Challenges and Research Directions in Big Data-driven Cloud

Adaptivity

Andreas Tsagkaropoulos

, Nikos Papageorgiou

, Dimitris Apostolou

1,2

Yiannis Verginadis

and Gregoris Mentzas

Information Management Unit (IMU), Institute of Communications and Computer Systems,

National Technical University of Athens (NTUA), Athens, Greece

Department of Informatics, University of Piraeus, Piraeus, Greece

Keywords: Cloud Adaptivity, Fog Computing.

Abstract: Mainstream cloud technologies are challenged by real-time, big data processing requirements or emerging

applications. This paper surveys recent research efforts on advancing cloud computing virtual

infrastructures and real-time big data technologies in order to provide dynamically scalable and distributed

architectures over federated clouds. We examine new methods for developing cloud systems operating in a

real-time, big data environment that can sense the context of the application environment and can adapt the

infrastructure accordingly. We describe research topics linked to the challenge of adaptivity such as

situation awareness, context detection, service-level objectives, and the capability to predict extraordinary

situations requiring remedying action. We also describe research directions for realising adaptivity in cloud

computing and we present a conceptual framework that represents research directions and shows inter-

relations.

1 INTRODUCTION

Cloud computing and, in particular, the

Infrastructure-as-a-Service (IaaS) model has become

the natural habitat for big data driven applications,

thanks to its unlimited scaling abilities. Big data

applications challenge cloud computing infrastru-

ctures which have not been designed to cope with

the velocity and sheer quantity of generated data.

The challenge is even greater when cloud

infrastructures are called to deal with the immense

amount of data that the billions of distributed IoT

sensors can genera-te. Thus, the cloud computing

paradigm is being expanded to reach the extreme

edge of the network, effectively developing so-

called fog infrastructures.

Mainstream technologies, such as Hadoop, are

challenged by real-time processing requirements,

due to their static nature (Burns 2013). While more

recent and dynamic technologies, such as Storm

(Apache Storm 2018), have proven to be efficient

computational platforms for real-time data stream

analytics, they fail to exploit the relatively new

decentralized architecture known as federated clouds

and fog infrastructures. A federated cloud is a hybrid

cloud that merges the resources available in multiple

clouds. Federated clouds consist of resource which

can be private clouds (e.g. in-premises OpenStack

installation), public IaaSes (e.g. Amazon Web

Services, Microsoft Azure) or decentralized clouds

(e.g. fog and edge clouds). Cloud resource

configuration can be performed using open

standards and APIs such as OCCI (OCCI, 2018),

CIMI (CIMI, 2013), TOSCA (OASIS, 2017) or

proprietary but widely used APIs like Amazon Web

Services (Amazon, 2018).

Recent research efforts have focused on

advancing cloud computing virtual infrastructures

and real-time big data technologies in order to

provide dynamically scalable and distributed

architectures on federated clouds. Various works

have proposed new methods for developing cloud

systems operating in a real-time, big data

environment that can sense the context of the

application environment and can adapt the

infrastructure accordingly.

Adaptivity in big data-driven cloud

infrastructures manifests primarily in two layers

(Figure 1): The real-time application adaptivity l

ayer refers to changes that can be performed in real

time by application-specific code on end devices.

190

Tsagkaropoulos, A., Papageorgiou, N., Apostolou, D., Verginadis, Y. and Mentzas, G.

Challenges and Research Directions in Big Data-driven Cloud Adaptivity.

DOI: 10.5220/0006761901900200

In Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018), pages 190-200

ISBN: 978-989-758-295-0

Figure 1: Adaptivity in big data-driven cloud infrastructures.

The topology adaptivity layer refers to changes that

can be performed to the processing topology and the

virtual resources available.

The challenge of adaptivity is linked to a number

of research topics that we survey in this paper:

situation awareness, context detection and the

capability to predict situations that pose the need for

adaptation. Our research objective is to investigate

the related state of the art, and facilitate the work of

researchers and practitioners through the

presentation of a conceptual framework. The

proposed framework aims to identify the tools and

methods needed for optimizing (i) the resource

usage for various applications based on specific

policies and (ii) the quality of service demands of

specific application ecosystems.

The remaining of the paper is structured as

follows: Section 2 provides an overview of the state

of the art in cloud adaptivity. Section 3 identifies

challenges for cloud computing technologies and

related research directions. Section 4 presents a

meta-management framework for cloud adaptation

for guiding relevant research & development needed

to realise big-data driven cloud adaptivity. Section 5

concludes the paper.

2 STATE OF THE ART

Cloud adaptation refers to the process of dynamic

selecting and configuring the cloud resources (such

as CPU, memory, storage or networking) that are

necessary to provide a service with the desired

quality attributes. (Jennings and Stadler, 2015) point

out that in cloud-computing there exist different

types of actors which have different objectives

pertaining cloud adaptation needs. Cloud providers

provide services (e.g. cloud infrastructure) to cloud

users (e.g. DevOps). Cloud users provide services to

end users (e.g. a web-based application). In IaaS or

PaaS context, cloud providers agree on certain SLAs

and Service Level Objectives (SLOs) with cloud

users. There are many categories of SLAs (Kyriazis,

2013). According to the authors, more relevant to

resource management are the SLOs that are

quantifiable (like those that are related to

performance and availability). SLOs sometimes,

depending on the definition of the SLA, may be

satisfied up to a certain possible degree, when other

constraints must be satisfied simultaneously.

