Multi-Objective Optimization for Task Mode Selection and Scheduling Utilizing a Genetic Algorithm.
Abstract not provided.
Dessanti, Alexander↗
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Characterizing the Performance of Executing Many-tasks on Summit
Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.
Matteo, Turilli↗
HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles
The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.
Ma, Jialiang↗
Real-time design with peer tasks
We introduce a real-time design methodology for large scale, distributed, parallel architecture, real-time systems (LDPARTS), as an alternative to those methods using rate or dead-line monotonic analysis. In our method the fundamental units of prioritization, work items, are domain specific objects with timing requirements (deadlines) found in user's specification. A work item consists of a collection of tasks of equal priority. Current scheduling theories are applied with artifact deadlines introduced by the designer whereas our method schedules work items to meet user's specification deadlines (sometimes called end-to-end deadlines). Our method supports these scheduling properties. Work item scheduling is based on domain specific importance instead of task level urgency and still meets as many user specification deadlines as can be met by scheduling tasks with respect to urgency. Second, the minimum (closest) on-line deadline that can be guaranteed for a work item of highest importance, scheduled at run time, is approximately the inverse of the throughput, measured in work items per second. Third, throughput is not degraded during overload and instead of resorting to task shedding during overload, the designer can specify which work items to shed. We prove these properties in a mathematical model.
Goforth, Andre↗
Crew Scheduling Performance
As NASA considers long-duration exploration missions (LDEMs), it is envisioned that crew will behave more autonomously as compared to low-Earth orbit missions. The necessary shift of Ops Planners’ complete management of scheduling and planning to provide flexibility for crew to manage their own schedule in real-time requires significant research and investigation in evaluation of concepts of operations, of software tools to support tasks, and of crew performance to complete scheduling tasks. Our research objective is to characterize the human performance envelope for the task of planning and scheduling (crew self-scheduling), develop countermeasures to mitigate adverse performance effects due to plan complexity, and inform performance standards and guidelines based on research results. In our efforts to understand and characterize scheduling performance of crew members, we have conducted subject matter expert interviews to obtain feedback on scheduling plan complexity drivers and plan goodness. Following the pilot study conducted last year, we designed a study and have begun remote human subject testing to evaluate non-expert human performance, workload, and situational awareness for the task of planning and scheduling. This study investigates the effects of the number and type of constraints on human performance, and the differences in scheduling metrics between scheduling and rescheduling tasks. Subject testing is currently in progress, and we aim to collect data from approximately thirty subjects. We will summarize the methods used and impacts of the number and type of scheduling constraints. We will also present lessons learned as well as relevant results of the study.
crew scheduling↗
ISS Expedition 1 Crew Interviews: William M. Shepherd
Live footage of a preflight interview with Commander Bill Shepherd is seen. The interview addresses many different questions including why Shepherd became interested in the space program, the events that led to his interest, the transition from the navy to his selection in the astronaut program. Other interesting information that this one-on-one interview discusses are the main goals of the first Expedition Crew, their scheduled docking with the International Space Station (ISS), making the ISS ready for human inhabitance, and all the specifics that will make his living arrangements difficult. Shepherd mentions his responsibilities during the much-anticipated two-day flight to the ISS, as well as the scheduled space-walk. Shepherd also discusses the crew's first tasks upon entrance including other scheduled tasks for the first week, docking from cargo ships, and spacecraft delivering equipment or performing Extra Vehicular Activities (EVA). He explains his interpretation of the meaning of mission success, and the implications of having human beings in space.
Source record↗
Experiment scheduling for Spacelab missions
The Experiment Scheduling Program (ESP) is the heart of a group of programs developed at NASA-Marshall to schedule the experiment activities of Spacelab and other Shuttle missions. Other programs in the group either prepare input data for ESP or produce derivative information based on the schedule produced by ESP. The task of experiment scheduling can be simply stated as positioning the experiment activities in a mission to that they collect their desired data without interfering with other activities. The program's capabilities as seen by the user are described along with mission constraints the program handles, and how the expert system in the program handles these constraints.
