This document provides information about the Distributed Computing Environments Team at AGH University of Science and Technology in Krakow, Poland. It describes the team's research focus areas including distributed computing infrastructures, cloud computing, resource management, billing models, and security. It also lists some current research topics and objectives such as optimization of service deployment on clouds, billing and accounting models, and cloud security. Finally, it provides examples of past research projects conducted by the team on topics like cost optimization of applications on clouds, resource allocation management systems, and data reliability in cloud infrastructures.

1. Distributed Computing Environments Team
Marian Bubak
bubak@agh.edu.pl
Department of Computer Science and Cyfronet
AGH University of Science and Technology
Krakow, Poland
dice.cyfronet.pl

2. DICE Team
Academic Computer Centre
CYFRONET AGH (1973)
120 employees
http://www.cyfronet.pl/en/
Department of Computer Science AGH (1980)
800 students, 70 employees
http://www.ki.agh.edu.pl/uk/index.htm
Faculty of Computer Science, Electronics and
Telecommunication (2012)
2000 students, 200 employees
http://www.iet.agh.edu.pl/
AGH University of Science and Technology (1919)
16 faculties, 36000 students; 4000 employees
http://www.agh.edu.pl/en
Other 15
faculties
Distributed Computing
Environments (DICE) Team
http://dice.cyfronet.pl
• Investigation of methods for building complex scientific collaborative applications
• Elaboration of environments and tools for e-Science
• Integration of large-scale distributed computing infrastructures
• Knowledge-based approach to services, components, and their semantic composition

3. • Investigating applicability of cloud computing model for complex
scientific applications
• Optimization of resource allocation for applications on clouds
• Resource management for services on heterogeneous resources
• Urgent computing scenarios on distributed infrastructures
• Billing and accounting models
• Procedural and technical aspects of ensuring efficient yet secure
data storage, transfer and processing
• Methods for component dependency management, composition
and deployment
• Information representation model for cloud federating platform, its
components and operating procedures
Current research objectives

4. • Optimization of service
deployment on clouds
– Constraint satisfaction and
optimization of multiple
criteria (cost, performance)
– Static deployment planning
and dynamic auto-scaling
• Billing and accounting
model
– Adapted for the federated
cloud infrastructure
– Handle multiple billing
models
• Supporting system-level
(e)Science
– tools for effective scientific
research and collaboration
– advanced scientific analyses
using HPC/HTC resources
• Cloud security
– security of data transfer
– reliable storage and removal
of the data
• Cross-cloud service
deployment based on
container model
Topics for collaboration

5. seconds
~95%
3 hours
100 jobs
1 job
<10%
asynchronous and frequent failures
and hardware/software upgrades
long and unpredictable job waiting times
J. T. Moscicki: Understanding and mastering dynamics in Computing Grids, UvA PhD thesis, promoter: M. Bubak, co-promoter: P. Sloot;
12.04.2011
Spatial and temporal dynamics in grids
• Grids increase research capabilities for science
• Large-scale federation of computing and storage resources
– 300 sites, 60 countries, 200 Virtual Organizations
– 10^5 CPUs, 20 PB data storage, 10^5 jobs daily
• However operational and runtime dynamics have a negative
impact on reliability and efficiency

6. Completion time
with late binding.
Completion time
with early binding.
40 hours1.5 hours
J. T. Moscicki, M. Lamanna, M. Bubak, P. M. A.Sloot: Processing moldable tasks on the Grid: late job binding with lightweight user-level
overlay, FGCS 27(6) pp 725-736, 2011
User-level overlay with late binding scheduling
• Improved job execution characteristics
• HTC-HPC Interoperability
• Heuristic resource selection
• Application aware task scheduling

