An official website of the United States government
Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system.
more »
« less
On the Feasibility of Simulation-driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems
Runtime systems that automate the execution of applications on distributed cyberinfrastructures need to make scheduling deci- sions. Researchers have proposed many scheduling algorithms, but most of them are designed based on analytical models and assumptions that may not hold in practice. The literature is thus rife with algorithms that have been evaluated only within the scope of their underlying as- sumptions but whose practical effectiveness is unclear. It is thus difficult for developers to decide which algorithm to implement in their runtime systems. To obviate the above difficulty, we propose an approach by which the runtime system executes, throughout application execution, simulations of this very execution. Each simulation is for a different algorithm in a scheduling algorithm portfolio, and the best algorithm is selected based on simulation results. The main objective of this work is to evaluate the feasibility and potential merit of this portfolio scheduling approach, even in the presence of simulation inaccuracy, when compared to the traditional one-algorithm approach. We perform this evaluation via a case study in the context of scientific workflows. Our main finding is that portfolio scheduling can outperform the best one-algorithm approach even in the presence of relatively large simulation inaccuracies.
more »
« less
- PAR ID:
- 10331054
- Date Published:
- Journal Name:
- Job Scheduling Strategies for Parallel Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Algorithms to solve hard combinatorial problems often exhibit complementary performance, i.e. where one algorithm fails, another shines. Algorithm portfolios and algorithm selection take advantage of this by running all algorithms in parallel or choosing the best one to run on a problem instance. In this paper, we show that neither of these approaches gives the best possible performance and propose the happy medium of running a subset of all algorithms in parallel. We propose a method to choose this subset automatically for each problem instance, and demonstrate empirical improvements of up to 19% in terms of runtime, 81% in terms of misclassification penalty, and 26% in terms of penalized averaged runtime on scenarios from the ASlib benchmark library. Unlike all other algorithm selection and scheduling approaches in the literature, our performance measures are based on the actual performance for algorithms running in parallel rather than assuming overhead-free parallelization based on sequential performance. Our approach is easy to apply in practice and does not require to solve hard problems to obtain a schedule, unlike other techniques in the literature, while still delivering superior performance.more » « less
-
While High Performance Computing systems are increasingly based on heterogeneous cores, their e ectiveness depends on how well the scheduler can allocate workloads onto appropriate computing devices and how communication and computation can be overlapped. With di erent types of resources integrated into one system, the complexity of the scheduler correspondingly increases. Moreover, for applications with varying problem sizes on di erent heterogeneous resources, the optimal scheduling approach may vary accordingly. We thus present PDAWL, an event-driven pro le-based Iterative Dynamic Adaptive Work-Load balance scheduling approach to dynamically and adaptively adjust workload to eciently utilize heterogeneous resources. It combines online scheduling (DAWL), which can adaptively adjust workload based on available real time heterogeneous resources, with an oine machine learning (pro lebased estimation model) which can build a device-speci c communication computation estimation model. Our scheduling approach is tested on control-regular applications, Stencil kernel (based on a Jacobi Algorithm) and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven runtime system. Experimental results show that PDAWL is either on-par or far outperforms whichever yields the best results (CPU or GPU).more » « less
-
Real-time data stream processing at the edge is crucial for time-sensitive tasks within large-scale IoT systems. Task scheduling plays a key role in managing the Quality of Service (QoS), necessitating a prioritization system to distinguish between high and low-priority tasks, thus ensuring efficient data processing on edge nodes. Existing scheduling algorithms rigidly prioritize tasks deemed as high-priority, often at the expense of fairness and overall system efficiency. In this paper, we propose a Priority-aware Fair Task Scheduling (FTS-Hybrid) algorithm that addresses these challenges by managing priority based task execution in a controlled manner. Our task scheduling algorithm streamlines resource utilization and enhances system responsiveness, contributing to low latency and high throughput, outperforming competing techniques including First-Come-FirstServe (FCFS), Round Robin (RR), and Priority Scheduling (PS). We implemented FTS-Hybrid on Apache Storm and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Experimental results show that the FTS-Hybrid algorithm reduces task execution latency by 24%, 31%, and 26% compared with FCFS, RR, and PS, respectively, by strategically mitigating queuing delays under dynamic workload conditions.more » « less
-
Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
