A Queue Simulation Tool for a High Performance Scientific Computing Center

The NASA Center for Computational Sciences (NCCS) at the Goddard Space Flight Center provides high performance highly parallel processors, mass storage, and supporting infrastructure to a community of computational Earth and space scientists. Long running (days) and highly parallel (hundreds of CPUs) jobs are common in the workload. NCCS management structures batch queues and allocates resources to optimize system use and prioritize workloads. NCCS technical staff use a locally developed discrete event simulation tool to model the impacts of evolving workloads, potential system upgrades, alternative queue structures and resource allocation policies

Backfilling with fairness and slack for parallel job scheduling

Parallel jobs have different runtimes and numbers of threads/processes. Thus, scheduling parallel jobs involves a packing problem. If jobs are packed as tightly as possible, utilization will be improved. Otherwise, some resources have to stay idle. The common solution to deal with idle resources is backfilling, which schedule smaller jobs submitted later to execute earlier as long as they do not postpone the first job or all the previous jobs in the waiting queue. Traditionally, backfilling uses first fit for idle resources, according to the submission order. However, in this case, better packing of jobs could be missed. Hence, we propose an algorithm which looks further ahead if significantly improving utilization. However at the same time, this could be unfair to some jobs ahead in the queue. So we use a delay factor as a constraint to limit unfairness. We propose a branch and bound algorithm which selects jobs for backfilling which keep utilization high, while trying to stay close to First-Come-First-Served (FCFS). We evaluate relative response time and utilization and compare to other backfilling approaches. The selection of jobs for backfilling to optimize for high utilization and low delay is implemented as an extension of the existing Scojo-PECT preemptive scheduler

ANALYZING SUPERCOMPUTER UTILIZATION UNDER QUEUING WITH A PRIORITY FORMULA AND A STRICT BACKFILL POLICY

Supercomputers have become increasingly important in recent years due to the growing amount of data available and the increasing demand for quicker results in the scientific community. Since supercomputers carry a high cost to build and maintain, efficiency becomes more important to the owners, administrators, and users of these supercomputers. One important factor in determining the efficiency of a supercomputer is the scheduling of jobs that are submitted by users of the system. Previous work has dealt with optimizing the schedule on the system’s end while the users are blinded from the process. The work presented in this thesis investigates a scheduling system that is implemented at the Oak Ridge National Laboratory (ORNL) supercomputer Kraken with a backfilling policy and attempts to outline the optimal methods from the user’s point of view in the scheduling system, along with using a simulation approach to optimize the priority formula. Normally the user has no idea which scheduling algorithms are used, but the users at ORNL not only know how the scheduling works but they can also view the current activity of the system. This gives an advantage to the users who are willing to benefit from this knowledge by utilizing some elementary game theory to optimize their strategies. The results will show a benefit to both the users, since they will be able to process their jobs sooner, and the system, since it will better utilized with little expense to the administrators, through competition. Queuing models and simulation have been well studied in almost all relevant aspects of the modern world. Higher efficiency is the goal of many researchers in several different fields; the supercomputer queues are no different. Efficient use of the resources makes the system administrator pleased while benefiting the users with more timely results. Studying these queuing models through simulation should help all parties involved by increasing utilization. The simulation will be validated and the utilization improvement will be measured and reported. User defined formulas will be developed for future users to help maximize utilization and minimize wait times

Grid-job scheduling with reservations and preemption

Computational grids make it possible to exploit grid resources across multiple clusters when grid jobs are deconstructed into tasks and allocated across clusters. Grid-job tasks are often scheduled in the form of workflows which require synchronization, and advance reservation makes it easy to guarantee predictable resource provisioning for these jobs. However, advance reservation for grid jobs creates roadblocks and fragmentation which adversely affects the system utilization and response times for local jobs. We provide a solution which incorporates relaxed reservations and uses a modified version of the standard grid-scheduling algorithm, HEFT, to obtain flexibility in placing reservations for workflow grid jobs. Furthermore, we deploy the relaxed reservation with modified HEFT as an extension of the preemption based job scheduling framework, SCOJO-PECT job scheduler. In SCOJO-PECT, relaxed reservations serve the additional purpose of permitting scheduler optimizations which shift the overall schedule forward. Furthermore, a propagation heuristics algorithm is used to alleviate the workflow job makespan extension caused by the slack of relaxed reservation. Our solution aims at decreasing the fragmentation caused by grid jobs, so that local jobs and system utilization are not compromised, and at the same time grid jobs also have reasonable response times

Energy-Efficient, Thermal-Aware Modeling and Simulation of Datacenters: The CoolEmAll Approach and Evaluation Results

International audienceThis paper describes the CoolEmAll project and its approach for modeling and simulating energy-efficient and thermal-aware data centers. The aim of the project was to address energy-thermal efficiency of data centers by combining the optimization of IT, cooling and workload management. This paper provides a complete data center model considering the workload profiles, the applications profiling, the power model and a cooling model. Different energy efficiency metrics are proposed and various resource management and scheduling policies are presented. The proposed strategies are validated through simulation at different levels of a data cente

"Virtual malleability" applied to MPI jobs to improve their execution in a multiprogrammed environment"