7. IaaS Provider
EEA
Zoning
jClouds
API
Support
BLOB
storage
support
Per-
hour
instance
billing
API
Access
Published
price
VM
Image
Import /
Export
Relational
DB
support Score
Weight 20 20 10 5 5 5 3 2
1 Amazon AWS 1 1 1 1 1 1 0 1 27
2 Rackspace 1 1 1 1 1 1 0 1 27
3 SoftLayer 1 1 1 1 1 1 0 0 25
4 CloudSigma 1 1 0 1 1 1 1 0 18
5 ElasticHosts 1 1 0 1 1 1 1 0 18
6 Serverlove 1 1 0 1 1 1 1 0 18
7 GoGrid 1 1 0 1 1 1 0 0 15
8 Terremark ecloud 1 1 0 1 1 0 1 0 13
9 RimuHosting 1 1 0 0 1 1 0 1 12
10 Stratogen 1 1 0 0 1 0 1 0 8
11 Bluelock 1 1 0 0 1 0 0 0 5
12 Fujitsu GCP 1 1 0 0 1 0 0 0 5
13 BitRefinery 0 0 0 0 0 1 0 1 0
14 BrightBox 1 0 0 1 1 1 1 0 0
15 BT Global Services 1 0 0 0 1 0 1 0 0
16 Carpathia Hosting 1 0 0 0 0 0 1 0 0
17 City Cloud 1 0 0 1 1 1 0 0 0
18 Claris Networks 0 0 0 1 0 0 0 0 0
19 Codero 0 0 0 1 1 1 0 0 0
20 CSC 1 0 0 0 0 0 1 0 0
21 Datapipe 1 0 0 1 1 0 0 0 0
22 e24cloud 1 0 0 1 0 1 0 0 0
23 eApps 0 0 0 0 0 1 0 0 0
24 FlexiScale 1 0 0 1 1 1 1 0 0
25 Google GCE 1 0 1 1 1 1 0 1 0
26 Green House Data 0 0 0 0 1 0 1 0 0
27 Hosting.com 0 0 0 0 0 1 1 1 0
28 HP Cloud 0 1 1 1 1 1 1 1 0
29 IBM SmartCloud 0 0 1 1 1 1 0 1 0
30 IIJ GIO 0 0 0 0 0 0 0 0 0
31 iland cloud 1 0 0 1 0 1 1 0 0
32 Internap 0 0 1 1 1 1 0 0 0
33 Joyent 0 0 0 1 1 1 0 0 0
34 LunaCloud 1 0 1 1 1 1 0 0 0
35 Oktawave 1 0 1 1 1 1 0 1 0
36 Openhosting.co.uk 1 0 0 0 0 1 0 0 0
37 Openhosting.com 0 1 0 1 1 1 1 0 0
38 OpSource 1 0 1 1 1 1 1 0 0
39 ProfitBricks 1 0 0 1 1 1 0 0 0
40 Qube 1 0 0 0 0 1 0 0 0
41 ReliaCloud 0 0 0 0 0 0 0 0 0
42 SaavisDirect 0 0 1 1 0 1 0 0 0
43 SkaliCloud 0 1 0 1 1 1 1 0 0
44 Teklinks 0 0 0 0 0 0 0 0 0
45 Terremark vcloud 0 1 0 1 1 1 1 0 0
46 Tier 3 0 0 0 0 1 0 0 0 0
47 Umbee 1 0 0 1 1 1 1 0 0
48 VPS.net 1 0 0 0 1 1 0 0 0
49 Windows Azure 1 0 1 1 1 1 0 1 0
• Performance of VM deployment times
• Virtualization overhead Evaluation of open source cloud
stacks (Eucalyptus, OpenNebula, OpenStack)
• Survey of European public cloud providers
• Performance evaluation of top cloud providers
(EC2, RackSpace, SoftLayer)
• A grant from Amazon has been obtained
M. Bubak, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski and S. Varma: Evaluation of Cloud Providers for VPH Applications, poster
at CCGrid2013 - 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Delft, the Netherlands, May 13-16, 2013
Cloud performance evaluation

8. • Infrastructure model
– Multiple compute and
storage clouds
– Heterogeneous instance
types
• Application model
– Bag of tasks
– Leyered workflows
• Modeling with AMPL (A
Modeling Language for
Mathematical
Programming)
• Cost optimization under
deadline constraints
• Mixed integer
programming
• Bonmin, Cplex solvers
0
500
1000
1500
2000
2500
3000
0 10 20 30 40 50 60 70 80 90 100
Cost($)
Time limit (hours)
20000 tasks, 512 MiB input and 512 MiB output, task execution time 0.1h @ 1ccu machine
Rackspace instances
Rackspace and private instances
Amazon's and private instances
Multiple providers
Amazon S3
Rackspace Cloud Files
Optimal
Layer 1 A
Layer 2
B
B B C
Layer 3 D
Layer 4 E
Layer 5 F
1h
2.5 h
0.5 h
0.3 h
2 h
6 h
M. Malawski, K. Figiela, J. Nabrzyski: Cost minimization for computational applications on hybrid cloud infrastructures, Future Generation
Computer Systems, Volume 29, Issue 7, September 2013, Pages 1786-1794, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2013.01.004
Private cloud
Compute
private
Amazon
Storage
Compute
m1.small m1.large
t1.micro m2.xlarge
Task
Input
Output
Application
Rackspace
Storage
Compute
rs.1gb rs.2gb
rs.4gb rs.16gb
Cost optimization of applications on clouds