Jaap, John↗
The application of artificial intelligence to astronomical scheduling problems
Efficient utilization of expensive space- and ground-based observatories is an important goal for the astronomical community; the cost of modern observing facilities is enormous, and the available observing time is much less than the demand from astronomers around the world. The complexity and variety of scheduling constraints and goals has led several groups to investigate how artificial intelligence (AI) techniques might help solve these kinds of problems. The earliest and most successful of these projects was started at Space Telescope Science Institute in 1987 and has led to the development of the Spike scheduling system to support the scheduling of Hubble Space Telescope (HST). The aim of Spike at STScI is to allocate observations to timescales of days to a week observing all scheduling constraints and maximizing preferences that help ensure that observations are made at optimal times. Spike has been in use operationally for HST since shortly after the observatory was launched in Apr. 1990. Although developed specifically for HST scheduling, Spike was carefully designed to provide a general framework for similar (activity-based) scheduling problems. In particular, the tasks to be scheduled are defined in the system in general terms, and no assumptions about the scheduling timescale are built in. The mechanisms for describing, combining, and propagating temporal and other constraints and preferences are quite general. The success of this approach has been demonstrated by the application of Spike to the scheduling of other satellite observatories: changes to the system are required only in the specific constraints that apply, and not in the framework itself. In particular, the Spike framework is sufficiently flexible to handle both long-term and short-term scheduling, on timescales of years down to minutes or less. This talk will discuss recent progress made in scheduling search techniques, the lessons learned from early HST operations, the application of Spike to other problem domains, and plans for the future evolution of the system.
Johnston, Mark D.↗
Exploring Self-Scheduling Strategies and Heuristics in Novice Schedulers
Missions beyond low Earth orbit will require crews to act with greater autonomy. Increasing communication delays will require crews to take on tasks currently supported by Mission Control, including the involved process of scheduling and rescheduling their own complex spaceflight timelines to fit a variety of restrictions and constraints. Unlike Mission Control, astronauts are not expert planners, and determining strategies and heuristics that enable crews to schedule successfully may increase the range of problems they can solve. In two human-in-the-loop scheduling experiments, we analyzed 1) common strategies among novice schedulers and 2) the development of self-scheduling heuristics. We find that, even when participants are instructed to follow a given strategy, they rapidly develop their own self-scheduling heuristics as they learn to successfully complete the scheduling task. While scheduling, participants naturally learn to arrange activities with the most constraints first, whereas while rescheduling, they display greater variability in their heuristics.
self-scheduling↗
Clustering execution in a processing system to increase power savings
Embodiments relate to clustering execution in a processing system. An aspect includes accessing a control flow graph that defines a data dependency and an execution sequence of a plurality of tasks of an application that executes on a plurality of system components. The execution sequence of the tasks in the control flow graph is modified as a clustered control flow graph that clusters active and idle phases of a system component while maintaining the data dependency. The clustered control flow graph is sent to an operating system, where the operating system utilizes the clustered control flow graph for scheduling the tasks.
97 MATHEMATICS AND COMPUTING↗
Clustering execution in a processing system to increase power savings
Embodiments relate to clustering execution in a processing system. An aspect includes accessing a control flow graph that defines a data dependency and an execution sequence of a plurality of tasks of an application that executes on a plurality of system components. The execution sequence of the tasks in the control flow graph is modified as a clustered control flow graph that clusters active and idle phases of a system component while maintaining the data dependency. The clustered control flow graph is sent to an operating system, where the operating system utilizes the clustered control flow graph for scheduling the tasks.
97 MATHEMATICS AND COMPUTING↗
Optimal Scheduling of a Multi-User, Multi-Tasking Automatic Robotic Telescope
An algorithm is introduced to generate the optimal schedule of observations for a multi-user, multi-tasking automatic robotic telescope. The schedule is optimal m that it will take the highest possible quality data, perform the largest number of tasks, and fairly serve multiple users in terms of data quality, allocated telescope time, and observation priorities. In addition, the algorithm addresses problems due to overloading and underloading the telescope. The algorithm, which employs dynamic-programming-based decision theory, is analytically proven to produce the optimal schedule using set theory. Simulations conducted using actual astronomical data are presented which show significant improvement over existing scheduling techniques. Once completed the new scheduling algorithm will be tested on the Tennessee State University/Harvard-Smithsonian Center for Astrophysics 32-inch automatic Photoelectric Telescope at Fairborn Observatory, Arizona.
Tantaris, R. N.↗
Development and evaluation of a Fault-Tolerant Multiprocessor (FTMP) computer. Volume 2: FTMP software
The software developed for the Fault-Tolerant Multiprocessor (FTMP) is described. The FTMP executive is a timer-interrupt driven dispatcher that schedules iterative tasks which run at 3.125, 12.5, and 25 Hz. Major tasks which run under the executive include system configuration control, flight control, and display. The flight control task includes autopilot and autoland functions for a jet transport aircraft. System Displays include status displays of all hardware elements (processors, memories, I/O ports, buses), failure log displays showing transient and hard faults, and an autopilot display. All software is in a higher order language (AED, an ALGOL derivative). The executive is a fully distributed general purpose executive which automatically balances the load among available processor triads. Provisions for graceful performance degradation under processing overload are an integral part of the scheduling algorithms.
