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

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

Extending Scojo-PECT by migration based on system-level checkpointing

In recent years, a significant amount of research has been done on job scheduling in high performance computing area. Parallel jobs have different running time and require a different number of processors, thus jobs need to be scheduled and packed to improve system utilization. Scojo-PECT is a job scheduler which provides service guarantees by using coarse-grain time sharing. However, Scojo-PECT does not provide process migration. We extend the Scojo-PECT by migrating parallel jobs based on system-level checkpointing. We investigate different cases in the Scojo-PECT scheduling algorithm where migration based on system-level checkpointing can be used to improve resource utilization and reduce job response time. Our experimental results show reduction of relative response times on medium jobs over the results of the original Scojo-PECT scheduler and the long jobs do not suffer any disadvantage

Investigation of cluster and cluster queuing system

Cluster became main platform as parallel and distributed computing structure for high performance computing. Following the development of high performance computer architecture more and more different branches of natural science benefit fromhuge and efficient computational power. For instance bio-informatics, climate science, computational physics, computational chemistry, marine science, etc. Efficient and reliable computing powermay not only expending demand of existing high performance computing users but also attracting more and more different users. Efficiency and performance are main factors on high performance computing. Most of the high performance computer exists as computer cluster. Computer clustering is the popular and main stream of high-performance computing. Discover the efficiency of high performance computing or cluster is very interesting and never enough as it is really depending on different users. Monitoring and tuning high performance or cluster facilities are always necessary. This project focuses on high performance computer monitoring. Comparing queuing status and work load on different computing nodes on the cluster. As the power consumption is main issue nowadays, our project will also try to estimate power consumption on these special sites and also try to support our way of doing estimation.Master i nettverks- og systemadministrasjo