COMPSs-Mobile: parallel programming for mobile-cloud computing

The advent of Cloud and the popularization of mobile devices have led us to a shift in computing access. Computing users will have an interaction display while the real computation will be performed remotely, in the Cloud. COMPSs-Mobile is a framework that aims to ease the development of energy-efficient and high-performing applications for this environment. The framework provides an infrastructure-unaware programming model that allows developers to code regular Android applications that, transparently, are parallelized, and partially offloaded to remote resources. This paper gives an overview of the programming model and describes the internal components of the toolkit which supports it focusing on the offloading and checkpointing mechanisms. It also presents the results of some tests conducted to evaluate the behavior of the solution and to measure the potential benefits in Android applications.Peer ReviewedPostprint (published version

Programming models for mobile environments

Premi extraordinari doctorat UPC curs 2017-2018. Àmbit d’Enginyeria de les TICFor the last decade, mobile devices have grown in popularity and became the best-selling computing devices. Despite their high capabilities for user interactions and network connectivity, the computing power of mobile devices is low and the lifetime of the application running on them limited by the battery. Mobile Cloud Computing (MCC) is a technology that tackles the limitations of mobile devices by bringing together their mobility with the vast computing power of the Cloud. Programming applications for Mobile Cloud Computing (MCC) environments is not as straightforward as coding monolithic applications. Developers have to deal with the issues related to parallel programming for distributed infrastructures while considering the battery lifetime and the variability of the network produced by the high mobility of this kind of devices. As with any other distributed environment, developers turn to programming models to improve their productivity by avoiding the complexity of manually dealing with these issues and delegate on the corresponding model all the management of these concerns. This thesis contributes to the current state of the art with an adaptation of the COMPSs programming model for MCC environments. COMPSs allows application programmers to code their applications in a sequential, infrastructure-agnostic fashion without calls to any COMPSs-specific API using the native language for the target platform as if they were to run on the mobile device. At execution time, a runtime system automatically partitions the application into tasks and orchestrates their execution on top of the available resources. This thesis contributes with an extension to the programming model to allow task polymorphism and let the runtime exploit computational resources other than the CPU of the resources. Besides, the runtime architecture has been redesigned with the characteristics of MCC in mind, and it runs as a common service which all the applications running simultaneously on the mobile device contact for submitting the execution of their tasks. For collaboratively exploiting both, local and remote resources, the runtime clusters the computational devices into Computing Platforms according to the mechanisms required to provide the processing elements with the necessary input values, launch the task execution avoiding resource oversubscription and fetching the results back from them. The CPU Platform run tasks on the cores of the CPU. The GPU Platform leverages on OpenCL to run tasks as kernels on GPUs or other accelerators embedded in the mobile device. Finally, the Cloud Platform offloads the execution of tasks onto remote resources. To holistically decide whether is worth running a task on embedded or on remote resources, the runtime considers the the costs -- time, energy and money -- of running the computation on each of the platforms and picks the best. Each platform manages internally its resources and orchestrates the execution of tasks on them using different scheduling policies. Using local and remote computing devices forces the runtime to share data values among the nodes of the infrastructure. This data is potentially privacy-sensitive, and the runtime exposes it to possible attackers when transferring it through the network. To protect the application user from data leaks, the runtime has to provide communications with