A Cloud Platform for Classification and Resource Management of

Complex Electromagnetic Problems

Andreas Kapsalis

, Panagiotis Kasnesis

, Panagiotis C. Theofanopoulos

, Panagiotis K. Gkonis

Christos S. Lavranos

, Dimitra I. Kaklamani

, Iakovos S. Venieris

and George A. Kyriacou

Intelligent Communications and Broadband Networks Laboratory, School of Electrical and Computer

Engineering, National Technical University of Athens, Athens, Greece

Democritus University of Thrace, Department of Electrical & Computer Engineering, Xanthi, Greece

Keywords: Cloud Computing, Machine Learning, Resource Allocation, SVM, Ant Colony Optimization, Eigenanalysis,

Finite Difference.

Abstract: Most scientific applications tend to have a very resource demanding nature and the simulation of such

scientific problems often requires a prohibitive amount of time to complete. Distributed computing offers a

solution by segmenting the application into smaller processes and allocating them to a cluster of workers.

This model was widely followed by Grid Computing. However, Cloud Computing emerges as a strong

alternative by offering reliable solutions for resource demanding applications and workflows that are of

scientific nature. In this paper we propose a Cloud Platform that supports the simulation of complex

electromagnetic problems and incorporates classification (SVM) and resource allocation (Ant Colony

Optimization) methods for the effective management of these simulations.

1 INTRODUCTION

The simulation of complex electromagnetic problems

was always a task that required both great amount of

time and resources. Even with the advancement of

conventional computer technology, the execution of

complex algorithms such as Monte-Carlo simulations

-especially when dealing with realistic scenarios (e.g.

large amount of users) - creates a resource demanding

process and even if the host environment has an

adequate amount of resources to successfully execute

it, it usually takes a prohibitive amount of time for the

user to receive the results of his or her simulation. Of

course modern programming languages (even the

high level ones) nowadays provide sophisticated

libraries that allow for parallel programming

paradigms that take advantage of multicore

computers. What is more, the solution of distributed

computing allows a cluster of computers in a private

network to share the load by executing different parts

of the application code.

Parallel and distributed computing has been a

solution for complex scientific simulations. Public

grid computing platforms offer to user communities

from various scientific domains the opportunity to

submit large resource-demanding simulations that

promise to yield the desired results in an acceptable

amount of time. However, grid computing platforms

impose certain restrictions to users. That happens

mainly because simulations are submitted directly to

the physical machines for execution, and as a result

the user code must be compatible with the host

operating system. Physical machines might be in the

situation of hosting processes for different

simulations from different users. This creates a form

of a single point of failure where an error (e.g.

memory leak) might jeopardize the rest of executing

processes.

On the other hand over the last few years Cloud

Computing has been a rapidly emerging alternative,

as it can also provide a very solid solution when

dealing with scientific applications. Cloud Platforms

that utilize Cloud Infrastructures (IaaS tier) can easily

allocate a cluster of Virtual Machines for the

execution of simulations. Each Virtual Machine or

cluster of Virtual Machines is dedicated to the

execution of a specific user simulation at each given

time. Moreover, because Virtual Machines are

created by using images or snapshots of operating

systems, the user has the opportunity to request an

388

Kapsalis, A., Kasnesis, P., Theofanopoulos, P., Gkonis, P., Lavranos, C., Kaklamani, D., Venieris, I. and Kyriacou, G..

A Cloud Platform for Classiﬁcation and Resource Management of Complex Electromagnetic Problems.

In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 1: KDIR, pages 388-393

ISBN: 978-989-758-158-8

environment specifically “tailored” for his needs; he

doesn’t have to change his code in that case.

