Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

A Preemption-Based Meta-Scheduling System for Distributed Computing

This research aims at designing and building a scheduling framework for distributed computing systems with the primary objectives of providing fast response times to the users, delivering high system throughput and accommodating maximum number of applications into the systems. The author claims that the above mentioned objectives are the most important objectives for scheduling in recent distributed computing systems, especially Grid computing environments. In order to achieve the objectives of the scheduling framework, the scheduler employs arbitration of application-level schedules and preemption of executing jobs under certain conditions. In application-level scheduling, the user develops a schedule for his application using an execution model that simulates the execution behavior of the application. Since application-level scheduling can seriously impede the performance of the system, the scheduling framework developed in this research arbitrates between different application-level schedules corresponding to different applications to provide fair system usage for all applications and balance the interests of different applications. In this sense, the scheduling framework is not a classical scheduling system, but a meta-scheduling system that interacts with the application-level schedulers. Due to the large system dynamics involved in Grid computing systems, the ability to preempt executing jobs becomes a necessity. The meta-scheduler described in this dissertation employs well defined scheduling policies to preempt and migrate executing applications. In order to provide the users with the capability to make their applications preemptible, a user-level check-pointing library called SRS (Stop-Restart Software) was also developed by this research. The SRS library is different from many user-level check-pointing libraries since it allows reconfiguration of applications between migrations. This reconfiguration can be achieved by changing the processor configuration and/or data distribution. The experimental results provided in this dissertation demonstrates the utility of the metascheduling framework for distributed computing systems. And lastly, the metascheduling framework was put to practical use by building a Grid computing system called GradSolve. GradSolve is a flexible system and it allows the application library writers to upload applications with different capabilities into the system. GradSolve is also unique with respect to maintaining traces of the execution of the applications and using the traces for subsequent executions of the application

PartRePer-MPI: Combining Fault Tolerance and Performance for MPI Applications

As we have entered Exascale computing, the faults in high-performance systems are expected to increase considerably. To compensate for a higher failure rate, the standard checkpoint/restart technique would need to create checkpoints at a much higher frequency resulting in an excessive amount of overhead which would not be sustainable for many scientific applications. Replication allows for fast recovery from failures by simply dropping the failed processes and using their replicas to continue the regular operation of the application. In this paper, we have implemented PartRePer-MPI, a novel fault-tolerant MPI library that adopts partial replication of some of the launched MPI processes in order to provide resilience from failures. The novelty of our work is that it combines both fault tolerance, due to the use of the User Level Failure Mitigation (ULFM) framework in the Open MPI library, and high performance, due to the use of communication protocols in the native MPI library that is generally fine-tuned for specific HPC platforms. We have implemented efficient and parallel communication strategies with computational and replica processes, and our library can seamlessly provide fault tolerance support to an existing MPI application. Our experiments using seven NAS Parallel Benchmarks and two scientific applications show that the failure-free overheads in PartRePer-MPI when compared to the baseline MVAPICH2, are only up to 6.4% for the NAS parallel benchmarks and up to 9.7% for the scientific applications

Holistic Slowdown Driven Scheduling and Resource Management for Malleable Jobs

In job scheduling, the concept of malleability has been explored since many years ago. Research shows that malleability improves system performance, but its utilization in HPC never became widespread. The causes are the difficulty in developing malleable applications, and the lack of support and integration of the different layers of the HPC software stack. However, in the last years, malleability in job scheduling is becoming more critical because of the increasing complexity of hardware and workloads. In this context, using nodes in an exclusive mode is not always the most efficient solution as in traditional HPC jobs, where applications were highly tuned for static allocations, but offering zero flexibility to dynamic executions. This paper proposes a new holistic, dynamic job scheduling policy, Slowdown Driven (SD-Policy), which exploits the malleability of applications as the key technology to reduce the average slowdown and response time of jobs. SD-Policy is based on backfill and node sharing. It applies malleability to running jobs to make room for jobs that will run with a reduced set of resources, only when the estimated slowdown improves over the static approach. We implemented SD-Policy in SLURM and evaluated it in a real production environment, and with a simulator using workloads of up to 198K jobs. Results show better resource utilization with the reduction of makespan, response time, slowdown, and energy consumption, up to respectively 7%, 50%, 70%, and 6%, for the evaluated workloads