funcX: A Federated Function Serving Fabric for Science

Exploding data volumes and velocities, new computational methods and platforms, and ubiquitous connectivity demand new approaches to computation in the sciences. These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most suitable resources. To address these needs we present funcX---a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. funcX's endpoint software can transform existing clouds, clusters, and supercomputers into function serving systems, while funcX's cloud-hosted service provides transparent, secure, and reliable function execution across a federated ecosystem of endpoints. We motivate the need for funcX with several scientific case studies, present our prototype design and implementation, show optimizations that deliver throughput in excess of 1 million functions per second, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap with arXiv:1908.0490

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Kestrel: Job Distribution and Scheduling using XMPP

A new distributed computing framework, named Kestrel, for Many-Task Computing (MTC) applications and implementing Virtual Organization Clusters (VOCs) is proposed. Kestrel is a lightweight, highly available system based on the Extensible Messaging and Presence Protocol (XMPP), and has been developed to explore XMPP-based techniques for improving MTC and VOC tolerance to faults due to scaling and intermittently connected heterogeneous resources. Kestrel provides a VOC with a special purpose scheduler for VOCs which can provide better scalability under certain workload assumptions, namely CPU bound processes and bag-of-task applications. Experimental results have shown that Kestrel is capable of operating a VOC of at least 1600 worker nodes with all nodes visible to the scheduler at once. When using multiple sites located in both North America and Europe, the latencies introduced to the round trip time of messages were on the order of 0.3 seconds. To offset the overhead of XMPP processing, a task execution time of 2 seconds is sufficient for a pool of 900 workers on a single site to operate at near 100% use. Requiring tasks that take on the order of 30 seconds to a minute to execute would compensate for increased latency during job dispatch across multiple sites. Kestrel\u27s architecture is rooted in pilot job frameworks heavily used in Grid computing, it is also modeled after the use of IRC by botnets to communicate between compromised machines and command and control servers. For Kestrel, the extensibility of XMPP has allowed development of protocols for identifying manager nodes, discovering the capabilities of worker agents, and for distributing tasks. The presence notifications provided by XMPP allow Kestrel to monitor the global state of the pool and to perform task dispatching based on worker availability. In this work it is argued that XMPP is by design a very good fit for cloud computing frameworks. It offers scalability, federation between servers and some autonomicity of the agents. During the summer of 2010, Kestrel was used and modified based on feedback from the STAR group at Brookhaven National Laboratories. STAR provided a virtual machine image with applications for simulating proton collisions using PYTHIA and GEANT3. A Kestrel-based virtual organization cluster, created on top of Clemson University\u27s Palmetto cluster, was able to provide over 400,000 CPU hours of computation over the course of a month using an average of 800 virtual machine instances every day, generating nearly seven terabytes of data and the largest PYTHIA production run that STAR ever achieved. Several architectural issues were encountered during the course of the experiment and were resolved by moving from the original JSON protocols used by Kestrel to native XMPP equivalents that offered better message delivery confirmation and integration with existing tools

The Grid[Way] Job Template Manager, a tool for parameter sweeping

Parameter sweeping is a widely used algorithmic technique in computational science. It is specially suited for high-throughput computing since the jobs evaluating the parameter space are loosely coupled or independent. A tool that integrates the modeling of a parameter study with the control of jobs in a distributed architecture is presented. The main task is to facilitate the creation and deletion of job templates, which are the elements describing the jobs to be run. Extra functionality relies upon the GridWay Metascheduler, acting as the middleware layer for job submission and control. It supports interesting features like multi-dimensional sweeping space, wildcarding of parameters, functional evaluation of ranges, value-skipping and job template automatic indexation. The use of this tool increases the reliability of the parameter sweep study thanks to the systematic bookkeping of job templates and respective job statuses. Furthermore, it simplifies the porting of the target application to the grid reducing the required amount of time and effort.Comment: 26 pages, 1 figure

Scheduling in Mapreduce Clusters

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing. As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied. The objective of this dissertation is to provide MapReduce applications with low cost and energy consumption through the development of scheduling theory and algorithms, energy models, and energy-aware resource management. In particular, we will investigate energy-efficient scheduling in hybrid CPU-GPU MapReduce clusters. This research work is expected to have a breakthrough in Big Data processing, particularly in providing green computing to Big Data applications such as social network analysis, medical care data mining, and financial fraud detection. The tools we propose to develop are expected to increase utilization and reduce energy consumption for MapReduce clusters. In this PhD dissertation, we propose to address the aforementioned challenges by investigating and developing 1) a match-making scheduling algorithm for improving the data locality of Map- Reduce applications, 2) a real-time scheduling algorithm for heterogeneous Map- Reduce clusters, and 3) an energy-efficient scheduler for hybrid CPU-GPU Map- Reduce cluster. Advisers: Ying Lu and David Swanso