Having the aforementioned –as well as many

other significant advantages of Cloud Computing- in

mind, in this paper we propose a platform that utilizes

Cloud Infrastructures in order to provide a solution

for complex resource demanding electromagnetic

simulation problems. The platform accepts user

simulation requests, decides the amount of resources

that will be granted for the execution of the simulation

application, creates the requested Virtual Machines,

collects monitoring data and stores results in a

persistent storage space. Two crucial features of the

platform will be discussed in detail in later sections;

the Machine Learning process which automatically

figures the resource demand of a given simulation,

and the process that allocates the newly-created

Virtual Machines to hosts, which is targeted towards

performance and energy efficiency. The rest of the

paper is organized as follows; in Section 2 we present

some notable related work, and in Section 3 we

present the platform architecture. In Sections 4 and 5

we describe the Machine Learning and VM

Allocation strategies that are adopted in the platform.

In Section 6 we describe the nature of the

Electromagnetic Problems and finally in Section 7 we

provide some useful conclusions and ideas for future

work.

2 RELATED WORK

Autonomous Virtual Machine allocation is quite a

new aspect. The idea of applying Machine Learning

techniques in VM allocation over the cloud is

investigated the last five years. This approach has the

advantage of adapting the cloud services dynamically

according to the Service Layer Agreement (SLA)

between the client (user) and the provider (cloud).

M. Macias et al. (2011 and 2012) classify the

clients’ policies for SLA negotiation and allocation.

The clients classification is based on the client’s

affinity with the provider and the Quality of Service

(QoS) the client are willing to acquire. Afterwards the

provider applies Machine Learning techniques (G.

Reig, 2010) to predict future jobs and confirm if the

offered job can be executed. J.Rao et al. (2009)

propose VCONF, a RL approach to automate the VM

configuration process. VCONF is based on model-

based RL algorithms that generate policies learned

from iterations with the environment. X. Dutreilh et

al. (2011) propose a more sophisticated model free

RL approach using appropriate initialization of the

weights for the early stages and convergence

speedups applied in order to avoid the slow

convergence and optimal policy discovery in the early

phases of learning. L. Chimakurthi et al. (2011)

introduce an energy efficient mechanism capable of

allocating the cloud resources according to the given

SLA using the Ant Colony algorithm.

Another approach (A. Quiroz, 2009) uses

decentralized online clustering to categorize the

incoming tasks and optimize the provisioning of the

resources. They also present a model-based approach

to calculate the application service time given the

derived provisioning in order to tackle the inaccurate

resource requirements. A rule-based approach is

followed by M. Maurer et al. (2012) in an attempt to

reduce the SLA violations by monitoring the

workload itself. They introduce the notion of

workload volatility (WV), determine a function to

calculate it and dynamically classify workload into

WV classes (LOW, MEDIUM, MEDIUM HIGH and

HIGH WV). C.J. Huang et al. (2013) propose a

system that optimizes the resource allocation in cloud

computing. The two main components of their system

is the application service prediction module that uses

Support Vector Regression (SVR) to calculate the

response time and the resource allocation

optimization module that is based on Genetic

Algorithm (GA) and redistributes the resources

according to the current status of the installed VMs.

3 PLATFORM ARCHITECTURE

The architecture of the proposed platform is based on

a multi-agent system. For each simulation request that

is received by the platform a group of agents is

created that handles the lifecycle of the simulation

until its completion. The platform entry point is the

Supervisor service that creates the aforementioned set

of agents. There are four agents (Profiler, Planner,

Client, and Monitor) that perform a distinct set of

tasks during the simulation lifecycle. The system is

event or message driven; each agent performs a task

when it receives a message from a peer or supervising

entity. Below we describe in detail the tasks that each

entity of the platform is responsible for. Αn overview

of the platform architecture is shown in fig. 1.

3.1 Supervisor

The Supervisor service is responsible for receiving

simulation requests by the users. It performs some

checks according to the user SLA and once it accepts

the request, the Supervisor service communicates

with the underlying agent system and requests for the

A Cloud Platform for Classiﬁcation and Resource Management of Complex Electromagnetic Problems

389

creation of four agents that will be dedicated to carry

out the execution of the user simulation.

