a different naming convention will impact
their infrastructure considerably. Nevertheless, this method does not force the users to
abandon their own internal naming convention. In the second part of the string the
user can use whatever value they want regardless the length.
Continuing with the previous government department example, let’s assume that
for internal reasons the institution uses a hierarchical naming convention that includes
the city, a code area where the device is located and at the end a consecutive number.
The internal records will look something like this: LA347-01, LA347-02, LA347-XX.
The adoption of this or any other naming scheme won’t affect the string conversion
Once the user identification string has been transformed into the final hash value it
is stored in key nodes across the F2C network using Distributed Hash Tables (DHT).
A full backup of the records always is kept in a cloud data server.
As well as in the previous step, the hash function module also have some challeng-
es that must to be addressed. One of the biggest challenges is the need of an incentive
that encourage users to keep using exactly the same string in both sides of the hash
input. This is particularly important for the implementation of long term identification
mechanisms and other historical functions because as was shown in Table 1, the min-
imum change in the input string will change dramatically the device identifier / name.
Fig. 3. Hash function process.
3.3 Identification
The last step in the proposed strategy is to look up for the hash value of the device in
the DHT, it is the identification.
In this point there could be three distinct scenarios:
The device is new in the system. When a device connects for the very first
time to the F2C network the look up in the DHT won’t find any coinci-
dence. In that case, the system should register the device and perform oth-
er assistant tasks (e.g. device characterization) in order to recognize it in
future interactions. The device will be registered in the DHT closer to the
device physical location and after x seconds, this and other new records
will be propagated to the upper F2C nodes in a hierarchical fashion, until
the record(s) reach the cloud where will be stored for a long term.
The device is connected to the F2C network in a known location. The
DHT will store for a predefined period of time a cache with all the devices
that have been connected in the last x days, so when a device reconnects
to the same F2C node, it will recognize the device without going to an up-
per level to look up for the device information. In this process the network
hierarchy will be leveraged. When a device is connected to a different but
still close node from the habitual one, it won’t have to go to cloud to have
the device information; going to one layer higher will be enough (figure
The device is connected in a distinct location than the habitual. Let’s as-
sume a three layered F2C network; if the device is connected in a distant
location and there is not information available in the same layer or even in
the next upper layer, there is still the cloud database, what in terms of
costs will be cheaper that characterize again the device.
In the figure 4 the “mobile device” uses to connect to the F2C network through the
nodes “A” and “B”, so every time it connects using one of those nodes the system
automatically detects and retrieves all the device information. In the case that the
node connects for the first time or after a long time to the F2C using the node “C”
where there is not information available about this device, the system will search in
the next upper layer for information about it. In the node “J” a copy of all the device
information is kept and updated for the nodes “A” and “B”.
Now, let’s consider that the “mobile device” moved to the area of the aggregator
node “H” and connects to the system using that node. Being the first time in this zone
there is not information available about the device, neither at the node “H” nor at the
next layer (node “K”), however, the node “J”, aggregator of the nodes “A”, “B”, “C”
and “D” registered the device in the cloud database so there is information about it
that the node “K” can access anytime and thus, the node “H”.
Fig. 4. Three layered Fog-to-Cloud network topology.
In this third step the main two problems that may arise are a poor throughput in the
lookup process and a high network overhead caused by the mobile devices. Neverthe-
less, if suitable policies are applied these two problems can be overcome.
4 Proposal Advantages
The key advantages of the proposed naming strategy are the capability of assign
worldwide unique names to the devices connected to the F2C network. In the case of
two or more F2C providers sharing the certification and hashing modules, the device
not only will use the same secret key to be identified but also it will keep the name
regardless the system provider.
The use of the Distributed Hash Tables will facilitate the implementation of new
functionalities in the system, such as a trust system, where the devices can get a clas-
sification in function of its availability, uptime, and other parameters. Other function
that the historical information stored in the DHTs will allow to implement in the plat-
form is a predictive resource utilization / available system without expose the device
specs or location.
Finally, if the hash function is implemented correctly, the possibility of a duplicat-
ed name will be minimal, what means that the proposed strategy is secure.
5 Conclusions
In this research work an integral hash-based naming strategy suitable for the Fog-to-
Cloud environment was proposed. The strategy is conformed for three main modules:
certification, hashing and identification. The proposal meets the F2C requirements,
such as mobility, scalability, security, privacy and uniqueness.
Even when the proposed naming strategy presents important advantages in com-
parison with other naming strategies and schemes there are still open challenges that
must to be addressed. Those challenges include the need of provide a secure channel
for the communications among edge devices and F2C nodes, a mechanism that disal-
low the secure key overlapping, the DHT lookup throughput, etcetera.
In order to solve the mentioned issues more research effort in every component of
the proposed method is needed, so the future work will be focused in overcome the
existing challenges.
This work is supported by the H2020 mF2C project (730929), as well as by the Span-
ish Ministry of Economy and Competitiveness and by the European Regional Devel-
opment Fund under contract TEC2015-66220-R (MINECO/FEDER).
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... The length of the IDs was different in each database. In the first database the length was set to 128 bytes (according to the identifier length used in [20]), this was the database containing the full resource identifiers. In the remaining databases, instead of using the full ID length, we used a fraction of the original IDs, this is, 32, 16, 8 and 4 bytes, respectively. ...
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WebNS: model for a peer-to-peer name service
  • C Webster
Craig Webster (2011) WebNS: Model for a Peer-to-peer Name Service. Monash University
  • S Farrell
  • C Dannewitz
  • B Ohlman
Farrell S, Dannewitz C, Ohlman B, et al (2013) Naming Things with Hashes. doi: 10.17487/rfc6920
Why DNS should be the naming service for Internet of Things?
  • S Balakrichenan
Sandoche Balakrichenan (2016) Why DNS should be the naming service for Internet of Things?
Study and comparative analysis of different hash algorithm
  • K Kumar Raghuvanshi
  • P Khurana
  • P Bindal
Kamlesh Kumar Raghuvanshi, Purnima Khurana, Purnima Bindal (2014) Study and Comparative Analysis of Different Hash Algorithm. JECAS 3:
OpenFog Reference Architecture for Fog Computing
  • Openfog Consortium
OpenFog Consortium (2017) OpenFog Reference Architecture for Fog Computing.