secrecy, integrity and authenticity. In the extreme case of a network breakdown that isolates the mobile device from the remote nodes, the runtime has to ensure that the execution continues to provide the application user with the expected result even if the connection never re-establishes. The mobile device has to respond using only the resources embedded in it, what could incur in the re-execution of computations already ran on the remote resources. Remote workers have to continue with the execution so that, in case of reconnection, both parts synchronize its progress to reduce the impact of the disruption.Els últims anys, els dispositius mòbils han guanyat en popularitat i s'han convertit en els dispositius més venuts. Tot i la connectivitat i la bona interacció amb l'usuari que ofereixen, la seva capacitat de càlcul is baixa i limitada per la vida de la bateria. El Mobile Cloud Computing (MCC) és una tecnologia que soluciona les limitacions d'aquests dispositius ajuntant la seva mobilitat amb la gran capacitat de còmput del Cloud. Programar aplicacions per entorns MCC no és tan directe com fer aplicacions monolítiques. Els desenvolupadors han de tractar amb els problemes relacionats amb la programació paral·lela mentre tenen en compte la duració de la bateria i la variabilitat de la xarxa degut a la mobilitat inherent a aquest tipus de dispositius. Com per qualsevol altre entorn distribuït, els desenvolupadors recorren a models de programació que millorin la seva productivitat i els evitin tractar manualment amb aquests problemes delegant la seva gestió en el model. Aquesta tesis contribueix a l'estat de l'art actual amb una adaptació del model de programació COMPSs als entorns MCC. COMPSs permet als desenvolupadors programar les aplicacions de forma agnòstica a la infraestructura i seqüencial sense necessitat d'invocar cap API específica utilitzant el llenguatge natiu de la platforma com si l'aplicació s'executés directament en el mòbil. En temps d'execució, una eina (runtime) automàticament divideix l'aplicació en tasques i n'orquestra la seva execució sobre els recursos disponibles. Aquesta tesis estèn el model de programació per tal de permetre polimorfisme a nivell de tasca i deixar al runtime explotar els recursos computacionals dels que disposa el mòbil a part de la CPU. A més a més, l'arquitectura del runtime s'ha redissenyat tenint en compte les característiques pròpies del MCC, i aquest s'executa com un servei comú al que totes les aplicacions del mòbil contacten per tal d'executar les seves tasques. Per explotar col·laborativament tots els recursos, locals i remots, el runtime agrupa els recursos en Computing Platforms en funció dels mecanismes necessaris per proveir el recurs amb les dades d'entrada necessàries, llançar l'execució i recuperar-ne els resultats. La CPU Platform executa tasques en els nuclis de la CPU. La GPU Platform utilitza OpenCL per executar tasques en forma de kernels a la GPU o altres acceleradors integrats en el mòbil. Finalment, la Cloud Platform descàrrega l'execució de tasques en recursos remots. Per decidir holisticament si és millor executar una tasca en un recurs local o en un remot, el runtime considera els costs (temporal, energètic econòmic) d'executar la tasca en cada una de les plataformes i n'escull la millor. Cada plataforma gestiona internament els seus recursos i orquestra l'execució de les tasques en ells seguint diferents polítiques de planificació. L'ús de recursos locals i remots força la compartició de dades entre els nodes de la infraestructura. Aquestes dades són potencialment sensibles i de caràcter privat i el runtime les exposa a possibles atacs que les transfereix per la xarxa. Per tal de protegir l'usuari de possibles fuites de dades, el runtime ha de dotar les comunicacions amb confidencialitat, integritat i autenticitat. En el cas extrem en que un error de xarxa aïlli el dispositiu mòbil dels nodes remots, el runtime ha d'assegurar que l'execució continua i que eventualment l'usuari rebrà el resultat esperat fins i tot en cas de que la connexió no és restableixi mai. El mòbil ha de ser capaç d'executar l'aplicació utilitzant únicament les dades i recursos disponibles en aquell moment, la qual cosa pot forçar la re-execució d'algunes tasques ja calculades en els recursos remots. Els recursos remots han de continuar l'execució per tal que en cas de reconnexió, ambdues parts sincronitzin el seu progrés i es minimitzi l'impacte de la desconnexió.Award-winningPostprint (published version