9. VPH-Share Master Int.
AdminDeveloper Scientist
Development Mode
VPH-Share Core Services Host
OpenStack/Nova Computational Cloud Site
Worker
Node
Worker
Node
Worker
Node
Worker
Node
Worker
Node
Worker
Node
Worker
Node
Worker
Node
Head
Node
Image store
(Glance)
Cloud Facade
(secure
RESTful API )
Other CS
Amazon EC2
Atmosphere
Management
Service (AMS)
Cloud stack
plugins (Fog)
Atmosphere
Internal
Registry (AIR)
Cloud Manager
Generic Invoker
Workflow management
External application
Cloud Facade client
Customized applications may directly
interface Atmosphere via its RESTful
API called the Cloud Facade
The Atmosphere Cloud Platform is a one-stop management service for
hybrid cloud resources, ensuring optimal deployment of application
services on the underlying hardware.
P. Nowakowski, T. Bartynski, T. Gubala, D. Harezlak, M. Kasztelnik, M. Malawski, J. Meizner, M. Bubak: Cloud Platform for Medical
Applications, eScience 2012 (2012)
Resource allocation management

10. DRI is a tool which can keeps track of binary data stored in a cloud infrastructure, monitor
data availability and faciliate optimal deployment of application services in a hybrid cloud
(bringing computations to data or the other way around).
Binary
data
registry
LOBCDER
Amazon S3 OpenStack Swift Cumulus
Register files
Get metadata
Migrate LOBs
Get usage stats
(etc.)
Distributed Cloud storage
Store and marshal data
End-user features
(browsing, querying,
direct access to data,
checksumming)
VPH Master Int.
Data management
portlet (with DRI
management
extensions)
DRI Service
A standalone application service, capable of autonomous operation. It periodically
verifies access to any datasets submitted for validation and is capable of issuing alerts
to dataset owners and system administrators in case of irregularities.Validation
policy
Configurable validation runtime
(registry-driven)
Runtime layer
Extensible
resource
client layer
Metadata extensions for DRI
Data reliability and integrity

11. Data security in clouds
Jan Meizner, Marian Bubak, Maciej Malawski, and Piotr Nowakowski: Secure storage and processing of confidential data on public clouds.
In: Proceedings of the International Conference On Parallel Processing and Applied Mathematics (PPAM) 2013
• To ensure security of data in transit
• Modern applications use secure tranport
protocols (e.g.TLS)
• For legacy unencrypted protocols if absolutly
needed, or as additional security measure:
– Site-to-Site VPN, e.g. between cloud sites is
outside of the instance, might use
– Remote access – for individual users accessing
e.g. from their laptops
• Data should be secure stored and realiable
deleted when no longer needed
• Clouds not secure enough, data optimisations
preventing ensuring that data were deleted
• A solution:
– end-to-end encryption (decryption key stays in
protected/private zone)
– data dispersal (portion of data, dispersed
between nodes so it’s non-trivial/impossible to
recover whole message)

12. • GworkflowDL language (with A.
Hoheisel)
• Dynamic, ad-hoc refinement of
workflows based on semantic
description in ontologies
• Novelty
– Abstract, functional blocks translated
automatically into computation unit
candidates (services)
– Expansion of a single block into a
subworkflow with proper concurrency
and parallelism constructs (based on
Petri Nets)
– Runtime refinement: unknown or failed
branches are re-constructed with
different computation unit candidates
T. Gubala, D. Harezlak, M. Bubak, M. Malawski: Semantic Composition of Scientific Workflows Based on the Petri Nets Formalism. In: "The
2nd IEEE International Conference on e-Science and Grid Computing", IEEE Computer Society
Press, http://doi.ieeecomputersociety.org/10.1109/E-SCIENCE.2006.127, 2006
Semantic workflow composition