This work focuses on scheduling of MPI jobs when executing in shared-memory multiprocessors (SMPs). The objective was to obtain the best performance in response time in multiprogrammed multiprocessors systems using batch systems, assuming all the jobs have the same priority. To achieve that purpose, the benefits of supporting malleability on MPI jobs to reduce fragmentation and consequently improve the performance of the system were studied. The contributions made in this work can be summarized as follows:· Virtual malleability: A mechanism where a job is assigned a dynamic processor partition, where the number of processes is greater than the number of processors. The partition size is modified at runtime, according to external requirements such as the load of the system, by varying the multiprogramming level, making the job contend for resources with itself. In addition to this, a mechanism which decides at runtime if applying local or global process queues to an application depending on the load balancing between processes of it. · A job scheduling policy, that takes decisions such as how many processes to start with and the maximum multiprogramming degree based on the type and number of applications running and queued. Moreover, as soon as a job finishes execution and where there are queued jobs, this algorithm analyzes whether it is better to start execution of another job immediately or just wait until there are more resources available. · A new alternative to backfilling strategies for the problema of window execution time expiring. Virtual malleability is applied to the backfilled job, reducing its partition size but without aborting or suspending it as in traditional backfilling. The evaluation of this thesis has been done using a practical approach. All the proposals were implemented, modifying the three scheduling levels: queuing system, processor scheduler and runtime library. The impact of the contributions were studied under several types of workloads, varying machine utilization, communication and, balance degree of the applications, multiprogramming level, and job size. Results showed that it is possible to offer malleability over MPI jobs. An application obtained better performance when contending for the resources with itself than with other applications, especially in workloads with high machine utilization. Load imbalance was taken into account obtaining better performance if applying the right queue type to each application independently.The job scheduling policy proposed exploited virtual malleability by choosing at the beginning of execution some parameters like the number of processes and maximum multiprogramming level. It performed well under bursty workloads with low to medium machine utilizations. However as the load increases, virtual malleability was not enough. That is because, when the machine is heavily loaded, the jobs, once shrunk are not able to expand, so they must be executed all the time with a partition smaller than the job size, thus degrading performance. Thus, at this point the job scheduling policy concentrated just in moldability.Fragmentation was alleviated also by applying backfilling techniques to the job scheduling algorithm. Virtual malleability showed to be an interesting improvement in the window expiring problem. Backfilled jobs even on a smaller partition, can continue execution reducing memory swapping generated by aborts/suspensions In this way the queueing system is prevented from reinserting the backfilled job in the queue and re-executing it in the future.Postprint (published version

Development of Data-Driven Dispatching Heuristics for Heterogeneous HPC Systems

Nell’ambito dei sistemi High-Performance Computing, l'uso di euristiche di dispatching efficaci, per lo scheduling e l'allocazione dei jobs in arrivo, è fondamentale al fine di ottenere buoni livelli di Quality of Service. In questo elaborato ci concentreremo sul design e l’analisi di euristiche di allocazione delle risorse, che saranno progettate per sistemi HPC eterogenei, nei quali i nodi possono essere equipaggiati con diverse tipologie di unità di elaborazione. Impiegheremo poi euristiche data-driven per la predizione della durata dei jobs, e valuteremo il tutto dal punto di vista del throughput di sistema. Considereremo in particolare Eurora, un sistema HPC eterogeneo realizzato da CINECA, oltre che un workload catturato dal relativo log di sistema, contenente jobs reali inviati dagli utenti. Tutto ciò è stato possibile grazie ad AccaSim, un simulatore di sistemi HPC sviluppato nel Dipartimento di Informatica - Scienza e Ingegneria (DISI) dell’Università di Bologna, ed al quale si è contribuito in modo sostanziale. Quest’elaborato mostra che l’impatto di diverse euristiche di allocazione sul throughput di un sistema HPC eterogeneo non è trascurabile, con variazioni in grado di raggiungere picchi di un ordine di grandezza, e più pronunciate considerando brevi intervalli temporali, dell'ordine dei mesi. Abbiamo inoltre osservato che l’impiego di euristiche per la predizione della durata dei jobs è di grande beneficio al throughput su tutte le euristiche di allocazione, e specialmente su quelle che integrano in maniera più profonda tali elementi data-driven. Infine, l’analisi effettuata ha permesso di caratterizzare integralmente il sistema Eurora ed il relativo workload, permettendoci di comprendere al meglio gli effetti su di esso dei diversi metodi di dispatching, nonché di estendere le nostre considerazioni anche ad altre classi di sistemi

Resource allocation model for grid computing environment

Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and places it in the waiting queue. When the resource is released, the scheduler selects the job from the waiting queue with a specific algorithm. Requests will be rejected if the required resources are not available. The user can re-submit a new request by modifying the parameter until available resources can be found. Eventually, there is a decrease in idle resources between work and resource utilization, and the waiting time will increase. An effective scheduling policy is required to improve resource use and reduce waiting times. In this paper, the FCFS-LRH method is proposed, where jobs received will be sorted by arrival time, execution time, and the number of resources needed. After the sorting process, the work will be placed in a logical view, and the job will be sent to the actual resource when it executes. The experimental results show that the proposed model can increase resource utilization by 1.34% and reduce waiting time by 20.47% when compared to existing approaches. This finding could be beneficially implemented in cloud systems resource allocation management