Programming models for mobile environments

For the last decade, mobile devices have grown in popularity and became the best-selling computing devices. Despite their high capabilities for user interactions and network connectivity, the computing power of mobile devices is low and the lifetime of the application running on them limited by the battery. Mobile Cloud Computing (MCC) is a technology that tackles the limitations of mobile devices by bringing together their mobility with the vast computing power of the Cloud. Programming applications for Mobile Cloud Computing (MCC) environments is not as straightforward as coding monolithic applications. Developers have to deal with the issues related to parallel programming for distributed infrastructures while considering the battery lifetime and the variability of the network produced by the high mobility of this kind of devices. As with any other distributed environment, developers turn to programming models to improve their productivity by avoiding the complexity of manually dealing with these issues and delegate on the corresponding model all the management of these concerns. This thesis contributes to the current state of the art with an adaptation of the COMPSs programming model for MCC environments. COMPSs allows application programmers to code their applications in a sequential, infrastructure-agnostic fashion without calls to any COMPSs-specific API using the native language for the target platform as if they were to run on the mobile device. At execution time, a runtime system automatically partitions the application into tasks and orchestrates their execution on top of the available resources. This thesis contributes with an extension to the programming model to allow task polymorphism and let the runtime exploit computational resources other than the CPU of the resources. Besides, the runtime architecture has been redesigned with the characteristics of MCC in mind, and it runs as a common service which all the applications running simultaneously on the mobile device contact for submitting the execution of their tasks. For collaboratively exploiting both, local and remote resources, the runtime clusters the computational devices into Computing Platforms according to the mechanisms required to provide the processing elements with the necessary input values, launch the task execution avoiding resource oversubscription and fetching the results back from them. The CPU Platform run tasks on the cores of the CPU. The GPU Platform leverages on OpenCL to run tasks as kernels on GPUs or other accelerators embedded in the mobile device. Finally, the Cloud Platform offloads the execution of tasks onto remote resources. To holistically decide whether is worth running a task on embedded or on remote resources, the runtime considers the the costs -- time, energy and money -- of running the computation on each of the platforms and picks the best. Each platform manages internally its resources and orchestrates the execution of tasks on them using different scheduling policies. Using local and remote computing devices forces the runtime to share data values among the nodes of the infrastructure. This data is potentially privacy-sensitive, and the runtime exposes it to possible attackers when transferring it through the network. To protect the application user from data leaks, the runtime has to provide communications with secrecy, integrity and authenticity. In the extreme case of a network breakdown that isolates the mobile device from the remote nodes, the runtime has to ensure that the execution continues to provide the application user with the expected result even if the connection never re-establishes. The mobile device has to respond using only the resources embedded in it, what could incur in the re-execution of computations already ran on the remote resources. Remote workers have to continue with the execution so that, in case of reconnection, both parts synchronize its progress to reduce the impact of the disruption.Els últims anys, els dispositius mòbils han guanyat en popularitat i s'han convertit en els dispositius més venuts. Tot i la connectivitat i la bona interacció amb l'usuari que ofereixen, la seva capacitat de càlcul is baixa i limitada per la vida de la bateria. El Mobile Cloud Computing (MCC) és una tecnologia que soluciona les limitacions d'aquests dispositius ajuntant la seva mobilitat amb la gran capacitat de còmput del Cloud. Programar aplicacions per entorns MCC no és tan directe com fer aplicacions monolítiques. Els desenvolupadors han de tractar amb els problemes relacionats amb la programació paral·lela mentre tenen en compte la duració de la bateria i la variabilitat de la xarxa degut a la mobilitat inherent a aquest tipus de dispositius. Com per qualsevol altre entorn distribuït, els desenvolupadors recorren a models de programació que millorin la seva productivitat i els evitin tractar manualment amb aquests problemes delegant la seva gestió en el model. Aquesta tesis contribueix a l'estat de l'art actual amb una adaptació del model de programació COMPSs als entorns MCC. COMPSs permet als desenvolupadors programar les aplicacions de forma agnòstica a la infraestructura i seqüencial sense necessitat d'invocar cap API específica utilitzant el llenguatge natiu de la platforma com si l'aplicació s'executés directament en el mòbil. En temps d'execució, una eina (runtime) automàticament divideix l'aplicació en tasques i n'orquestra la seva execució sobre els recursos disponibles. Aquesta tesis estèn el model de programació per tal de permetre polimorfisme a nivell de tasca i deixar al runtime explotar els recursos computacionals dels que disposa el mòbil a part de la CPU. A més a més, l'arquitectura del runtime s'ha redissenyat tenint en compte les característiques pròpies del MCC, i aquest s'executa com un servei comú al que totes les aplicacions del mòbil contacten per tal d'executar les seves tasques. Per explotar col·laborativament tots els recursos, locals i remots, el runtime agrupa els recursos en Computing Platforms en funció dels mecanismes necessaris per proveir el recurs amb les dades d'entrada necessàries, llançar l'execució i recuperar-ne els resultats. La CPU Platform executa tasques en els nuclis de la CPU. La GPU Platform utilitza OpenCL per executar tasques en forma de kernels a la GPU o altres acceleradors integrats en el mòbil. Finalment, la Cloud Platform descàrrega l'execució de tasques en recursos remots. Per decidir holisticament si és millor executar una tasca en un recurs local o en un remot, el runtime considera els costs (temporal, energètic econòmic) d'executar la tasca en cada una de les plataformes i n'escull la millor. Cada plataforma gestiona internament els seus recursos i orquestra l'execució de les tasques en ells seguint diferents polítiques de planificació. L'ús de recursos locals i remots força la compartició de dades entre els nodes de la infraestructura. Aquestes dades són potencialment sensibles i de caràcter privat i el runtime les exposa a possibles atacs que les transfereix per la xarxa. Per tal de protegir l'usuari de possibles fuites de dades, el runtime ha de dotar les comunicacions amb confidencialitat, integritat i autenticitat. En el cas extrem en que un error de xarxa aïlli el dispositiu mòbil dels nodes remots, el runtime ha d'assegurar que l'execució continua i que eventualment l'usuari rebrà el resultat esperat fins i tot en cas de que la connexió no és restableixi mai. El mòbil ha de ser capaç d'executar l'aplicació utilitzant únicament les dades i recursos disponibles en aquell moment, la qual cosa pot forçar la re-execució d'algunes tasques ja calculades en els recursos remots. Els recursos remots han de continuar l'execució per tal que en cas de reconnexió, ambdues parts sincronitzin el seu progrés i es minimitzi l'impacte de la desconnexió