3.2 Profiler

The Profiler is responsible on making decisions based

on historical data and user SLAs about the amount of

resource that are to be allocated into a VM or VMs.

The Profiler incorporates the SVM (Support Vector

Machine) supervised machine learning method. The

algorithm predicts the appropriate amount of

resources that are necessary by the simulation in order

to be executed in the required amount of time

according to the user SLA and the infrastructure

characteristics.

3.3 Planner

Another integral part of the platform is the agent that

receives the decision output from the Profiles and is

responsible for creating an allocation plan for the

resources. For this the Planner agent uses an Ant

Colony Optimization algorithm variant that is

targeted towards energy efficiency. The ACO variant

is described in detail in Section 5.

3.4 Client

The Client agent receives the allocation plan and its

sole responsibility is to communicate with the service

stack of the IaaS (Infrastructure as a Service) in order

to create or destroy the requested VMs into the pre-

selected virtual hosts.

3.5 Monitor

Once the requested resources are created and are

accessible, the Monitor agent performs periodic

checks on the VMs that host the user’s simulation.

The Monitor collects data regarding the resource

utilization of each VM as well as the resource usage

of the simulation process of each VM.

3.6 Datastore

The datastore may not be a part of the agent system,

but it is a highly important part of the platform as it

holds monitoring data that are used for profiling of

user simulations. The datastore uses a NoSQL

document database. Due to the unstructured and

loosely coupled form of stored data NoSQL databases

provide better write/read times especially when

clients query for large chunks of data.

3.7 User Dashboard

The user dashboard is a Web UI that allows the user

initiate new simulations as well as review his or hers

on-going or completed ones. It displays status

notifications on screen in real time fashion and

provides also information logs regarding the

simulations that have complete successfully or

unsuccessfully. The user can also download the

results of the simulation into his computer.

Figure 1: Overview of the Platform Architecture.

4 PROFILER

In our approach we attempt to categorize the

aforementioned computational tasks (Section 6)

requested by the users into classes. Each

computational task will be mapped to the generation

of a finite number of VMs with the proper attributes

(CPU and Memory) to handle the user’s request. As a

result we propose a supervised machine learning

classification approach to achieve the autonomous

VM allocation in a cloud computing architecture. The

most appropriate method for supervised classification

for this is the Support Vector Machine (SVM) using

the Gaussian Kernel. For a number of training

examples, the regularized SVM cost function

(







) of a data set =



,



,  ∈ is

given by:









=







log











+1−







log

















+





∑









(1)









=















 (2)

where  denotes the number of features, denotes the

weight of a feature, 









depicts the hypothesis

value that equals the sigmoid (logistic) function and

 depicts the regularization term. A huge advantage

KDIR 2015 - 7th International Conference on Knowledge Discovery and Information Retrieval

390

of SVM is that it finds a hypothesis h that assures the

lowest true error, Structural Risk Minimization

principle (V. N. Vapnik, 1995). The Quality of

Experience (QoE) is a major issue for the users and

SVM provides a more “safe” distance (fig. 2) from

the decision boundary for each example, which

reduces the classification errors that other classifiers

with “soft” margin produce. What is more, the

Gaussian Kernel the ability to generate non-linear

decision boundaries (fig. 3) using linear classification

methods and Gaussian kernel function enables the

classification of data that have no obvious fixed-

dimensional vector space representation (A. Ben-

Hur, 2010). The Gaussian Kernel function on two

samples (x, x’) is given by:





,





=−

‖



‖







 (3)

where

‖

−′

‖



denotes the squared Euclidean

distance between two feature vectors and 



depicts

the variance.

a) b)

Figure 2: a) Margin Classifier b) Non-linear decision

boundary.