13. • Design of a laboratory for virologists, epidemiologists and clinicians
investigating the HIV virus and the possibilities of treating HIV-positive
patients
• Based on notion of in-silico experiments built and refined by cooperating
teams of programmers, scientists and clinicians
• Novelty
– Employed full concept-prototype-
refinement-production circle for
virology tools
– Set of dedicated yet interoperable
tools bind together programmers
and scientists for a single task
– Support for system-level science
with concept of result reuse
between different experiments
T. Gubala, M. Bubak, P. M. A. Sloot: Semantic Integration of Collaborative Research Environments, chapter XXVI in “Handbook of Research
on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare”, Information Science Reference IGI Global 2009, ISBN:
978-1-60566-374-6, pages 514-530
Cooperative virtual laboratory for e-Science

14. T. Gubala, K. Prymula, P. Nowakowski, M. Bubak: Semantic Integration for Model-based Life Science Applications. In: SIMULTECH 2013
Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Reykjavik, Iceland
29 - 31 July, 2013, pp. 74-81
• Concept of describing scientific domains for in-silico
experimentation and collaboration within laboratories
• Based on separation of the domain model, containing
concepts of the subject of experimentation from the
integration model, regarding the method of (virtual)
experimentation (tools, processes, computations)
• Facets defined in integration model are automatically
mixed-in concepts from domain model: any piece of
data may show any desired behavior
• Proposed, designed and deployed the
method for 3 domains of science:
– Computational chemistry inside InSilicoLab
chemistry portal
– Sensor processing for early warning and crisis
simulation in UrbanFlood EWS
– Processing of results of massive bioinformatic
computations for protein folding method
comparison
– Composition and execution of multiscale
simulations
– Setup and management of VPH applications
Semantic integration for science domains

15. GridSpace - platform for e-Science applications
• Experiment: an e-science application
composed of code fragments
(snippets), expressed in either general-
purpose scripting programming
languages, domain-specific languages or
purpose-specific notations. Each snippet
is evaluated by a corresponding
interpreter.
• GridSpace2 Experiment Workbench: a
web application - an entry point to
GridSpace2. It facilitates exploratory
development, execution and
management of e-science experiments.
• Embedded Experiment: a published
experiment embedded in a web site.
• GridSpace2 Core: a Java library providing
an API for
development, storage, management and
execution of experiments. Records all
available interpreters and their
installations on the underlying
computational resources.
• Computational Resources:
servers, clusters, grids, clouds and e-
infrastructures where the experiments
are computed.
E. Ciepiela, D. Harezlak, J. Kocot, T. Bartynski, M. Kasztelnik, P. Nowakowski, T. Gubała, M. Malawski, M. Bubak: Exploratory Programming
in the Virtual Laboratory. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 621-
628, October 2010, the best paper award.

16. Goal:
Extending the traditional
scientific publishing model with
computational access and
interactivity mechanisms;
enabling readers (including
reviewers) to replicate and
verify experimentation results
and browse large-scale result
spaces.
Challenges:
Scientific: A common description schema for primary data (experimental data, algorithms, software,
workflows, scripts) as part of publications; deployment mechanisms for on-demand reenactment of
experiments in e-Science.
Technological: An integrated architecture for storing, annotating, publishing, referencing and reusing
primary data sources.
Organizational: Provisioning of executable paper services to a large community of users representing
various branches of computational science; fostering further uptake through involvement of major
players in the field of scientific publishing.
P. Nowakowski, E. Ciepiela, D. Harężlak, J. Kocot, M. Kasztelnik, T. Bartyński, J. Meizner, G. Dyk, M. Malawski: The Collage Authoring
Environment. In: Proceedings of the International Conference on Computational Science, ICCS 2011 (2011), Winner of the Elseview/ICCS
Executable Paper Grand Challenge
E. Ciepiela, D. Harężlak, M. Kasztelnik, J. Meizner, G. Dyk, P. Nowakowski, M. Bubak: The Collage Authoring Environment: From Proof-of-
Concept Prototype to Pilot Service in Procedia Computer Science, vol. 18, 2013
Collage - executable e-Science publications