Cloud providers may pursue additional

objectives that are important for their business such

as energy use minimization and fault tolerance, in

some cases in a prioritized manner depending on the

context (e.g. prioritize low energy use when the total

workload is not high). Cloud users have SLAs with

their customers. In order to satisfy them they agree

SLAs with cloud providers but they may have

additional objectives (such as minimizing the risk of

violating SLAs with end users by using multiple

cloud providers).

(Jennings and Stadler, 2015) in a recent survey

of over 250 publications about resource management

relevant to cloud based infrastructures recognize

four types of adaptation: “Infrastructure Scaling”,

“Virtual Machine Migration”, “Virtualization” and

Stream

Aggregator

Edge Layer

Stream

Processor

Cloudlet

Εvent processing

Context analysis

Data flow

Real-time analysis

On-demand

processing cloudlet

Stream

Processor

Real time application

adaptivity

Infrastructure-level

proactive adaptivity

Cloud Layer

Management

Framework

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

191

“Equipment Power State Adjustment”. Challenges

that the authors identify include: (i) the way to

achieve predictable performance when adaptations

are performed, knowing that the cloud resources are

shared between different applications; and (ii) the

way to globally manage different types of resources

(compute, storage, networking, etc.) in an

orchestrated way in order to achieve “Global

Manageability of Cloud Resources”. (Ranjan et

al.,2015) summarize the different types of attributes

(cores, speed, capacity, etc.) that can be configured

and orchestrated in cloud infrastructure resources

(CPU, BLOB storage, network, etc.) according to its

type (IaaS, SaaS, PaaS) and the operations that can

be applied to each attribute (start, stop, restart, etc.).

This work not only describes the cloud resources as

the ontology in (Youseff et al.,2008) but it attempts

to identify the way a cloud-based infrastructure can

be programmed in order to achieve its goals.

2.1 Adaptation Implementation

Concerning the actual implementation of adaptation

actions, proposed approaches broadly fall under two

categories: Proactive adaptation policies - which

anticipate unusual situations and take actions in

order to ensure the correct functioning of the service

and reactive adaptation policies, which use data

from the current situation in order to trigger the

necessary adaptation actions.

The review paper of (Lorido-Botran et al.,2014)

identifies and compares five categories of

approaches for decision making in auto-scaling:

Threshold-based rules, Reinforcement Learning

(RL), Queuing theory (QT), Control theory (CT) and

Time series analysis (TS). (Gandhi et al., 2012)

identify five similar auto-scaling approach

categories: Prediction models, Control theoretic

techniques, Queueing-based models, Black-box and

Grey-box approaches. Black box models use

machine learning or statistical methods for decision

making in order to overcome the problem of

modelling the cloud application using expert

knowledge. Grey-box models are hybrid approaches

that use models in combination with machine

learning (Gandhi et al., 2012). (Qu et al., 2016)

support in their review paper that, according to the

bibliography, resource estimation in horizontal or

vertical auto-scaling can be performed using Rules,

Fuzzy-Inference, Application-Profiling, Analytical

Modelling, Machine Learning or hybrid methods.

Analytical modelling according to the authors

includes Queuing Theory and Markov Chains.

Machine learning includes Reinforcement Learning

and Regression. Regression is applied in auto-

scaling techniques that use Time-series Analysis or

Control Theory.

Rule-based approaches are used in commercial

auto-scaling systems such as (Amazon AWS Auto-

Scaling service,2018) or (RightScale, 2018). An

advantage of this approach is that a DevOps user can

relatively easily create and understand them. In

(Lorido-Botran et al.,2014) the authors maintain that

while rule-based approaches are usually reactive,

they can be combined with queueing theory, or time-

series analysis, in order to respond proactively to

situations.

Another approach for adaptation relies on

Queuing theory, which discusses queues (waiting

lines) using mathematics. The modelling using

queues is relevant to cloud applications, because the

requests to a service can be assumed to follow a

queue. The result of the modelling is the necessary

resources to process a workload with a specific size.

However, as queuing models require a fixed

architecture in order to be solved, they need to be

solved every time that an adaptation occurs which is

computationally expensive.

A different approach uses Control Theory to

maintain an output variable (e.g. CPU usage)

constant, while modifying the resources of the cloud

infrastructure respectively (e.g. number of

VM’s/characteristics of a VM). According to the

review paper by (Lorido-Botran et al.,2014), these

approaches are fine-tuned either for vertical scaling

or horizontal scaling, but not for both.

An alternative method to handle the problem or

resource adaptation relies on machine learning, and

more specifically on Reinforcement Learning (RL).