5 PLANNER

The proposed platform uses the output of the Machine

Learning Classification process and performs the next

crucial task which is the resource allocation (in the

form of VMs) into physical hosts. The goal of the

Planner agent is to pack VMs that are created for a

specific simulation into the same host. That is

profitable for two reasons; the first is that the

simulation will experience lower network overhead

especially when popular distributed computing

frameworks (e.g. MPI, MapReduce) use ssh as their

main communication protocol. The second reason is

that the allocation should be also driven towards

energy efficiency regarding the physical

infrastructure, as inactive hosts can be put into energy

saving mode. Given the fact that the allocation

problem is of NP-Hard nature, we have chosen to use

the meta-heuristic Ant-Colony Optimization

algorithm (ACO). The ACO algorithm models a

multi-agent system that imitates the behavior of real

life ant colonies when searching for food sources. It

was originally developed by Marco Dorigo et al.

(1999). The ACO algorithm shows better results

towards workload performance and energy efficiency

than other greedy algorithms such as the First-Fit

Decreasing (FFD) (Feller et al., 2011). Our approach

regarding the implementation of the algorithm takes

the following assumptions into account; a) the

resource demand by the Virtual Machines is static and

b) the resource vector contains the CPU cores and ram

in GB. A full formalization and description of the

ACO algorithm is provided in the work by Feller et

al. (2011).

In the proposed variant let  denote the set of

physical hosts with size, and  the set of VMs with

size  that the algorithm will try to allocate into

hosts. Also, let 







denote the resource capacity vector

(2-dimensional in our case) of a physical host, ∈

. Similarly, let 







denote the resource demand vector

of a VM, ∈ . As mentioned earlier this ACO

variant is targeted towards energy efficiency by trying

to pack as many VMs to the same physical host. Thus,

the objective function the algorithm tries to minimize

is given by the following:









=







(4)

Where 



is equal 1 if a physical host has

allocated VMs, and 0 otherwise. The objective

function is subject to the following constraints; a)

each VM  is allocated to exactly one physical host

and b) the maximum resource capacity for each of the

physical hosts is not exceeded. An integral

characteristic of the ACO algorithm is the way each

ant-agent is making decisions on which physical host

will allocate a VM into. The probability to choose the

, pair is a combination of the pheromone trail,

which is a mechanism for encouraging other ants to

follow the same path, and a heuristic information. The

probability is given by the following:



,



,





×

,





∑



,





×

,





∈



 (5)

Where 

,

is the amount of pheromone trail for

the specific VM - physical host pair, 

,

is the

heuristic information for that same pair and 



is the

set of all VMs that are not assigned into any host and

ensure that the resource capacity constraint of the

physical host is not violated when taking also into

account the current load assigned to it. The above can

be show in eq. 6:





≔

(6)

A Cloud Platform for Classiﬁcation and Resource Management of Complex Electromagnetic Problems

391

.



,





(7)









+







≤







(8)

Where 







denotes the current resource load for

physical host . Another characteristic that is worth

mentioning is the pheromone evaporation mechanism

which is triggered after the end of each iteration of the

algorithm and is given by the following:



,

≔



1−



×

,

+∆



,



(9)

Where  is the pheromone evaporation rate and

∆



,



is a bonus to the best VM – physical host pair

of each iteration.

6 ELECTROMAGNETIC

PROBLEMS

6.1 Principal Component Analysis in

MIMO-WCDMA Networks

The first electromagnetic problem performs the

calculation of the appropriate transmission vectors in

MIMO-WCDMA networks that improve Signal to

Noise Ratio (SNR), where diversity combining

transmission mode is assumed. In order to reduce

overall complexity, Principal Component Analysis

(PCA) is employed at the reception, and only the

terms that contribute to a predefined signal energy are

taken into account. Even so, however, in cases of

multiuser/multipath scenarios, computational load

can be significantly increased.