17. 17
Jun 2012
• Goal: Extend the traditional way of authoring and
publishing scientific methods with computational
access and interactivity mechanisms thus bringing
reproducibility to scientific computational
workflows and publications
• Scientific challenge: Conceive a model and
methodology to embrace reproducibility in
scientific worflows and publications
• Technological challenge: support these by modern
Internet technologies and available computing
infrastructures
• Solution proposed:
• GridSpace2 – web-oriented distributed
computing platform
• Collage – authoring environment for
executable publications Dec 2011
Jun 2011
GridSpace2 / Collage - Executable
e-Science Publications

18. Results:
• GridSpace2/Collage won Executable
Paper Grand Challenge in 2011
• Collage was integrated with Elsevier
ScienceDirect portal so papers can be
linked and presented with
corresponding computational
experiments
• Special Issue of Computers &
Graphics journal featuring Collage-
based executable papers was
released in May 2013
• GridSpace2/Collage has been applied
to multiple computational workflows
in the scope of PL-Grid, PL-Grid Plus
and Mapper projects
E. Ciepiela, P. Nowakowski, J. Kocot, D. Harężlak, T. Gubała, J. Meizner, M. Kasztelnik, T. Bartyński, M. Malawski, M. Bubak: Managing
entire lifecycles of e-science applications in the GridSpace2 virtual laboratory–from motivation through idea to operable web-accessible
environment built on top of PL-grid e-infrastructure. In: Building a National Distributed e-Infrastructure–PL-Grid, 2012
P. Nowakowski, E. Ciepiela, D. Harężlak, J. Kocot, M. Kasztelnik, T. Bartyński, J. Meizner, G. Dyk, M. Malawski: The Collage Authoring
Environment. In: Procedia Computer Science, vol. 4, 2011
GridSpace2 / Collage - Executable e-Science
Publications
E. Ciepiela, D. Harężlak, M. Kasztelnik, J. Meizner, G. Dyk, P.
Nowakowski, M. Bubak: The Collage Authoring Environment:
From Proof-of-Concept Prototype to Pilot Service. In: Procedia
Computer Science, vol. 18, 2013

19. Common Information Space (CIS)
• Facilitate creation, deployment and robust operation of Early Warning
Systems in virtualized cloud environment
• Early Warning System (EWS): any system
working according to four steps:
monitoring, analysis, judgment,
action (e.g. environmental
monitoring)
B. Balis, M. Kasztelnik, M. Bubak, T. Bartynski, T. Gubala, P. Nowakowski, J. Broekhuijsen: The UrbanFlood Common Information Space for
Early Warning Systems. In: Elsevier Procedia Computer Science, vol 4, pp 96-105, ICCS 2011.
Common Information Space
• connects distributed component
into EWS and deploy it on cloud
• optimizes resource usage taking into
acount EWS importance level
• provides EWS and self monitoring
• equipped with autohealing

20. • Simple yet expressive model for complex scientific apps
• App = set of processes performing well-defined functions and
exchanging signals HyperFlow model JSON serialization
{
"name": "...",  name of the app
"processes": [ ... ],  processes of the app
"functions": [ ... ],  functions used by processes
"signals": [ ... ],  exchanged signals info
"ins": [ ... ],  inputs of the app
"outs": [ ... ]  outputs of the app
}
• Supports a rich set
of workflow patterns
• Suitable for various
application classes
• Abstracts from other distributed app aspects (service model,
data exchange model, communication protocols, etc.)
HyperFlow: model & execution engine

21. • HyperFlow model
& engine for
distributed apps
• App optimization
& scheduling
• Autoscaling and
dynamic app
reconfiguration
• Multi-cloud
resource
provisioning
Execution Platform Provisioning platform
VM
VM
VM
Cloud
VM VM
Executor
Input data
Trigger app execution
Monitoring
Provisioner
Start/Stop/Reconfigure VM
Autoscaler
Optimizer & Scheduler
Reconfigure app
Scaling rules
measuremants
HyperFlow
Enactment Engine
Enact
Execute
App model
App state
Composite App
Initial
deployment
Platform for distributed applications

22. Objectives
• Provide means for ad-hoc metadata model
creation and deployment of corresponding
storage facilities
• Create a research space for metadata model
exchange and discovery with associated data
repositories with access restrictions in place
• Support different types of storage sites and
data transfer protocols
• Support the exploratory paradigm by making
the models evolve together with data
Architecture
• Web Interface is used by users to
create, extend and discover metadata models
• Model repositories are deployed in the PaaS
Cloud layer for scalable and reliable access
from computing nodes through REST
interfaces
• Data items from Storage Sites are linked from
the model repositories
Colaborative metadata management