Using this method, an agent interacts with the

system and proposes the best adaptation action (from

the pool of defined actions) based on its experience

in similar situations. Then, the outcome of the

adaptation is measured using a cost/reward function,

and the agent improves its understanding.

Unfortunately, models following the RL method

suffer from bad performance during on-line training

for a long time, require considerable processing

power, and – being highly specialized – perform

sub-optimally when the context is changed(Lorido-

Botran et al.,2014) .

Table 1 summarises the characteristics of the

aforementioned cloud infrastructure adaptation

approaches.

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

192

Table 1: Summary of Cloud Infrastructure Αdaptation Approaches.

Adaptation

method

Policy Type (proactive vs

reactive)

Real-time

model

adaptation

Model

response

after

context

change

Vertical and

Horizontal

Scaling

Optimization

Model

update

difficulty

Computational

complexity

Reinforcement

Learning

Proactive Yes Bad Yes Hard High

Queueing

Theory

Proactive No Bad None Hard High

Control Theory Reactive No Good Only one

attribute

optimized

Hard Average

Time Series

Analysis

/Prediction

Models

Proactive Yes Very

Good

None Average Average

Rule-based

approaches

Reactive (Can be combined

with queuing theory

methods or prediction

methods to gain proactivity)

No Good Yes Average Low

2.2 Application-specific Αdaptivity

This section presents recent approaches that deal

with the challenge of adapting applications than run

on cloud infrastructures (see also Table 2).

(Cavalcante et al., 2013) developed an autonomous

adaptation process for cloud-based applications by

replacing a service by an alternative one that fulfils

the application needs and describe the adaptation

process within the Cloud Integrator, a service-

oriented middleware platform for composing,

executing, and managing services provided by

different cloud platforms. (Inzinger et al. 2013),

(Inzinger et al, 2014), proposed a provider-managed,

model-based adaptation approach for cloud

computing applications, allowing customers to

specify application behaviour goals or rules, thus

actively engaging the application refactoring

process. There are also recent agent-based efforts

that try to fuse adaptivity in cloud resources usage.

For example, (Comi et al.,2015) present an approach

based on agent cloning, i.e. a mechanism of agent

reproduction allowing providers to substitute an

“unsatisfactory” agent acting in a “cloud context”

with a clone of an existing agent having suitable

knowledge and good reputation in the multi-cloud

context.

Another stream of work focuses on application

migration approaches. (Gholami et al., 2016)

published a detailed survey of cloud migration

approaches. The authors revealed that little work

exists that provides a means to design situation-

specific approaches with respect to the

characteristics of a migration project. (Inese et al.,

2015) described methods used for enterprise

application decomposition in cloud migration

projects. Their work distinguishes between four

decomposition phases: fact extraction, pre-

processing, clustering/component classification and

post-processing. Methods for fact extraction include

static, dynamic and semantic code analysis as well

as dynamic SQL analysis. Pre-processing includes

similarity evaluation, trace compression, rules,

classification, code cleansing and concept

assignment. Clustering and component identification

is typically done with clustering methods as well as

rules. Finally, post-processing refers to evaluation

methods using rules, refinement as well as

optimisation and layer identification.

A methodological approach for cloud migration

has been developed by (Jamshidi et al., 2016) based

on (i) a catalogue of fine-grained service-based

cloud architecture migration patterns that target

multi-clouds, (ii) a Situational migration process

framework to guide pattern selection and

composition, and (iii) a variability model to structure

system migration into a coherent framework. The

proposed migration patterns are based on empirical

evidence from several migration projects, best

practice for cloud architectures and a systematic

literature review of existing research. The

methodology allows an organization to (i) select

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

193

appropriate migration patterns, (ii) compose them to

define a migration plan, and (iii) extend them based

on the identification of new patterns in new contexts.

A cloud migration strategy recommender method

is proposed by (Bonab and Busherian), 2015). The

main rationale behind this method is to provide easy

and fast recovery from failed components or

replacing the required functionality of the legacy

software with the reliable cloud services. In this

paper a semi-automated reverse engineering method

based on the clustering algorithms is proposed to

recommend the best migration-to-cloud strategy.

The recommendation is based on four defined

metrics: the extent of effort required for

reengineering, maintenance costs, achieved

availability and the number of cloud services that are

used.

(Kwon and Tilevich, 2014) proposed an

application refactoring approach based on a

recommender tool that computes the coupling

metrics for all the classes in a legacy application and

then displays the classes that are least tightly

coupled. Accessing the functionality represented by

these classes from a remote cloud-based service

should impose only a limited performance penalty

on the refactored application. The approach

leverages two recommendation mechanisms:

profiling and clustering-based recommenders. The

profiling-based recommender engages a static

program analysis and runtime monitoring to collect

program information. By combining the class

coupling metrics collected through both static

analysis and runtime monitoring, the

recommendation algorithm then suggests a subset of

an application that can be transformed to cloud-

based services. The profiling-based recommender

sorts application classes based on their execution

duration and frequencies, so that the programmer

can know what classes are computation-intensive

and how frequently they are accessed. (ii) The

clustering-based recommender clusters classes with

similar functionality, thus identifying class clusters

whose functionality can be naturally exposed as a

cloud-based service. Because the clustering-based

recommender groups classes based on their

functionality, the programmer can avoid duplicating

a functionality in the cloud by selecting candidates

for cloud-based service from different clusters.

