Ephemeral Computing and Bioinspired Optimization

Challenges and Opportunities

Carlos Cotta

, Antonio J. Fern

andez-Leiva

, Francisco Fern

andez de Vega

, Francisco Ch

avez

Juan J. Merelo

, Pedro A. Castillo

, David Camacho

and Gema Bello-Orgaz

Dept. Lenguajes y Ciencias de la Computaci

on, Universidad de M

alaga, M

alaga, Spain

Dept. Tecnolog

ıa de los Computadores y de las Comunicaciones, Universidad de Extremadura, M

erida, Spain

Dept. Arquitectura y Tecnolog

ıa de los Computadores, Universidad de Granada, Granada, Spain

Dept. Ingenier

ıa Inform

atica, Universidad Aut

onoma de Madrid, Madrid, Spain

Keywords:

Ephemeral Computing, Bioinspired Optimization, Evolutionary Computation, Complex Systems, Autonomic

Computing, Distributed Computing.

Abstract:

Computational devices with signiﬁcant computing power are pervasive yet often under-exploited since they

are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution

for solving complex computational tasks. Device-wise, this computational power can some times comprise a

stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts,

mainly in the presence of devices “lent” voluntarily by their users. A highly dynamic and volatile computa-

tional landscape emerges from the collective contribution of numerous such devices. Algorithms consciously

running on these environments require speciﬁc properties in terms of ﬂexibility, plasticity and robustness.

Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: de-

centralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert

advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing

self-adaptation capabilities to these techniques, yet the science of self-? bionspired algorithms is still nascent,

in particular regarding to higher-level self-adaptation, and self-management in the context of large scale opti-

mization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on

this scenario will also pave the way for the application of other techniques on this computational domain.

1 EPHEMERAL COMPUTING:

WHAT AND WHY

This paper revolves around the notion of Ephemeral

Computing (Eph-C) which can be deﬁned as “the use

and exploitation of computing resources whose avail-

ability is ephemeral (i.e., transitory and short-lived)

in order to carry out complex computational tasks”.

The main goal in Eph-C is thus making an effective

use of highly-volatile resources whose computational

power (which can be collectively enormous) would be

otherwise wasted or under-exploited. Think for exam-

ple of the pervasive abundance of networked handheld

devices, tablets and, lately, wearables –not to mention

more classical devices such as desktop computers–

whose computational capabilities are often not fully

exploited. Hence, the concept of ephemeral com-

puting partially overlaps with ubiquitous comput-

ing, volunteer computing and distributed computing

(how these research ﬁelds deal with the concept of

ephemerality is explained in next section) but exhibits

its own distinctive features, mainly in terms of the ex-

treme dynamism of the underlying resources, and the

ephemerality-aware nature of the computation which

autonomously adapt to the ever-changing computa-

tional landscape, not just trying to ﬁt to the inherent

volatility of the latter but even trying to use it for its

own advantage.

In light of the computational context described

above, it is clear that the algorithmic processes de-

ployed onto it should be ﬂexible (to work on a variety

of computational scenarios), resilient (to cope with

sudden failures and with the phenomenon of churn

(Stutzbach and Rejaie, 2006)), (self-)adaptive (to re-

act autonomously to changes in the environment and

optimize its own performance in a smart way), and

intrinsically decentralized (since centralized control

Cotta, C., Fernández-Leiva, A., Vega, F., Chávez, F., Merelo, J., Castillo, P., Camacho, D. and Bello-Orgaz, G..

Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities.

In Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 1: ECTA, pages 319-324

ISBN: 978-989-758-157-1

319

strategies cannot consistently comprehend the state

of the computational landscape and decisions emerg-

ing from them would lag behind the changing con-

ditions of the latter). Some bioinspired algorithms,

like the Evolutionary algorithms (EAs) ﬁt nicely into

this scenario. However, few works have previously

considered the interest of endowing evolutionary al-

gorithms with the capability for coping with transient

behaviors in underlaying computer systems. More-

over, in the age when the term Big Data (Lohr, 2012)

is present in many initiatives requiring large amount

of computational resources for storing, processing,

and learning from huge amount of data, new meth-

ods and algorithms for properly managing heteroge-

neous computing resources widely distributed along

the world are required. Energy consumption must

also be considered from the point of view of both al-

gorithms and hardware resources, given the large dif-

ferences among large computing infrastructures typi-

cally devoted to running massively parallel algorithms

when compared to smart devices optimized for reduc-

ing battery consumption. It is of the foremost interest

to research on the basic features allowing to provide

efﬁcient and reliable ephemeral evolutionary services.

2 EPHEMERAL COMPUTING IN

PERSPECTIVE

According to the Oxford Dictionary, the term

ephemeral means “lasting for a very short time”. It

thus encompasses things or events with a transitory

nature, with a brief existence. A number of phenom-

ena and resources in computer science are endowed

with that feature (e.g. in computing networking, an

ephemeral port is a TCP port, for instance, dynami-

cally assigned to a client application for a brief period

of time, in contrast with well known ports) (Borella

et al., 2000). Ephemeral behaviors can be also ob-

served in the way users collaborate in volunteer net-

works of computers.

Although ephemeral phenomena naturally arise in

several areas such as ubiquitous computing, volun-

teer computing or traditional research areas like dis-

tributed computing, some issues arise when dealing

with ephemeral behavior. In cloud computing (Arm-

brust et al., 2010), for instance, the opposite is usu-

ally looked for: persistence. Although services are

commonly associated with computations among au-

tonomous heterogeneous parties in dynamic environ-

ments, exceptions must be handled to take corrective

actions. Ephemeral services are thus commonly seen

more as a problem than a solution (Huhns and Singh,

2005).

On the other hand, in ubiquitous computing the

main goal is to leverage computation everywhere and

anywhere, so that computation can occur using any

kind of device, in any location, starting and ending at

any time and using any format and during any amount

of time. The main efforts in this area have been ori-

ented to design and develop the underlying technolo-

gies needed to support ubiquitous computing (Lyyti-

nen and Yoo, 2002) (like advanced middleware, op-

erating systems, mobile code, sensors, microproces-

sors, new I/O and user interfaces, networks or mo-

bile protocols). However, and in the same way it hap-

pens with cloud computing, the main target in ubiqui-

tous computing is to allow stable and persistent com-

putation processes perform a complete execution of

the programs. When this area handles the concept of

ephemeral devices, services or computation, the main

solution is to stop the process, or processes, and re-

sume once new devices are ready (Wang et al., 2004).

Previous hypothesis and assumptions can be extrapo-

lated to distributed computing, where the concept of

ephemeral services can be a problem that could even-

tually generate a failure in the execution of the process

(Sharmin et al., 2005).

As stated before, the main focus of Eph-C is differ-

ent from the above approaches: rather than trying to

build layers onto the network of ephemeral resources

in order to “hide” their transient nature and provide

the illusion of a virtual stable environment, Eph-C ap-

plications are fully aware of the nature of the com-

putational landscape and are speciﬁcally built to live

(and optimize their performance) in this realm. Note

this does not imply the latter have a lower-level vision

of the underlying computational substrate, or at least

not markedly so. In fact, most low-level features can

be abstracted without precluding attaining a more ac-

curate vision of this ﬂuctuating substrate.

To some extent, some of these ephemerality is-

sues are also present in areas such as volunteer com-

puting (VC) (Sarmenta and Hirano, 1999), whereby

a dynamic collection of computing devices collabo-

rate in solving a massive computational task, decom-

posing it into small processing chunks. Most VC

approaches follow a centralized master/slave scheme

though, and typically deal with resource volatility

via redundant computation. A much more decentral-

ized, emergent approach can be found in amorphous

computing (Abelson et al., 2000), but that paradigm

is more geared towards programmable materials and

their use to attack massive simulation problems. Mas-

sive problems are also the theme in ultrascale com-

puting, where issues such as scalability, resilience to

failures, energy management, and handling of large

volume of data are of paramount importance (Kamil

ECTA 2015 - 7th International Conference on Evolutionary Computation Theory and Applications

320

et al., 2005; Network for Sustainable Ultrascale Com-

puting, 2014). Note however Eph-C is not necessarily

exascale nor it is oriented towards supercomputing.