In particular, when deploying PCA in MIMO-

WCDMA networks, it is assumed that all received

data for a specific user after processing can be stacked

as an × matrix (denoted as  throughout the rest

of this section), where  is the number of

independent observations and S the number of

samples per observation (Shlens, 2005). In our

case, = where is the number of multipath

components. The covariance matrix of  will be

given by:











(10)

and the primary eigenvalues and eigenvectors can be

directly calculated from eigenvalue analysis of matrix





. Afterwards, an iterative optimization approach is

followed, and all transmit weight vectors can be

calculated (Gkonis et al., 2015). Note however that as

the number of active users increases, eigenvalue

analysis should be performed at each user separately.

Moreover, the dimensions of matrix  are directly

related to the number of active multipath components,

while processing complexity also depends on the

number of transmit and receive antennas.

6.2 Eigenanalysis based on a 2-D FDFD

Method

The second case of electromagnetic problems is the

eigenanalysis of open-periodic electromagnetic

structures. The eigenanalysis of electromagnetic

structures is used to enlighten all the hidden physical

properties exhibited by each structure. The following

eigenanalysis is exploiting a 2-D Curvilinear FDFD

method, (Lavranos et al., 2009, Theofanopoulos et al.

2014 and Lavranos et al., 2014).

The eigenanalysis starts from Maxwell’s curl

equations in the frequency domain and after some

algebraic manipulations the following linear eigen



problems occur:





























=















(11)

































=















(12)

The eigenproblem given in eq. 11 is called ‘β-

formulation’ and the independent variable is the

eigenfrequency ω, while the eigenvalue is the complex

propagation constant β. On the other hand, the

eigenproblem of eq. 12 is called ‘ω-formulation’ and

the independent variable is the propagation constant β

and the eigenvalue is the eigenfrequency ω. So, in

order to reveal all the modes exhibited by the

structure, an independent eigenproblem has to be

solved for every ω or β in a given range. The size of

the eigenproblems depends on the level of meshing

detail.

The nature of the eigenproblems reveals the

urgency towards a form of parallelization or

distributed execution. The proposed parallelization

technique relies on the independent nature of each

eigenproblem. So, exploiting the independence of the

eigenproblems, an assignment to different machines is

expected. The estimated analysis time is reduced

depending on the number of the machines-workers

available.

7 CONCLUSIONS AND FUTURE

WORK

In this paper we described a platform that takes

KDIR 2015 - 7th International Conference on Knowledge Discovery and Information Retrieval

392

advantage of Cloud Computing Infrastructures in

order to apply classification and resource

management methods for complex and resource

heavy electromagnetic simulations. The proposed

platform requires no special configuration from the

user regarding his or her application code. Also,

depending on the selected simulation the platform can

predict the required resource demand in order to

allow it to complete in the desired user time,

according of course the SLA. Also, the platform uses

an energy-efficient ACO variant for the allocation of

the VMs into physical hosts that can also achieve low

network overhead.

A series of next steps include the collection of test

results against the proposed platform and the

simulation of the described electromagnetic problems

in various set ups. Furthermore, we would like to

address the issue of dynamically reconfiguring the

allocation plan according to the continuous changing

resource demand of the electromagnetic simulations.

Finally, we intend also to test a number of allocation

algorithms and machine learning methods against the

ones that are already being used in our platform.

ACKNOWLEDGEMENTS

This research has been co-financed by the European

Union (European Social Fund) and Greek national

funds through the Operational Program "Education

and Lifelong Learning" of the National Strategic

Reference Framework (NSRF) - Research Funding

Program: THALES. Investing in knowledge society

through the European Social Fund.

REFERENCES

Shlens, J., (2005, December). A tutorial on principal

component analysis.

Gkonis, P., K., Kapsalis, A., Zekios, K., Kaklamani, D., I.,

Venieris, I., S., Chrysomallis, M., Kyriakou, G., (2015,

April). On the Performance of Spatial Multiplexing in

MIMO-WCDMA Networks with Principal Component

Analysis at the Reception. CD-ROM in Procs. EuCAP

2015, Lisbon, Portugal.