(Hilton et al., 2014) developed Cloudifyer, a

touchdevelop IDE plugin which refactors

touchdevelop scripts in place. First, Cloudifyer

retrieves the source of the target app as an Abstract

Syntax Tree (AST) stored in JSON format from the

touchdevelop script bazaar. It then transforms the

AST as needed. Once all the transformations are

performed, Cloudifyer completes the refactoring by

saving the new AST for the target app. (Vasconcelos

et al.,2015) presented a novel approach to support

organizations in automatically adapting existing

software applications to the cloud. The approach is

based on the loosely-coupled implementation of

non-intrusive code transformations, called cloud

detours, which enable the automatic replacement of

local services used by an application with similar or

functionally-related services available in the cloud.

Table 2: Summary of Application-level Αdaptivity Approaches.

Authors Approach Adaptation type Active multi-cloud

support

Cavalcante et al. Replacement of a service with a new

instance fulfilling application needs

Component

replacement

Inzinger et al. Adaptation based on application behavior

goals or rules

Satisfaction of goals

/rules

Comi et al. Substitution of unsatisfactory agents with

suitable clones of better-performing

existing agents

Component

replacement

Yes

Inese et al. Methods for application decomposition in

cloud migration projects

Cloud migration Yes

Jamshidi et al. Methodological approach for cloud

migration

Cloud migration Yes

Bonab et al. Easy and fast recovery from failed

components/ replacement of legacy

components with reliable cloud services

Cloud migration Yes

Kwon and Tilevich Application refactoring based on class-

coupling metric

Processing offloading

to the Cloud

Hilton et al. Refactoring of touchdevelop scripts in

place

Processing offloading

to the Cloud

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

194

3 CHALLENGES AND

RESEARCH DIRECTIONS

The majority of existing works in cloud adaptation

focus on cloud resource selection and configuration

based on the computing needs of applications but do

not take into consideration trade-offs between cost,

speed, efficiency and reliability of adaptations.

Future research can investigate new ways to manage

the relations between Big Data processing, cloud

and edge resources adaptation needs and adaptation

strategies. Novel data analytics approaches are

needed for understanding from the past data

different modes of the cloud infrastructure

workloads and predicting which can be the next one

based on the current context of big data applications.

Factors that influence the workload can be studied

and new behavioural models can be built from past

data. These models should be truly data driven and

built in an unsupervised manner (no a priori labelled

data should be used).

Approaches for analysing past data should in a

way ‘explain’ what the usual behaviour of the

system is in a high-dimensional space, which can

dynamically change as incoming data will change.

Methods should ensure that the models will be

continuously updated to the new situations,

resolving the problem of the model drift. Moreover,

models should be used in real-time in order to check

what the current parameters of the system indicate

regarding the workload, i.e. can the current situation

be classified as “usual” (with the standard set of

actions), or it is unusual and requires an additional

processing.

Moreover, new methods are needed for

extracting and modelling context features of big data

applications that pertain cloud resource

requirements. To this end, works may need to extend

decision methods (Patiniotakis et al.,2013a),

(Patiniotakis et al.,2013b) with the necessary

enhancements and extensions in order to deal with

the scalability issues involved in the Big Data

processing application domain. This will enable

adapting resources allocation and deploying parts of

the data-intensive applications across the processing

topology. For example, a new processing resource

can be recommended based on a predefined but

dynamically changeable pool of resource

alternatives that should be configurable according to

the context of the case. Moreover, the processing

load of a specific resource can be offloaded to

another one that maybe closer to the edge of the

network for efficiency reasons.

To support adaptation triggering, instead of

using static threshold-based alerts for an entire class

of cloud resources as most monitoring systems

typically use, there is a need to develop new

triggering methods based on monitoring multiple

data streams, understanding each individually as

well as their relationship to each other, resulting in a

highly sensitive system that can provide early

recommendations for adaptations to optimise

performance.

4 ADAPTATION

META-MANAGEMENT

FRAMEWORK

This section describes our proposal for a meta-

management framework for big data-driven cloud

adaptation. This refers to a conceptual layer on top

of the existing cloud infrastructure management

layers aiming to provide support for dynamic

adaptation of the cloud infrastructure based on

dynamically changing, big data application

computing needs. The optimal selection of cloud

resources is the primary objective of the meta-

management framework, which should support

decisions making for the number and kind of

resources, the location of the resources and the level

of virtualization. The so-called decision enablers are

described next.

4.1 Adaptivity Decision

4.1.1 Scale Up - Scale Down

The first function of the proposed framework is to

scale the cloud application service up and down,

depending on the workload, using a suitable

deployment platform (virtual machines or

containers). This scaling can be implemented

horizontally or vertically (Michael et al., 2007).