Lala, J. H.↗
A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes
Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. The situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL’s asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom ex- tension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.
Index Terms—Parallel systems, Scheduling and Task ↗
Stochastic Programming Approach for Resource Selection under Demand Uncertainty
Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.
scientific workflows, task scheduling, optimizatio↗
Automated Procurement System (APS): Project management plan (DS-03), version 1.2
The National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) is implementing an Automated Procurement System (APS) to streamline its business activities that are used to procure goods and services. This Project Management Plan (PMP) is the governing document throughout the implementation process and is identified as the APS Project Management Plan (DS-03). At this point in time, the project plan includes the schedules and tasks necessary to proceed through implementation. Since the basis of APS is an existing COTS system, the implementation process is revised from the standard SDLC. The purpose of the PMP is to provide the framework for the implementation process. It discusses the roles and responsibilities of the NASA project staff, the functions to be performed by the APS Development Contractor (PAI), and the support required of the NASA computer support contractor (CSC). To be successful, these three organizations must work together as a team, working towards the goals established in this Project Plan. The Project Plan includes a description of the proposed system, describes the work to be done, establishes a schedule of deliverables, and discusses the major standards and procedures to be followed.
Murphy, Diane R.↗
Work Plan for the Site-wide SSHAC Level 3 Probabilistic Seismic Hazard Analysis at INL
This work plan provides the overall framework, key activities, key tasks, and schedule for the execution of a probabilistic seismic hazard analysis at Idaho National Laboratory (INL). The probabilistic seismic hazard analysis will follow processes and procedures appropriate for the Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 study provided in U. S. Nuclear Regulatory Commission guidance, NUREG-2213. The SSHAC Level 3 probabilistic seismic hazard analysis (PSHA) is being conducted as an INL site-wide effort. The work plan was developed and revised by participants who will perform the SSHAC Level 3 PSHA. The work plan also includes the quality assurance requirements commensurate with American Society of Mechanical Engineers Nuclear Quality Assurance NQA 1 2008/2009 and 2011 Addendum for design nuclear facilities under the U.S. Department of Energy and U. S. Nuclear Regulatory Commission. The scope of the SSHAC Level 3 PSHA is designed to produce the mean-centered definition of the seismic hazard with the appropriate treatment of uncertainties supported by the associated technical justifications. The SSHAC Level 3 study will properly implement and completely document the activities of evaluation, integration, and documentation. The evaluation process will consider data, models, and methods proposed by the larger technical community that are relevant to the PSHA at INL. The integration process will build seismic-source and ground-motion characterization models that represent the center, body, and range of technically defensible interpretations, taking into consideration the outcomes of the evaluation process. Key tasks and activities—such as a project kickoff meeting, three workshops, four formal working meetings, and a briefing meeting with the Participatory Peer Review Panel—will occur for the seven-member technical integration teams to construct seismic-source and ground-motion-characterization models. Both preliminary and final hazard-input documents will be developed to perform sensitivity analyses and the final PSHA, respectively. The entire study will be thoroughly documented in draft and final reports. The Participatory Peer Review Panel will review the SSHAC Level 3 study from start to finish, including the draft and final INL Site-wide PSHA reports, so they can provide a closure letter documenting the study’s adherence to SSHAC processes and technical adequacy of the final products.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Bi-objective approaches for home healthcare medical team planning and scheduling problem
In this paper, we study the medical team planning and scheduling in home healthcare within a weekly horizon, including the planning of nursing visits and the daily traveling route scheduling. Two objectives are considered: the first one is to minimize the total operation cost of the healthcare agency and the second one is to maximize the patient satisfaction. The problem is formulated as a mixed integer program, to seek for a trade-off between two objectives, with the characteristics of the patient requirements including nursing types, nursing frequency and service length considered. Medical team types, available service days and overwork penalty are also respected in the model. Then an ϵ-constraint method is adopted to obtain exact non-dominated solutions. To address large-scale problem instances, three heuristic approaches are developed to generate approximate Pareto fronts in a reasonable time. The set of non-dominated solutions are valuable for decision-making. Computational experiments are conducted and the results demonstrate the efficiency of the approaches.
97 MATHEMATICS AND COMPUTING↗