SAGA supporting Distributed Applications on Grids, Clouds on FutureGrid

SAGA on FutureGrid


The Simple API for Grid Applications (SAGA) is an OGF standard (, and defines a high level, application-driven API for developing first-principle distributed applications, and for distributed application frameworks and tools. Our SAGA project (see provides SAGA API implementations in C++ and Python, which interface to a variety of middleware backends, as well as higher level application frameworks, such as Master-Worker, MapReduce, AllPairs, and BigJob.  For all those components, we use FutureGrid and the different software environments available on FG for extensive portability and interoperability testing, but also for scale-up and scale-out experiments. These activities allow hardening of the SAGA components described above, and supporting CS and Science experiments based on SAGA.


FG has provided a persistent, production-grade experimental infrastructure with the ability to perform controlled experiments without violating production policies and disrupting production infrastructure priorities.  These attributes, coupled with FutureGrid's technical support, have resulted in the following specific advances in the short period of under a year:

1: Use of FG for Standards-based development and interoperability tests:

We have, in particular, been able to prepare SAGA for future deployments on XSEDE; this has occurred by testing the SAGA-BES adaptor in a variety of configurations: against Unicore and Genesis-II backends, with UserPass and Certificate-based authentication, with POSIX and HPC application types, with and without file staging support.  While those tests are still ongoing, it allows us to be confident about the expected XSEDE middleware evolution; in the vast majority of cases, the standards-based approach seems to work without a hitch.

Furthermore, we are continuously using FG-based job submission endpoints for GIN-driven interoperation tests with a variety of other production Grid infrastructures, including DEISA, PRACE, Teragrid and EGI (see and

In order to simplify the deployment and to improve end user support for SAGA, we have been using FG hosts to develop, test and harden our deployment procedures by mimicking the CSA approach we currently use on TeraGrid and XSEDE.  At the same time, that deployment procedure makes SAGA and SAGA-based components available and maintained on all FG endpoints.

2: Use of FG for Analysing & Comparing Programming Models and Run-time tools for Computation and Data-Intensive Science.

2.a: Development of Tools and Frameworks:
  • P* experiments
    'P*' is a conceptual model of pilot-based abstractions, in particular for pilot jobs.  Our work on P* includes comparison between different PilotJob frameworks (BigJob, Condor GlideIn, Diane, Swift), and between different coordination models within those frameworks.  We used FG for a number of those experiments, as it allowed us to compare a range of characteristics in a controlled environment.
  • Advanced dynamic partitioning and distribution of data-intensive distributed applications
    Futuregrid resources have been crucial in carrying out a first set of scoping experiments for O.W.'s Ph.D thesis: "Towards a Reasoning Framework and Software System to Aid the Dynamic Partitioning, Distribution and Execution of Data-Intensive Applications". In these scoping experiments, three distinct FutureGrid resources (india, hotel, alamo) were used to coordinately execute a data-intensive genome matching workload (HTC). The partitioning and distribution decisions were dynamically made by an experimental software system based on autonomic computing concepts, and which is capable of monitoring FG HPC resources as well as jobs during workload execution.
  • Bliss (Bliss is SAGA)
    Bliss ( is an experimental implementation of SAGA written in pure Python. Bliss does not rely on any distributed Grid middleware; however, it allows distributed access to all FutureGrid HPC resources by providing an SFTP plugin for file transfer as well as 'PBS over SSH' for SAGA's job submission and resource information capabilities.  Bliss has been developed specifically with FutureGrid in mind and has been used in several cross-site experiments as the primary access mechanism to computing and storage resources.  While 'PBS over SSH' probably won't be a replacement for 'real' Grid middleware (like, e.g., Globus), its exposure through the standardized SAGA API presents an attractive and lightweight alternative to traditionally large Grid middleware stacks.
  • High-performance dynamic applications
    In extreme-scale computational science, there is a growing importance and need for specialized architectures and multi-model simulations. In this emerging environment, different simulation components will have different computational requirements.  Instead of coarsely assigning resources to all simulation components for their lifetime, we research methodologies whereby simulations can be split into their constituent components, and distributed computational resources are allocated according to the needs of these individual components.  Each simulation component is transferred along with the data and parameters needed to execute the simulation component on the target hardware.  This approach enables multi-component applications to more easily benefit from heterogeneous and distributed computing environments, in which multiple types of processing elements and storage may be available.
    In cases where software is developed with a static execution mode and only one resource in mind, the choice to distribute may not be available. By creating a dynamic method of execution and developing software which can package, transmit, and execute sub-applications remotely, existing simulations may be extended to make use of distributed resources. Through specially designed modules that are compatible with pre-existing Cactus framework applications, we demonstrated means of improving task-level parallelism, and extended the range of computing resources used with a minimal amount of change needed to existing applications.  Experiments were conducted using production cyberinfrastructures on FutureGrid and XSEDE, with up to 128 cores.
  • Grid/Cloud interop (with Andre Luckow) [finished]
    We demonstrated for the first time the use of Pilot-Jobs concurrently on different types of infrastructures; specifically, we use BigJob both on FutureGrid HPC and Cloud resources as well as on other resources such as the XSEDE and OSG Condor resources.