Horizontal scaling is defined as the ability of a cloud

service to spawn more (limit existing) VM instances

running the same application. Vertical scaling is

defined as the process of increasing (decreasing) the

specifications of the VM or container hosting the

application at runtime. If an application can use both

types of scaling, then we say that it can use hybrid

scaling.

(Dutta et al.,2012) claim that vertical scaling can

offer lower cost adaptations, most appropriate when

the range of the intensiveness of the application

workload is relatively small. Besides, horizontal

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

195

scaling can allow for much higher throughput albeit

possibly at higher cost. (Fang et al., 2012) describe

vertical scaling as a methodology most appropriate

for regular adaptations to workload, whereas

horizontal scaling is more appropriate in cases of

sharp workload changes.

However, not all applications support vertical

scaling and horizontal scaling. For example,

applications relying on the JVM should restart

before being able to modify the heap allotted to a

java program. Also, applications not using external

load-balancing frameworks will not benefit from the

additional instances spawned. The above mentioned

special characteristics of each mode of adaptation

indicate that research is required by application

developers in order to formulate the correct criteria

to trigger adaptations. A relevant survey, which

examines many scaling techniques used by

dedicated software (auto-scaling) has been compiled

by (Qu et al., 2016)

4.1.2 Migrate & Re-position

The second function of the framework is live service

migration. Live migration is the transfer of a service

to another processing resource, without user-

noticeable service interruption. Not only does it

enable maintenance operations to be conducted

without the interruption of the service, but it also

permits the reconfiguration of the service under

unexpected workloads, and can be used as a

preparation step for efficient vertical scaling. Several

published studies examine live migration as a means

to consolidate resources or handle increasing

workloads (Bryant et al., 2011), (Hermenier et al.,

2009), (Nguyen et al, 2013). It must be noted

however that live migration degrades the

performance of all virtualized entities running on

both the physical machine offloading the

VM/container, and the physical machine onloading

the VM/container (Koto et al, 2012).

4.1.3 Application-specific Adaptation

Actions

A third function of the framework is related to

supporting the class of adaptation actions that

consider the internal structure of the application.The

framework should expose to the DevOp the

capability to dynamically modify hardware

resources with application-level logic. For example,

in a grid computing cluster, the DevOp can specify

that when a “multicore processing” job arrives, all

processing nodes should request at least 8 cpu cores.

This flexibility provides one more means for the

DevOp to ensure proper quality of service.

4.2 Enabling Capabilities

To support technically decision making with respect

to cloud adaptivity, the framework puts forwards

certain enabling capabilities. In a real-world setting,

each capability described below reflects a software

component that can range in implementation from a

simple process in a virtual machine to a distributed

multi-cloud processing architecture. The enabling

capabilities are depicted in Figure 2.

Figure 2: Adaptation meta-management framework.

4.2.1 Workload Prediction

The meta-management framework should have the

capability, given an appropriate model and method,

recent monitoring information and workload

evolution over time, to predict the workload that

may be experienced by the cloud infrastructure in

the near future. In addition, it should be able to

predict possible failures because of the overuse of

certain processing nodes, thus enhancing the

reliability of the processing topology and

minimizing any chances for down time incidents.

While it is possible to predict the workloads of

long-running tasks based on the seasonality in their

historical workloads, it is difficult to do so for tasks

which do not have such recurring workload patterns.

Novel workload prediction approaches should be

developed that take the statistical properties of a

pool of tasks to help predict the workload patterns of

new tasks. Efficient machine learning methods can

process high-dimensional data and can be applied

directly to memory, disk bandwidth, and network

bandwidth demand predictions. Both machine

learning and statistical methods can leverage readily

existing tools, such as (TensorFlow, 2018), (Keras,

2018) or (R, 2018).

Workload

Predictor

Situation

Detection

Adaptation

Recommender

Real-time data

streams

Context Analyzer

Adaptation

Action

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

196

Workload prediction methods should be capable

• Receiving, pre-processing and storing the

data relevant for workload prediction (for

example: CPU usage, RAM usage, network

and disk i/o, etc.).

• Understanding and modelling the types of

the workloads based on the provided data

(infrastructure, applications).

• Predicting the workload of the underlying

cloud infrastructure.

• Enabling refinement process (self-

adaptation).

4.2.2 Situation Detection

Situation Detection capabilities should detect

situations that might lead to resources adaptation or

data-intensive application reconfiguration or

redeployment. Situation Detection should receive as

inputs the context of edge devices & cloud

resources, the current workload (e.g. current

throughput, volume) as well as workload predictions

(e.g. predicted throughput, volume) and should

generate as outputs the detected situation and its

associated context conditions. Novel situation

detection methods are needed that go beyond the

state of the art by enabling smart situations detection

based on event subscriptions, workload predictions

capable of coping with the dynamicity of Big Data.

Scalable, high-performance complex event

processing engines (e.g (Siddhi, 2018)) can be used

to implement the functionalities described above.

Situation Detection methods should be capable

of:

• • Detecting situations during the usage of

the Cloud Application that might indicate

the necessity for adaptation under certain

context conditions. For example, if a server

node is indicated to have high CPU usage

levels, then a situation requiring immediate

action is reflected (because the service

might start dropping requests). In the case

described above, the detected situation is

that a server machine is under severe load.