23. • MAPPER Memory (MaMe) a semantics-
aware persistence store to record metadata
about models and scales
• Multiscale Application Designer (MAD)
visual composition tool transforming high level
description into executable experiment
• GridSpace Experiment Workbench
(GridSpace) execution and result
management of experiments
choose/add/delete
Mapper A
Mapper B
Submodule
A
Submodule
B
MADGridSpace
MaMe
K. Rycerz, E. Ciepiela, G. Dyk, D. Groen, T. Gubala, D. Harezlak, M. Pawlik, J. Suter, S. Zasada, P. Coveney, M. Bubak: Support for Multiscale
Simulations with Molecular Dynamics, Procedia Computer Science, Volume 18, 2013, pp. 1116-1125, ISSN 1877-0509
K. Rycerz, M. Bubak, E. Ciepiela, D. Harezlak, T. Gubala, J. Meizner, M. Pawlik, B.Wilk: Composing, Execution and Sharing of Multiscale
Applications, submitted to Future Generation Computer Systems, after 1st review (2013)
K. Rycerz, M. Bubak, E. Ciepiela, M. Pawlik, O. Hoenen, D. Harezlak, B. Wilk, T. Gubala, J. Meizner, and D. Coster: Enabling Multiscale Fusion
Simulations on Distributed Computing Resources, submitted to PLGrid PLUS book 2014
• A method and an environment for composing multiscale
applications from single-scale models
• Validation of the the method against real applications
structured using tools
• Extension of application composition techniques to
multiscale simulations
• Support for multisite execution of multiscale simulations
• Proof-of-concept transformation of high-level formal
descriptions into actual execution using e-infrastructures
Multiscale programming and execution tools

24. Research on Feature Modeling:
• modelling eScience applications family
component hierarchy
• modelling requirements
• methods of mapping Feature Models to
Software Product Line architectures
Research on adapting Software Product Line
principles in scientific software projects:
• automatic composition of distributed
eScience applications based on Feature
Model configuration
• architectural design of Software Product
Line engine framework
B. Wilk, M. Bubak, M. Kasztelnik: Software for eScience: from feature modeling to automatic setup of environments, Advances in Software
Development, Scientific Papers of the Polish Informations Processing, Society Scientific Council, 2013 pp. 83-96
Building scientific software based on Feature Model

25. CrossGrid 2002-2005 Interactive compute- and data-intensive applications
K-Wf Grid 2004-2007 Knowledge-based composition of grid workflow applications
CoreGRID 2004-2008 Problem solving environments, programming models for grid applications
GREDIA 2006-2009 Grid platform for media and banking applications
ViroLab 2006-2009 Script based composition of applications, GridSpace virtual laboratory
PL-Grid; + 2009-2015 Advanced virtual laboratory, DataNet – metadata models (2 large Polish projects)
gSLM 2009-2012 Service level management for grid and clouds
UrbanFlood 2009-2012 Common Information Space for Early Warning Systems
MAPPER 2010-2013 Computational strategies, software and services for distributed multiscale simulations
VPH-Share 2011-2015 Federating cloud resources for VPH compute- and data intensive applications
Collage 2011-2013 Executable Papers; 1st award of Elsevier Competition at ICCS2011 (Elsevier project)
ISMOP 2013-2016 Management of cloud resources, workflows, big data storage and access, analysis tools (MCBiR)
PaaSage 2013-2016 Optimization of workflow applications on cloud resources
DICE team in EU projects

26. • Optimization of service
deployment on clouds
– Constraint satisfaction and
optimization of multiple
criteria (cost, performance)
– Static deployment planning
and dynamic auto-scaling
• Billing and accounting
model
– Adapted for the federated
cloud infrastructure
– Handle multiple billing
models
• Supporting system-level
(e)Science
– tools for effective scientific
research and collaboration
– advanced scientific analyses
using HPC/HTC resources
• Cloud security
– security of data transfer
– reliable storage and removal
of the data
• Cross-cloud service
deployment based on
container model
Topics for collaboration
dice.cyfronet.pl