3 BIOINSPIRED ALGORITHMS

AND EPHEMERALITY

The term bioinspired algorithms usually refers to

methods that draw some inspiration from Nature

to solve search, optimization or pattern recognition

problems. If we focus on optimization problems,

the most prominent bioinspired paradigms are evolu-

tionary computation and swarm intelligence. We are

particularly interested in these kinds of population-

based search and optimization algorithms, which have

a natural path to distributed computing by simply dis-

tributing the population among the different comput-

ing nodes, the issue being how to do it in an algo-

rithmically efﬁcient and scalable way. Eph-C, besides

the obvious fact that the contribution of a node might

come and go at any time, adds several other dimen-

sions to the design of algorithms:

• Inclusion: all nodes should have a meaningful

contribution to the ﬁnal result, and they should be

incorporated to the distributed system in such a

way that they do.

• Asynchrony: nodes communicate with the others

without a ﬁxed schedule due to their different per-

formance.

• Resilience: the sudden disappearance of comput-

ing nodes must not destabilize the functioning of

the algorithm.

• Emergence: the nature of the computational envi-

ronment does not allow a centralized control and

requires decentralized, emergent behavior.

• Self-adaptation: the algorithm should adapt itself

to the changing computational landscape.

This latter issue is particularly important, and en-

compasses a number of self-? properties (Babaoglu

et al., 2005) the system must exhibit in order to ex-

ert advanced control on its own functioning and/or

structure, e.g., self-maintaining in proper state, self-

healing externally infringed damage (Frei et al.,

2013), self-adapting to different environmental con-

ditions (Nogueras and Cotta, 2015b), and even self-

generating new functionalities just to cite a few exam-

ples, see also (Cotta et al., 2008; Eiben, 2005). Quite

interestingly, these properties are frequently intrinsic

features of the system, that is, emergent properties

of its complex structure, rather than the result of en-

dowing it with a central command. This also implies

there is no need for a central control in the system;

every node schedules itself. This decentralization im-

plies a certain fault-tolerance due to the lack of a sin-

gle point of failure, but it also means resilience must

be built into the algorithms present in each node so

that their sensitivity to changes in the rest of the sys-

tem is minimal. This will include measures such as

population sizing and the conservation of diversity in

each node, as indicated by Cant

u-Paz in (Cant

u-Paz,

1998) but taken to new meanings in this context. In-

deed, models and algorithms have to be designed to

be fault-tolerant (Nogueras and Cotta, 2015a) so that

inclusion of new nodes will be done in a self-adaptive

way, but also in such a way that its disappearance

from the network will not have a big impact on perfor-

mance. In fact, VC systems, which are an early exam-

ple of Eph-C, have been proved to be fault tolerant to

a certain point (Gonz

alez Lombra

na et al., 2010), but

this fault tolerance will have to be taken into account

not just at the implementation level (backing up solu-

tions, for instance) but also at the model and algorithm

level, measuring the impact of different churn models

(Laredo et al., 2008; Nogueras and Cotta, 2015c).

4 EPHEMERAL

COMPUTING-BASED

APPLICATIONS

This section provides a short revision on some of

those bioinspired methods and applications that could

be affected by Eph-C characteristics.

4.1 Big Data & Bio-inspired Clustering

The data volume and the multitude of sources have

experienced an exponential growing with a new tech-

nical and application challenges. The data genera-

tion has been estimated as 2.5 quintillion bytes of

data per day

. This data comes from everywhere:

sensors used to gather climate, trafﬁc, air ﬂight in-

formation, posts to social media sites (i.e. Twit-

ter or Facebook as popular examples), digital pic-

tures and videos (YouTube users upload 72 hours of

new video content per minute

), purchase transaction

records, or cell phone GPS signals to name a few. The

classic methods, algorithms, frameworks or tools for

data management have become both, inadequate for

http://www-01.ibm.com/software/data/bigdata/what-is

-big-data.html.

http://aci.info/2014/07/12/the-data-explosion-in-2014-

minute-by-minute-infographic/).