• • Triggering adaptation of the processing

topology.

• • Guiding the deployment of application

fragments.

4.2.3 Context Analysis

Efficient analysis of the status of cloud resources

including edge devices should correlate between

cloud and edge related attributes, types of jobs

running and “observed” QoS variations which

constitute the context about the current status of

cloud and edge devices. For example, context may

refer to the task execution time on an edge device or

an estimate of the remaining battery lifetime for

edge device where it is applicable. The main element

necessary for context analysis is monitoring data,

such as real-time QoS variations, cloud resource

metrics and edge device state parameters – for

example battery, location and network . This data

will be transmitted by a special agent, present on all

cloud resources and edge devices. Finally this data

should be processed (e.g using machine learning and

/or other statistical techniques) in order to identify

the context of each resource/device and generate

meaningful correlations between the monitored

variables. Tools such as (Apache Kafka, 2018) can

be used to manipulate high-volume streams of

monitoring data.

Context Analysis should:

• Collect monitoring data from cloud

resources and edge devices and infer

context.

• Relate current context to the processing

capacity of a device, e.g. expected

execution time, battery level.

4.2.4 Adaptation Recommenders

We distinguish between two broad types of

recommendations the framework should deliver:

Recommendations for adaptations of the

applications running on the cloud infrastructure and

recommendations for cloud resource adaptations.

The first type of recommenders should produce,

for example, the appropriate partitioning of cloud

applications into smaller parts that can be efficiently

deployed over cloud / edge resources. They should

also associate applications and application parts with

placement constraints and optimization preferences.

For example, they could advise that application part

1 must run on a VM with RAM > 4 GB, application

part 2 may run on any edge device, or that all parts

should be placed under the same availability zone.

Input may be the available VM flavours & edge

devices as well as the qualitative, quantitative

preferences of the DevOp or application developer

in order to formulate the optimization function.

Outputs may be a recommended partitioning of the

application along with a recommended deployment

serialized in a TOSCA-based specification (without

specific VM and edge instances). The

recommendations should take into consideration

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

197

constraints such as security constraints or other

quantitative or qualitative constraints, e.g. cost,

response time, data sanitization support etc.

The recommendations will be forwarded to the

second type of recommenders, which will then use

them to reactively (or proactively in case the

situation detected involves workload predictions)

find an optimal cloud resource reconfiguration. The

proposed reconfiguration will include the changes

on the used cloud resources, the used edge devices,

and the placement of applications or application

parts.

Inputs may be the current processing topology

and placement, the detected situations along with the

respective context of the used and the available edge

devices. Output can be the adaptation

recommendation to reconfigure the processing

topology, e.g., to introduce new processing nodes,

replicate nodes for failover purposes, remove

redundant or underused processing nodes.

Recommendations will entail how to alter the

processing topology on cloud resources, e.g., to

reconfigure additional VMs that host existing

processing nodes, spawn VMs to new physical

machines to deal with failover, start new containers

or deploy on additional hosts in a cluster. The

recommendation may be the product of any suitable

decision technique. In the example provided before,

a possible adaptation recommendation could be the

instantiation of a new server instance, and a load-

balancing service to alleviate the high CPU load.

Finally, recommendations will include shifting

processing effort to/from resources at the extreme

edge of the network, if necessary.

5 CONCLUSIONS

In this paper, we investigated challenges, research

efforts and research directions related to the

adaptation of next-generation cloud infrastructures

to support the advanced processing needs of big data

applications. We introduced the related challenges:

situation awareness, context detection, adaptivity

and the capability to predict situations adaptation.

To the best of our knowledge, there is currently no

approach that goes beyond cloud resource selection

and allocation towards recommending cloud and

edge resource adaptations based on the dynamically

changing processing needs, taking under

consideration trade-offs between cost, speed,

efficiency and reliability of adaptations. Our work

provides directions to researchers for investigating

new ways to manage the relations between Big Data

processing, cloud and edge resources adaptation

needs and adaptation strategies.

The proposed framework aims to provide a

blueprint for extending the state of the art through a

novel data analytics approaches for understanding

from the past data different modes of the workloads

and predicting which can be the next one based on

the current situation. Moreover, the framework

emphasises the need for situation-awareness

capabilities, which can be supported using event

processing technologies.

With respect to adaptation recommendations, we

plan to research and develop two recommenders that

will issue recommendations with respect to adapting

resources allocation in real-time and deploying parts

of the data-intensive applications across the

processing topology. For example, a new processing

node can be recommended based on a predefined but

dynamically changeable pool of alternatives that

should be configurable according to the context of

the case. Moreover, the processing load of a specific

node can be recommended to be offloaded to

another one that maybe closer to the edge of the

network for efficiency reasons. For developing these

recommenders, we propose methods for run-time

evaluation of adaptation actions based on situations

detected from the big-data processing and the

current usage context. To support predictive

behaviour, instead of using the same threshold-based

alerts for an entire class of cloud resources as most

monitoring systems use, we propose the

development of systems that will be monitoring

multiple data streams, understanding each

individually as well as their relationship to each

other, resulting in a highly sensitive system that can

provide early recommendations for adaptations to

optimise performance.