2.b: Data Intensive Apps:

  • MapReduce [with Andre Luckow]
    In Ref. [1], published in Future Generation Computing Systems, we compare implementations of the word-count application to use not only multiple, heterogeneous infrastructure (Sector versus DFS), but also different programming models (Sphere versus MapReduce).
  • Grid/Cloud NGS analysis experiments
    Building upon SAGA-based MapReduce, we have constructed an efficient pipeline for gene sequencing. This pipeline is capable of dynamic resource utilization and task/worker placement.
  • Hybrid cloud-Grid scientific applications and tools (autonomic schedulers) [with Manish Parashar, finished]
    Policy-based (objective driven) Autonomic Scheduler provides a system-level approach to hybrid grid-cloud usage.  FG has been used for the development and extension of such Autonomic Scheduling and application requirements.  We have integrated the distributed and heterogeneous resources of FG as a pool of resources which can be allocated by the policy-based Autonomic Scheduler (Comet). The Autonomic Scheduler dynamically determines and allocates instances to meet specific objectives, such as lowest time-to-completion, lowest cost etc. We also used FG supplement objective-driven pilot jobs on TeraGrid (ranger).
  • Investigate run time fluctuations of application kernels
    We attempt to explore and characterize run-time fluctuations for a given application kernel representative of both a large number of MPI/parallel workloads and workflows.  Fluctuation appears to be independent of the system load and a consequence of the complex interaction of the MPI library specifics and virtualization layer, as well as operating environment.  Thus we have been investigating fluctuations in application performance due to the cloud operational environment.  An explicit aim is to correlate these fluctuations to details of the infrastructure.  As it is difficult to discern or reverse engineer the specific infrastructure details on EC2 or other commercial infrastructure, FG has provided us a controlled and well understood environment at infrastructure scales that are not possible at the individual PI/resource level.

3. SAGA has also produced the following papers (selection):

See Refs: [1],[2],[3],[4],[5]


We will be continuing to use FG as a resource for SAGA development.  Among other goals, we intend the following: to move the testing infrastructure to other SAGA based components, like our PilotJob and PilotData frameworks; to widen the set of middlewares used for testing (again, keeping XSEDE and other PGIs in mind); to enhance the scope and scale of our scalability testing; and to test and harden our deployment and packaging procedures.



  1. [fg-1975] Sehgal, S., M. Erdelyi, A. Merzky, and S. Jha, "Understanding application-level interoperability: Scaling-out MapReduce over high-performance grids and clouds", Future Generation Computer Systems, vol. 27, issue 5, 2011.
  2. [fg-1976] Luckow, A., L. Lacinski, and S. Jha, "SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems", 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2010.
  3. [fg-1977] Luckow, A., and S. Jha, "Abstractions for Loosely-Coupled and Ensemble-Based Simulations on Azure", IEEE International Conference on Cloud Computing Technology and Science, 2010.
  4. [fg-1978] Kim, J., S. Maddineni, and S. Jha, "Building Gateways for Life-Science Applications using the Dynamic Application Runtime Environment (DARE) Framework", The 2011 TeraGrid Conference: Extreme Digital Discovery, 2011.
  5. [fg-1979] Kim, J., S. Maddineni, and S. Jha, "Characterizing Deep Sequencing Analytics using BFAST: Towards a Scalable Distributed Architecture for Next-Generation Sequencing Data", The Second International Workshop on Emerging Computational Methods for the Life Sciences, 06/2011.
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