Lavranos, C., S., Kyriacou, G., A., (2009, March).

Eigenvalue analysis of curved waveguides employing

an orthogonal curvilinear frequency domain finite

difference method, IEEE Microwave Theory and

Techniques.

Theofanopoulos, P., C., Lavranos, C., S., Sahalos, J., N. and

Kyriacou, G., A., (2014, April). Backward Wave

Eigenanalysis of a Tuneable Two-Dimensional Array

of Wires Covered with Magnetized Ferrite in Proc.

EuCAP 2014, The Hague, The Netherlands.

Lavranos, C., S., Theofanopoulos, P., C., Vafiades, E.,

Sahalos, J., N. and Kyriacou, G., A., (2014, November).

Eigenanalysis of Tuneable Two-Dimensional Array of

Wires or Strips Embedded in Magnetized Ferrite, in the

Proc. LAPC 2014, Loughborough, UK.

Dorigo, M., Di Caro, G., & Gambardella, L. (1999, April).

Ant Algorithms for discrete optimization. Artificial

Life, 137-172.

Feller, E., Rilling, L., & Morin, C. (2011). Energy-Aware

Ant Colony Based Workload Placement in Clouds.

[Research Report] RR-7622.

Macias, M., Guitart, J., (2012). Client Classification

Policies for SLA Enforcement in Shared Cloud

Datacenters. Proceedings of the 2012 12th IEEE/ACM

International Symposium on Cluster, Cloud and Grid

Computing.

Macias, M., Guitart, J., (2011, December). Client

Classification Policies for SLA Negotiation and

Allocation in Shared Cloud Datacenters, in Proc.

GECON 2011.

Reig, G., Alonso, J., and Guitart, J., (2010, July). Prediction

of job resource requirements for deadline schedulers to

manage high-level SLAs on the cloud, in 9th IEEE Intl.

Symp. on Network Computing and Applications,

Cambridge, MA, USA, 162–167.

Rao, J., Bu, X., Xu, C., Wang, L., Yin, G., (2009). VCONF:

A Reinforcement Learning Approach to Virtual

Machines Auto-configuration, in Proc. ICAC '09.

Dutreilh, X., Kirgizov, S., Melekhova, O., Malenfant, J.,

Rivierre, N., and Truck, I., (2011). Using

Reinforcement Learning for Autonomic Resource

Allocation in Clouds: Towards a Fully Automated

Workflow, Seventh International Conference on

Autonomic and Autonomous Systems.

Chimakurthi, L., and Kumar S D, M., (2011). Power

Efficient Resource Allocation for Clouds Using Ant

Colony Framework, International Conference on

Computer, Communication and Electrical Technology.

Quiroz, A., Kim, H., Parashar, M., Gnanasambandam, N.,

and Sharma, N., (2009). Towards Autonomic Workload

Provisioning for Enterprise Grids and Clouds, Proc.

IEEE/ACM 10th Int',l Conf. Grid Computing, 50-57.

Maurer, M., Brandic, I., and Sakellariou, R., (2012). Self-

adaptive and resource efficient SLA enactment for

cloud computing infrastructures, 5th International

Conference on Cloud Computing (CLOUD).

Huang, C., Wang, Y., Guan, C., Chen, H., and Jian, J.,

(2013). Applications of Machine Learning to Resource

Management in Cloud Computing, International

Journal of Modeling and Optimization, 2, 148-152.

Vapnik, V., N., (1995). The Nature of Statistical Learning

Theory. Springer, New York.

Ben-Hur, A., and Weston, J., (2010). A User’s Guide to

Support Vector Machines, Data Mining Techniques for

the Life Sciences, 609, 223-239.

A Cloud Platform for Classiﬁcation and Resource Management of Complex Electromagnetic Problems

393