ACKNOWLEDGEMENTS

This work is partly funded by the European

Commission project H2020 PrestoCloud - Proactive

Cloud Resources Management at the Edge for

Efficient Real-Time Big Data Processing (732339).

REFERENCES

Amazon AWS, 2018. AWS Cloud computing services.

Available online: https://aws.amazon.com/.

Amazon AWS Autoscaling Service, 2018. Amazon Web

Services (AWS), Auto scaling. Available online:

https://aws.amazon.com/autoscaling/.

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

198

Apache Kafka, 2018. Apache Kafka, A distributed

streaming platform. Available online: https://kafka.

apache.org/

Apache Storm, 2018. Available online:

http://storm.apache.org/.

Bonab, B. A., and Bushehrian, O., 2015. A semi-

automated reverse engineering method to recommend

the best migration-to-cloud strategy. In 2015

International Symposium on Computer Science and

Software Engineering (CSSE), Aug. 2015, pp. 1–7.

Bryant, R., Tumanov, A., Irzak, O., Scannell, A., Joshi,

K., Hiltunen, M., Lagar-Cavilla, A., and de Lara, E.,

2011. Kaleidoscope: Cloud micro-elasticity via vm

state coloring. In Proceedings of the Sixth Conference

on Computer Systems, ser. EuroSys ’11, ACM, 2011,

pp. 273–286.

Burns, E., 2013. Hadoop still too slow for real-time

analysisapplications?. Available online: http://search

businessanalytics.techtarget.com/feature/Hadoop-still-

too-slow-for-real-time-analysis-applications.

Cavalcante, E., Batista, T., Lopes, F., Almeida, A., de

Moura, A. L., Rodriguez, N., Alves, G., Delicato, F.,

and Pires, P., 2013.Autonomous adaptation of cloud

applications. In Distributed Applications and Inter-

operable Systems: 13th IFIP WG 6.1 International

Conference, DAIS 2013. Springer, 2013, pp. 175–180.

CIMI, ISO/IEC JTC 1, 2013.Cloud infrastructure

management interface (cimi) model and restful http-

based protocol, ISO/IEC19831. In ISO Standards

Catalogue.

Comi, A., Fotia, L., Messina, F., Pappalardo, G., Rosaci,

D., and Sarné,G.M. L.,2015. An evolutionary

approach for cloud learning agents in multi-cloud

distributed contexts. In 2015 IEEE 24th International

Conference on Enabling Technologies: Infrastructure

for Collaborative Enterprises, Jun. 2015, pp. 99–104.

Dutta, S., Gera, S., Verma., A., and Viswanathan, B.,2012.

SmartScale: Automatic application scaling in

enterprise clouds. In 2012 IEEE Fifth International

Conference on Cloud Computing, IEEE, pp. 221–228

(2012). doi:10.1109/CLOUD.2012.12.

Fang, W., Lu, Z., Wu, J., Cao, Z., 2012. RPPS: a novel

resource prediction and provisioning scheme in cloud

data center. In 2012 IEEE Ninth International

Conference on Services Computing, IEEE, pp. 609–

616 (2012). doi:10.1109/SCC.2012.47.

Gandhi, A., Dube, P., Karve, A., Kochut, A. and Zhang,

L., 2014. Adaptive, model-driven autoscaling for

cloud applications. In 11

International Conference on

Autonomic Computing (ICAC14), USENIX, 2014,

pp.57–64.

Gholami, M. F., Daneshgar, F., Low, G., and Beydoun, G.,

2016. Cloud migration process-a survey, evaluation

framework, and open challenges. In Journal of

Systems and Software, vol. 120, no. C, pp. 31–69, Oct.

2016.

Hilton, M., Christi, A., Dig, D., Moskal, M., Burckhardt,

S., and Tillmann, N., 2014. Refactoring local to cloud

data types for mobile apps. In Proceedings of the 1st

International Conference on Mobile Software

Engineering and Systems, ser. MOBILESoft 2014,

ACM, 2014, pp. 83–92.

Hermenier, F., Lorca, X., Menaud, J.-M., Muller, G., and

Lawall, J., 2009. Entropy: A consolidation manager

for clusters. In Proceedings of the 2009 ACM

SIGPLAN/SIGOPS International Conference on

Virtual Execution Environments, ser. VEE ’09, ACM,

2009, pp. 41–50.

Inese, S. Inese, P., Solvita, B., Janis, G., Egils, M., and

Edgars, O., 2015. Decomposition of enterprise

application: A Systematic literature review and

research outlook. In Information Technology and

Management Science, vol. 18, no. 1, pp. 30–36, 2015.

Inzinger, C., Hummer, W., Satzger, B., Leitner, P., and

Dustdar, S.,2014. Generic event-based monitoring and

adaptation methodology for heterogeneous distributed

systems. In Software: Practice and Experience, vol.