Extensions al model de programació COMPSs per infrastructures Cloud

COMP Superscalar és un model de programació que busca explotar el paral·lelisme a nivell de tasca inherent a les aplicacions i simplificar el desenvolupament d'aplicacions pel Grid i altres plataformes de computació distribuïda. L'usuari pot programar les seves aplicacions sense tenir en compte la plataforma en la que s'executarà; només ha de seleccionar quines tasques han d'executar-s'hi sense haver de realitzar cap tipus de crida que hi estigui relacionada Per obtenir un bon rendiment i fer un bon ús dels recursos, les decisions es prenen en temps d'execució. Això comporta la necessitat d'un runtime que sigui capaç de gestionar els recursos disponibles i decidir en cada moment quina és la millor decisió a l'hora d'assignar les tasques. El resultat final del projecte ha de ser una modificació d'aquest runtime per tal d'oferir un model de programació orientat als serveis que ofereixen les infraestructures Cloud respectant sempre el model de programació en sí, per tal de simplificar a l'usuari el pas de la versió pel Grid a la nova versió i així mantenir la simplicitat en la programació de les aplicacions

Programming models for mobile environments

In this article, we present COMPSs-Mobile: a framework that aims to ease the development of MCC applications by freeing the developer of all these concerns. At packaging time applications, written following its own programming model, are modified to invoke a runtime toolkit included in the application bundle as a library. This toolkit manages the parallelization and distribution of the application execution aiming to reduce the execution time and the energy consumption of the mobile device. To the best of our knowledge, COMPSs-Mobile is the first framework that applies the automatic parallelization and distribution of applications on mobile environments

