Modeling of an Adaptive Parallel System with Malleable Applications in a Distributed Computing Environment

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost

Adaptive space-time sharing with SCOJO.

Coscheduling is a technique used to improve the performance of parallel computer applications under time sharing, i.e., to provide better response times than standard time sharing or space sharing. Dynamic coscheduling and gang scheduling are two main forms of coscheduling. In SCOJO (Share-based Job Coscheduling), we have introduced our own original framework to employ loosely coordinated dynamic coscheduling and a dynamic directory service in support of scheduling cross-site jobs in grid scheduling. SCOJO guarantees effective CPU shares by taking coscheduling effects into consideration and supports both time and CPU share reservation for cross-site job. However, coscheduling leads to high memory pressure and still involves problems like fragmentation and context-switch overhead, especially when applying higher multiprogramming levels. As main part of this thesis, we employ gang scheduling as more directly suitable approach for combined space-time sharing and extend SCOJO for clusters to incorporate adaptive space sharing into gang scheduling. We focus on taking advantage of moldable and malleable characteristics of realistic job mixes to dynamically adapt to varying system workloads and flexibly reduce fragmentation. In addition, our adaptive scheduling approach applies standard job-scheduling techniques like a priority and aging system, backfilling or easy backfilling. We demonstrate by the results of a discrete-event simulation that this dynamic adaptive space-time sharing approach can deliver better response times and bounded relative response times even with a lower multiprogramming level than traditional gang scheduling.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H825. Source: Masters Abstracts International, Volume: 43-01, page: 0237. Adviser: A. Sodan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Modeling of an Adaptive Parallel System with Malleable Applications in a Distributed Computing Environment

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost

Scheduling Rigid, Evolving Applications on Homogeneous Resources

Classical applications executed on clusters or grids are either rigid/moldable or workflow-based. However, the increase of resource computing and storage capabilities has leveraged more complex applications. For example, some code coupling applications exhibit changing resource requirements without being a workflow. Executing them on current batch schedulers leads to an inefficient resource usage, as a block of resources has to be reserved for the whole duration of the application. This paper studies the problem of offline scheduling of rigid and evolving applications on homogeneous resources. It proposes several scheduling algorithms and evaluates them based on simulations. Results show that significant makespan and resource usage improvement can be achieved with short scheduling computing time

Decentralized Online Scheduling of Malleable NP-hard Jobs

In this work, we address an online job scheduling problem in a large distributed computing environment. Each job has a priority and a demand of resources, takes an unknown amount of time, and is malleable, i.e., the number of allotted workers can fluctuate during its execution. We subdivide the problem into (a) determining a fair amount of resources for each job and (b) assigning each job to an according number of processing elements. Our approach is fully decentralized, uses lightweight communication, and arranges each job as a binary tree of workers which can grow and shrink as necessary. Using the NP-complete problem of propositional satisfiability (SAT) as a case study, we experimentally show on up to 128 machines (6144 cores) that our approach leads to near-optimal utilization, imposes minimal computational overhead, and performs fair scheduling of incoming jobs within a few milliseconds

Towards Scheduling Evolving Applications

International audienceMost high-performance computing resource managers only allow applications to request a static allocation of resources. However, evolving applications have resource requirements which change (evolve) during their execution. Currently, such applications are forced to make an allocation based on their peak resource requirements, which leads to an inefficient resource usage. This paper studies whether it makes sense for resource managers to support evolving applications. It focuses on scheduling fully-predictably evolving applications on homogeneous resources, for which it proposes several algorithms and evaluates them based on simulations. Results show that resource usage and application response time can be significantly improved with short scheduling times

Idle regulation in non-clairvoyant scheduling of parallel jobs

AbstractThe optimization of parallel applications is difficult to achieve by classical optimization techniques because of their diversity and the variety of actual parallel and distributed platforms and/or environments. Adaptive algorithmic schemes, capable of dynamically changing the allocation of jobs during the execution to optimize global system behavior, are the best alternatives for solving this problem. In this paper, we focus on non-clairvoyant scheduling of parallel jobs with known resource requirements but unknown running times, with emphasis on the regulation of idle periods in the context of general list policies. We consider a new family of scheduling strategies based on two phases which successively combine sequential and parallel execution of jobs. We generalize known worst-case performance bounds by considering two extra parameters, in addition to the number of processors and maximum processor requirements considered in the literature, namely, job parallelization penalty and idle regulation factor. Furthermore, we prove that under certain conditions of idle regulation, the performance guarantee of parallel job scheduling in space-sharing mode can be improved

An RMS for Non-predictably Evolving Applications

International audienceNon-predictably evolving applications are applications that change their resource requirements during execution. These applications exist, for example, as a result of using adaptive numeric methods, such as adaptive mesh refinement and adaptive particle methods. Increasing interest is being shown to have such applications acquire resources on the fly. However, current HPC Resource Management Systems (RMSs) only allow a static allocation of resources, which cannot be changed after it started. Therefore, non-predictably evolving applications cannot make efficient use of HPC resources, being forced to make an allocation based on their maximum expected requirements. This paper presents CooRMv2, an RMS which supports efficient scheduling of non-predictably evolving applications. An application can make "pre-allocations" to specify its peak resource usage. The application can then dynamically allocate resources as long as the pre-allocation is not outgrown. Resources which are pre-allocated but not used, can be filled by other applications. Results show that the approach is feasible and leads to a more efficient resource usage

"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