44, no. 7, pp. 805–822, 2014.

Inzinger, C., Satzger, B., Leitner, P., Hummer, W., and S.

Dustdar, 2013. Model-based adaptation of cloud

computing applications. In Proceedings of the 1st

International Conference on Model-Driven Engineer-

ing and Software Development (MODELSWARD

2013), 2013, pp. 351–355.

Jamshidi, P. Pahl, C., and Mendonça, N. C., 2016. Pattern-

based multi-cloud architecture migration. In Software:

Practice and Experience, 2016.

Jennings, B. and Stadler, R, 2015. Resource management

in clouds: Survey and research challenges. In Journal

of Network and Systems Management, vol. 23, no. 3,

pp. 567–619, Jul. 2015.

Keras, 2018. Keras: The Python Deep Learning library.

Available online: https://keras.io/

Koto, A., Yamada, H., Ohmura, K., Kono, K., 2012.

Towards unobtrusive VM live migration for cloud

computing platforms. In Proceedings of the Asia-

Pacific Workshop on Systems: ACM; 2012. p. 7.

Kwon, Y.-W., and Tilevich, E.,2014. Cloud refactoring:

Automated transitioning to cloud-based services. In

Automated Software Eng., vol. 21, no. 3, pp. 345–372,

Sep. 2014.

Kyriazis, D, 2013. Cloud computing service level

agreements-exploitation of research results. In

Technical report European Commission. Available

online: http://ec.europa.eu/information_society/news

room/cf/dae/document.cfm?doc_id=2496, 2013.

Lorido-Botran, T., Miguel-Alonso, J., and Lozano, J. A.,

2014. A review of auto-scaling techniques for elastic

applications in cloud environments. In Journal of Grid

Computing, vol.12, no.4, pp.559–592, Dec.2014.

Michael, M. , Moreira, J. E., Shiloach D., and Wisniewski,

R. W.,2007. Scale-up x Scale-out: A Case Study using

Nutch/Lucene. In 2007 IEEE International Parallel

and Distributed Processing Symposium, Long Beach,

CA, 2007, pp. 1-8. doi: 10.1109/IPDPS.2007.370631.

Nguyen, H., Shen, Z., Gu, X., Subbiah, S. and Wilkes,

J.,2013. Agile: Elastic distributed resource scaling for

infrastructure-as-a-service. In Proceedings of the 10th

International Conference on Autonomic Computing

(ICAC 13), USENIX, 2013.

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

199

OASIS, 2017. Topology and orchestration specification

for cloud applications version 1.2, Committee

Specification Draft 01, OASIS Standard. Available

online: http://docs.oasis-open.org/tosca/TOSCA-Simp

le-Profile-YAML/v1.2/csd01/TOSCA-Simple-Profile-

YAML-v1.2-csd01.pdf.

OCCI, 2018. Open cloud computing interface, http://occi-

wg.org/.

Patiniotakis, I., Papageorgiou, N., Verginadis, Y.,

Apostolou, D., and Mentzas, G.,2013a. A framework

for situation-aware adaptation of service-based

applications. In Adaptive Web Services for Modular

and Reusable Software Development: Tactics and

Solution. IGI Global, 2013, pp. 253–262.

Patiniotakis, I., Papageorgiou, N., Verginadis, Y.,

Apostolou, D., and Mentzas, G., 2013b. Dynamic

event subscriptions in distributed event based

architectures. In Expert Systems with Applications ,

vol. 40, no. 6, pp. 1935–1946, May 2013.

Qu, C., Calheiros R.N., and Buyya, R., 2016. Auto-scaling

web applications in clouds: A taxonomy and survey.

In CoRR, vol. abs/1609.09224, 2016.

R, 2018. The R project for statistical computing. Available

online: https://www.r-project.org/

Ranjan, R., Benatallah, B., Dustdar, S., and Papazoglou,

M. P., 2015. Cloud resource orchestration program-

ming: Overview, issues, and directions. In IEEE

Internet Computing, vol. 19, no. 5, pp. 46–56, Sep.

2015.

Rightscale, 2018. What is auto scaling?. Available online:

http://docs.rightscale.com/faq/What_is_auto-scaling.ht

ml.

Siddhi,2018. WSO2 Siddhi. Available online: https://git

hub.com/wso2/siddhi.

TensorFlow,2018. An open source library for machine

intelligence. Available online: https://www.tensor

flow.org/

Vasconcelos, M., Mendonça, N.C., and Maia, P.H.

M.,2015. Cloud detours: A non-intrusive approach for

automatic software adaptation to the cloud. In Service

Oriented and Cloud Computing: 4th European

Conference, ESOCC 2015, Taormina, Italy, September

15-17, 2015, Proceedings. Springer, 2015, pp. 181–

195.

Youseff, L., Butrico, M. and Silva, D. D., 2008. Towards a

unified ontology of cloud computing. In Proceedings

of the IEEE Grid Computing Environments Workshop

(GCE08), 2008.

CLOSER 2018 - 8th International Conference on Cloud Computing and Services Science

200