Ephemeral Computing and Bioinspired Optimization - Challenges and Opportunities

321

processing these amount of data, and unable to of-

fer effective solutions to deal with the data growing.

The management, handling and extraction of useful

knowledge from these data sources is currently one

of the most popular and hot topics in computing re-

search.

In this context, Big Data is a popular phenomenon

which aims to provide an alternative to traditional so-

lutions database and data analysis, leading to a rev-

olution not only in terms of technology but also in

business. It is not just about storage of and access to

data, Big Data solutions aim to analyze data in order

to make sense of that data and exploiting its value.

One of the current main challenges in Data Min-

ing related to Big Data problems is to ﬁnd ade-

quate approaches to analyze massive data online (or

data streams). Due classiﬁcation methods requires

from a previous labelling process, these methods need

high efforts for real-time analysis. However, due

to unsupervised techniques do not need this previ-

ous process, clustering becomes a promising ﬁeld for

real-time analysis. Clustering is perhaps one of the

most popular approaches used in unsupervised ma-

chine learning and in Data Mining (Han and Kam-

ber, 2006). It is used to ﬁnd hidden information or

patterns in an unlabelled dataset and has several ap-

plications related to biomedicine, marketing (Haider

et al., 2012), or image segmentation (Pascual et al.,

1999) amongst others. Clustering algorithms provide

a large number of methods to search for ”blind” pat-

terns in data, some of these approaches are based on

Bio-inspired methods such as evolutionary computa-

tion (Goldberg, 1989), swarm intelligence (Bonabeau

et al., 1999) or neural networks amongst others.

In the last years, and due to the fast growing of

a large Big Data-based problems, new challenges are

appearing in previous research areas to manage the

new features and problems that these type of problems

produce. New kinds of algorithms, as online clus-

tering or streaming clustering are appearing to deal

with the main problems related to Big Data domains.

When data streams are analyzed, it is important to

consider the analysis goal, in order to determine the

best type of algorithm to be used. We could divide

data stream analysis in two main categories:

• Ofﬂine Analysis: we consider a portion of data

(usually large data) and apply an ofﬂine cluster-

ing algorithm to analyze this data.

• Online Analysis: the data are analyzed in real-

time. These kinds of algorithms are constantly

receiving new data instances and are not usually

able to keep past information. The most relevant

limitations of these systems are: the data order

matters and can not be modiﬁed; the data can not

be stored or re-analyzed during the process; the

results of the analysis depend of the time the al-

gorithm has been stopped. The main problem of

these algorithms is that they need a speciﬁc space

to update the information. This reduces the possi-

bilities of the new algorithm.

From our previous experience in different complex

and industrial problems in different areas from Social

Networks Analysis (Bello-Orgaz et al., 2014; Bello-

Orgaz et al., 2012), Project Scheduling, Videogames,

Music classiﬁcation, Unmmaned Systems, or Bio-

informatics, we have designed and developed several

bioinspired algorithms for clustering or graph-based

computing with the aim to handle Big Data-based

problems. We can distinguish from two main types

of algorithms, those that have combined evolution-

ary strategies (mainly genetic algorithms) (Men

endez

et al., 2014a; Men

endez et al., 2014b) and the sec-

ond ones which have been designed using swarm in-

telligence approaches (ant colonies optimization al-

gorithms) (Gonzalez-Pardo and Camacho, 2015).

4.2 Social-based Analysis and Mining

With the large number and fast growing of Social Me-

dia systems and applications, Social-based applica-

tions for Data Mining, Data Analysis, Big Data com-

putation, Social Mining, etc. has become an impor-

tant and hot topic for a wide number of research ar-

eas. Although there exists a large number of existing

systems (e.g., frameworks, libraries or software ap-

plications) which have been developed, and currently

are used in various domains and applications based on

Social Media. The applications and their main tech-

nologies used are mainly based on Big Data, Cloud or

Grid Computing. The concept of Ephemeral comput-

ing has been rarely considered.