Securing the Execution of ML Workflows across the Compute Continua

Cloud computing has become the major computational paradigm for the deployment of all kind of applications, ranging from mobile apps to complex AI algorithms. On the other side, the rapid growth of IoT market has led to the need of processing the data produced by smart devices using their embedded resources. The computing continuum paradigm aims at solving the issues related to the deployment of applications across edge-to-cloud cyber-infrastructures.This work considers in-memory data protection to enhance security over the compute continua and proposes a solution for the development of distributed applications that handles security in a transparent way for the developer. The proposed framework has been evaluated using an ML application that classifies health data using a pre-trained model. The results show that securing in-memory data incurs no additional effort at development time and the overheads introduced by the encryption mechanisms do not compromise the scalability of the application.This work has been supported by the Spanish Government (PID2019-107255GB) and by MCIN/AEI /10.13039/501100011033 (CEX2021- 001148-S), by Generalitat de Catalunya (contract 2021-SGR-00412),and by the European Commission through the Horizon Europe Research and Innovation program under Grant Agreement No.101016577 (AI-SPRINT project).Peer ReviewedPostprint (author's final draft

Towards mobile cloud computing with single sign-on access

This is a post-peer-review, pre-copyedit version of an article published in Journal of Grid Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10723-017-9413-3The low computing power of mobile devices impedes the development of mobile applications with a heavy computing load. Mobile Cloud Computing (MCC) has emerged as the solution to this by connecting mobile devices with the “infinite” computing power of the Cloud. As mobile devices typically communicate over untrusted networks, it becomes necessary to secure the communications to avoid privacy-sensitive data breaches. This paper presents work on implementing MCC applications with secure communications. For that purpose, we built on COMPSs-Mobile, a redesigned implementation of the COMP Superscalar (COMPSs) framework aiming to MCC platorms. COMPSs-Mobile automatically exploits the parallelism inherent in an application and orchestrates its execution on loosely-coupled distributed environment. To avoid a vendor lock-in, this extension leverages on the Generic Security Services Application Program Interface (GSSAPI) (RFC2743) as a generic way to access security services to provide communications with authentication, secrecy and integrity. Besides, GSSAPI allows applications to take profit of more advanced features, such as Federated Identity or Single Sign-On, which the underlying security framework could provide. To validate the practicality of the proposal, we use Kerberos as the security services provider to implement SSO; however, applications do not authenticate themselves and require users to obtain and place the credentials beforehand. To evaluate the performance, we conducted some tests running an application on a smartphone offloading tasks to a private cloud. Our results show that the overhead of securing the communications is acceptable.This work has been supported by the Spanish Government (contracts TIN2012-34557, TIN2015-65316-P and grants BES-2013-067167, EEBB-I-15-09808 of the Research Training Program and SEV-2011-00067 of Severo Ochoa Program), by Generalitat de Catalunya (contract 2014-SGR-1051) and by the European Commission (ASCETiC project, FP7-ICT-2013.1.2 contract 610874). The second author was partially supported by the European Commission's Horizon2020 programme under grant agreement 653965 (AARC).Peer ReviewedPostprint (author's final draft

An Architecture for Programming Distributed Applications on Fog to Cloud Systems

This paper presents a framework to develop and execute applications in distributed and highly dynamic computing systems composed of cloud resources and fog devices such as mobile phones, cloudlets, and micro-clouds. The work builds on the COMPSs programming framework, which includes a programming model and a runtime already validated in HPC and cloud environments for the transparent execution of parallel applications. As part of the proposed contribution, COMPSs has been enhanced to support the execution of applications on mobile platforms that offer GPUs and CPUs. The scheduling component of COMPSs is under design to be able to offload the computation to other fog devices in the same level of the hierarchy and to cloud resources when more computational power is required. The framework has been tested executing a sample application on a mobile phone offloading task to a laptop and a private cloud.This work is partly supported by the Spanish Ministry of Science and Technology through project TIN2015-65316-P and grant BES-2013-067167, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation programme under grant 730929 (mF2C Project).Peer ReviewedPostprint (author's final draft

COMP Superscalar, an interoperable programming framework

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources.This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration.Peer ReviewedPostprint (published version