LOMARC: Look ahead matchmaking for multi-resource coscheduling.

Hyper-Threading (HT) provides a new possibility for job coscheduling without context switch and without the cost for coordinating processes of one parallel job. However, HT achieves high processor throughput at the expense of reducing the performance of the individual process. Since the hardware resources are actually shared between two coscheduled jobs, the resource contention will harm the performance of each job. Most scheduling approaches only focus on the CPU without considering the impact on other resources. In this thesis we present LOMARC, a space-time sharing approach that takes multiple resources, including CPU, I/O, memory and network, into consideration for job coscheduling on HT processors. To improve resource utilization and reduce job response times, LOMARC matches two jobs with complementary resource requirements to coschedule. Our approach partially reorders the waiting job queue by lookahead to increase the possibility of finding a good match. LOMARC also generalizes for standard CPUs, using an adjusted matching scheme and only focusing on hiding I/O latency. In addition, LOMARC incorporates standard scheduling approaches such as priority ordering, aging and backfilling. In our simulation experiment, we use a realistic workload model to provide the convincing results. Our experimental results demonstrate that LOMARC delivers better performance than the standard space sharing approach and the other two job coscheduling approaches for HT processors. The performance gain is mainly due to an increased possibility of coscheduling two complementary jobs by looking ahead on the waiting queue. Source: Masters Abstracts International, Volume: 43-01, page: 0239. Adviser: Angela Sodan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Scheduling Heterogeneous HPC Applications in Next-Generation Exascale Systems

Next generation HPC applications will increasingly time-share system resources with emerging workloads such as in-situ analytics, resilience tasks, runtime adaptation services and power management activities. HPC systems must carefully schedule these co-located codes in order to reduce their impact on application performance. Among the techniques traditionally used to mitigate the performance effects of time- share systems is gang scheduling. This approach, however, leverages global synchronization and time agreement mechanisms that will become hard to support as systems increase in size. Alternative performance interference mitigation approaches must be explored for future HPC systems. This dissertation evaluates the impacts of workload concurrency in future HPC systems. It uses simulation and modeling techniques to study the performance impacts of existing and emerging interference sources on a selection of HPC benchmarks, mini-applications, and applications. It also quantifies the cost and benefits of different approaches to scheduling co-located workloads, studies performance interference mitigation solutions based on gang scheduling, and examines their synchronization requirements. To do so, this dissertation presents and leverages a new Extreme Value Theory- based model to characterize interference sources, and investigate their impact on Bulk Synchronous Parallel (BSP) applications. It demonstrates how this model can be used to analyze the interference attenuation effects of alternative fine-grained OS scheduling approaches based on periodic real time schedulers. This analysis can, in turn, guide the design of those mitigation techniques by providing tools to understand the tradeoffs of selecting scheduling parameters

LBSim: A simulation system for dynamic load-balancing algorithms for distributed systems.

In a distributed system consisting of autonomous computational units, the total computational power of all the units needs to be utilized efficiently by applying suitable load-balancing policies. For accomplishing the task, a large number of load balancing algorithms have been proposed in the literature. To facilitate the performance study of each of these load-balancing strategies, simulation has been widely used. However comparison of the load balancing algorithms becomes difficult if a different simulator is used for each case. There have been few studies on generalized simulation of load-balancing algorithms in distributed systems. Most of the simulation systems address the experiments for some particular load-balancing algorithms, whereas this thesis aims to study the simulation for a broad range of algorithms. After the characterization of the distributed systems and the extraction of the common components of load-balancing algorithms, a simulation system, called LBSim, has been built. LBSim is a generalized event-driven simulator for studying load-balancing algorithms with coarse-grained applications running on distributed networks of autonomous processing nodes. In order to verify that the simulation model can represent actual systems reasonably well, we have validated LBSim both qualitatively and quantitatively. As a toolkit of simulation, LBSim programming libraries can be reused to implement load-balancing algorithms for the purpose of performance measurement and analysis from different perspectives. As a framework of algorithm simulation can be extended with a moderate effort by following object-oriented methodology, to meet any new requirements that may arise in the future.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .D8. Source: Masters Abstracts International, Volume: 43-05, page: 1747. Adviser: A. K. Aggarwal. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Scheduling of Hard Real-Time Multi-Thread Periodic Tasks

In this paper we study the scheduling of parallel and real-time recurrent tasks. Firstly, we propose a new parallel task model which allows recurrent tasks to be composed of several threads, each thread requires a single processor for execution and can be scheduled simultaneously. Secondly, we define several kinds of real-time schedulers that can be applied to our parallel task model. We distinguish between two scheduling classes: hierarchical schedulers and global thread schedulers. We present and prove correct an exact schedulability test for each class. Lastly, we also evaluate the performance of our scheduling paradigm in comparison with Gang scheduling by means of simulations

Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

In this paper, we address the industrial challenge put forth by ARM in ECRTS 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use micro-architectural denial-of-service (DoS) attacks as aggressor tasks of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application, which results in significant deviations of the estimated trajectories from the ground truth, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. We show that dynamic LLC (or DRAM depending on the aggressor) bandwidth throttling of the aggressor tasks is an effective mean to ensure real-time performance of the AR-HUD application without resorting to over-provisioning the system