Most of the current challenges under study in

Social-based analysis and mining are related to the

problem of efﬁcient knowledge representation, man-

agement and discovery. Areas as Social Network

Analysis (SNA), Social Media Analytics (SMA) and

Big Data, have as main aims to track, trends dis-

covery or forecasting, so methods and techniques

from: Opinion Mining, Sentiment Analysis, Multi-

media management or Social Mining are commonly

used. For example, when anyone tries to analyze

how a Social Network is evolving using a straightfor-

ward representation based on a graph, but ignoring the

information ﬂow between nodes the information ex-

tracted from this analysis will be very limited. Other

simple example, based on SNA, is an application that

could try to extract behavioral patterns among users

connected to a particular social network without tak-

ECTA 2015 - 7th International Conference on Evolutionary Computation Theory and Applications

322

ing into account their connections, their strengthens,

or how their relationships are evolving through time.

Social Big Data analysis, instead, aims to study large-

scale Web phenomena such as Social Networks from

a holistic point of view, i.e., by concurrently taking

into account all the socio-technical aspects involved

in their dynamic evolution.

Previous domains could be joined into a more gen-

eral application area named Social Big Data. This

area, or application domain, comes from the joining

efforts of two domains: Social Media and Big Data.

Therefore, Social Big Data will be based on the analy-

sis of very-large to huge amount of data, which could

belong to several distributed sources, but with a strong

focus on Social media. Hence, Social Big Data anal-

ysis (Cambria et al., 2013; Manovich, 2011) is inher-

ently interdisciplinary and spans areas such as Data

Mining, Machine Learning, Statistics, Graph Min-

ing, Information Retrieval, Linguistics, Natural Lan-

guage Processing, Semantic Web, Ontologies, or Big

Data Computing, amongst others. Their applications

can be extended to a wide number of domains such

as health and political trending and forecasting, hob-

bies, e-business, cyber-crime, counter terrorism, time-

evolving opinion mining, social network analysis, or

human-machine interaction.

Taking into account the nature of Social Big Data

sources and the necessary processes and methods

that will be required for data processing, the knowl-

edge models, and possibly the analysis and visualiza-

tion techniques to allow discover meaningful patterns

(Kaisler et al., 2013), the potential application of Eph-

C features could generate a new kind of algorithms

that would be suitably applied in ephemeral environ-

ments.

5 CONCLUSIONS

Ephemeral computation provides an interesting new,

and promising, research area with signiﬁcant differ-

ences when it is compared against other areas as grid

computing, or traditional distributed computing. Al-

though Eph-C presents some features close to volun-

teer computing or amorphous computing, the combi-

nation of their main features: inclusion, asynchrony,

resilience, emergence, and self-adaptation, deﬁnes it

more precisely.

Therefore, the main focus of Eph-C is different

from the above approaches, rather than trying to build

layers onto the network of ephemeral resources in or-

der to “hide” their transient nature and provide the

illusion of a virtual stable environment, Eph-C appli-

cations are fully aware of the nature of the computa-

tional landscape and are speciﬁcally built to live (and

optimize their performance) in this realm.

Related to the application of traditional methods

and techniques from Machine Learning to Big Data

problems, our previous experience has shown the high

performance that bioinspired algorithms can achieve

in huge, open and dynamic problems, showing how

bioinspired approaches can be used to improve the

performance of unsupervised approaches. In the near

future, and taking into account the new restrictions

and features imposed by Eph-C environments, a new

suit of algorithms able to efﬁciently handle the new

challenges in data management and knowledge dis-

covery in large Big Data-based problems will be stud-

ied and analyzed.

ACKNOWLEDGEMENTS

This work is supported by MINECO project

EphemeCH (TIN2014-56494-C4-1-P, -2-P, -3-P and

-4-P) – Check http://blog.epheme.ch.

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