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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/s10723-017-9413-3 ; The 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 Reviewed ; Postprint (author's final draft)

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/s10723-017-9413-3 ; The 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 Reviewed ; Postprint (author's final draft)

Recently cloud services have been evaluated by scientific communities as a viable solution to satisfy their computing needs, reducing the cost of ownership and operation to the minimum. The analysis on the adoption of the cloud computing model for eScience has identified several areas of improvement as federation management and interoperability between providers. Portability between cloud vendors is a widely recognized feature to avoid the risk of lock-in of users in proprietary systems, a stopper to the complete adoption of clouds. In this paper we present a programming framework that allows the coordination of applications on federated clouds used to provide flexibility to traditional research infrastructures as clusters and grids. This approach allows researchers to program applications abstracting the underlying infrastructure and providing scaling and elasticity features through the dynamic provision of virtualized resources. The adoption of standard interfaces is a basic feature in the development of connectors for different middlewares ensuring the portability of the code between different providers. ; This work has been supported through the grants: SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government and the Spanish Ministry of Science and Innovation under contract TIN2012-34557, the Generalitat de Catalunya (contract 2009-SGR-980) and the European Commission (EUBrazilOpenbio: RI-288754 and EGI-InSPIRE: RI- 261323). ; Peer Reviewed ; Postprint (published version)

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 Reviewed ; Postprint (author's final draft)

Task-based programming models offer a flexible way to express the unstructured parallelism patterns of nowadays complex applications. This expressive capability is required to achieve maximum possible performance for applications that are executed in distributed execution platforms. In current task-based workflows, tasks are launched for execution when their data dependencies are satisfied. However, even though the data dependencies of a certain task might have been already produced, the execution of this task will be delayed until its predecessor tasks completely finish their execution. As a consequence of this approach of releasing dependencies, the amount of parallelism inherent in applications is limited and performance improvement opportunities are wasted. To mitigate this limitation, we propose an eager approach for releasing data dependencies. Following this approach, the execution of tasks will not be delayed until their predecessor tasks completely finish their execution, instead, tasks will be launched for execution as soon as their data requirements are available. Hence, more parallelism is exposed and applications can achieve higher levels of performance by overlapping the execution of tasks. Towards achieving this goal, in this paper we propose applying two changes to task-based workflow systems. First, modifying the dependency relationships of tasks to be specified not only in terms of predecessor and successor tasks but also in terms of the data that caused these dependencies. Second, triggering the release of dependencies as soon as a predecessor task generates the output data instead of having to wait until the end of the predecessor execution to release all of its dependencies. We realize this proposal using PyCOMPSs: a task-based programming model for parallelizing Python applications. Our experiments show that using an eager approach for releasing dependencies achieves more than 50% performance improvement in the total execution time as compared to the default approach of releasing dependencies. ; This work is partially supported by the European Union through the Horizon 2020 research and innovation programme under contracts 721865 (EXPERTISE Project) by the Spanish Government (SEV2015-0493,TIN2015-65316-P) and the Generalitat de Catalunya (contract 2014-SGR1051). ; Peer Reviewed ; Postprint (author's final draft)

With the advent of distributed computing, the need for frameworks that facilitate its programming and management has also appeared. These tools have typically been used to support the research on application areas that require them. This poses good initial conditions for translational computer science (TCS), although this does not always occur. This article describes our experience with the PyCOMPSs project, a programming model for distributed computing. While it is a research instrument for our team, it has also been applied in multiple real use cases under the umbrella of European Funded projects, or as part of internal projects between various departments at the Barcelona Supercomputing Center. This article illustrates how the authors have engaged in TCS as an underlying research methodology, collecting experiences from three European projects. ; This work was supported in part by Spanish Government under Contract TIN2015-65316-P, in part by the Generalitat de Catalunya under Contract 2014-SGR-1051, and in part by the European Commission's Horizon 2020 Framework program through BioExcel Center of Excellence under Contract 823830 and Contract 675728, in part by the ExaQUte Project under Contract 800898, in part by the European High-Performance Computing Joint Undertaking (JU) under Grant 955558, in part by the MCIN/AEI/10.13039/501100011033, and in part by the European Union NextGenerationEU/PRTR. ; Peer Reviewed ; Postprint (author's final draft)

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 Reviewed ; Postprint (published version)

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 Reviewed ; Postprint (published version)