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Exascale workflow applications and middleware: An ExaWorks retrospective

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING applications 2025年

作者： Alsaadi, Aymen Hategan-Marandiuc, Mihael Maheshwari, Ketan Merzky, Andre Titov, Mikhail Turilli, Matteo Wilke, Andreas Wozniak, Justin M. Chard, Kyle da Silva, Rafael Ferreira Jha, Shantenu Laney, Daniel Rutgers State Univ State Univ New Jersey RAD Grp Piscataway NJ USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA Univ Chicago Comp Sci Dept Chicago IL USA Oak Ridge Natl Lab NCCS Div Oak Ridge TN USA Incomputable LLC Highland Pk NJ USA Brookhaven Natl Lab Computat Sci Dept Upton NY USA Princeton Plasma Phys Lab Computat Sci DOEs Princeton NJ USA Princeton Univ Comp Sci Princeton NJ USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA USA

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

关键词： Exascale HPC workflows workflow interoperability SDK ECP workflow applications middleware building blocks workflow community initiative

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Safety-Critical Task Offloading Heuristics for workflow applications in Mobile Edge Computing

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第11期32卷

作者： Wang, Yushen Sun, Tianwen Yang, Guang Yang, Kai Song, Xuefei Zheng, Changling China Mobile Grp Jiangsu Co Ltd 59 Huju Rd Nanjing 210029 Jiangsu Peoples R China China Mobile Res Inst 32 Xuanwumen West St Beijing 100053 Peoples R China

As the fundamental mechanism in mobile edge computing (MEC), task offloading strategy is of great significance to the quality of computing services provided by MEC systems. When coping with workflow applications, the precedence relations among tasks increase the difficulty in developing task offloading strategies. This paper studies the problem of safety-critical task offloading for workflow applications in a MEC environment. Considering the precedence constraints on workflow tasks and the overhead of security services, we formulate the safety-critical workflow offloading model with the objective of jointly optimizing the total completion time and energy consumption. By using a task sequence to represent a feasible solution to the optimization model, we introduce a family of heuristics to solve the safety-critical workflow offloading problem under precedence constraints upon workflow tasks. Depending on whether the offloading solution satisfies the precedence relations among workflow tasks, task sequences can be classified into two categories, i.e., precedence-aware and precedence-unaware offloading solutions. With the satisfaction of precedence constraints, a family of heuristics by using a precedence-aware strategy and a precedence-unaware strategy is designed to offload safety-critical workflow tasks. Given an offloading sequence and the operating conditions of MEC servers, the heuristic algorithms select the currently best MEC server to offload workflow tasks. Experimental results justify the performance of the proposed algorithms in solving the safety-critical workflow offloading problem under precedence constraints.

关键词： Mobile edge computing task offloading safety-criticality workflow applications

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Score-based genetic algorithm for scheduling workflow applications in clouds

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2016年第4期7卷 272-284页

作者： Singh, Lovejit Singh, Sarbjeet Panjab Univ Univ Inst Engn & Technol Comp Sci & Engn Chandigarh 160014 India

Cloud service providers offer services to customers through the internet. They use economic models to charge customers for using their resources. Many applications require workflow processing for their execution. In workflow processing, there are dependencies among various tasks, and parent tasks are executed before child tasks. There are various criteria on the basis of which resources are allocated to workflow applications such as time, cost, reliability etc. In this paper, a score-based genetic algorithm is proposed which allocates resources to workflow application tasks by taking into consideration three important factors: score of the machine, makespan and cost. The algorithm allocates those virtual instances to workflow application tasks that result in meeting user-defined deadlines as well as budget. It also reduces the failure rate of workflow applications using a score manager. The performance of the score-based genetic algorithm has been compared with a simple genetic algorithm on the basis of time, cost and failure rate. Different workflows such as CyberShake, Montage, SIPHT and Epigenomics have been executed to evaluate the performance of both scheduling algorithms. The results indicate that the score-based genetic algorithm performs better than the simple genetic algorithm in terms of reducing makespan, failure rate and cost.

关键词： cloud computing workflow applications genetic algorithms

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A cost, time, energy-aware workflow scheduling using adaptive PSO algorithm in a cloud-fog environment

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COMPUTING 2024年第10期106卷 3279-3308页

作者： Singh, Gyan Chaturvedi, Amit K. Govt Engn Coll Dept Comp Applicat Badliya Circle NH-08 Ajmer 305001 Rajasthan India

Recent years have seen an exponential rise in data produced by Internet of Things (IoT) applications. Cloud servers were not designed for such extensive data, leading to challenges like increased makespan, cost, bandwidth, energy consumption, and network latency. To address these, the cloud-fog environment has emerged as an extension to cloud servers, offering services closer to IoT devices. Scheduling workflow applications to optimize multiple conflicting objectives in cloud fog is an NP-hard problem. Particle Swarm Optimization (PSO) is a good choice for multi-objective solutions due to its simplicity and rapid convergence. However, it has shortcomings like premature convergence and stagnation. To address these challenges, we formalize a theoretical background for scheduling workflow applications in the cloud-fog environment with multiple conflicting objectives. Subsequently, we propose an adaptive particle swarm optimization (APSO) algorithm with novel enhancements, including an S-shaped sigmoid function to dynamically decrease inertia weight and a linear updating mechanism for cognitive factors. Their integration in cloud-fog environments has not been previously explored. This novel application addresses unique challenges of workflow scheduling in cloud-fog systems, such as heterogeneous resource management, energy consumption, and increased cost. The effectiveness of APSO is evaluated using a real-world scientific workflow in a simulated cloud-fog environment and compared with four meta-heuristics. Our proposed workflow scheduling significantly reduces makespan and energy consumption without compromising overall cost compared to other meta-heuristics.

关键词： Cloud-fog computing Direct acyclic graph Particle swarm optimization workflow applications Makespan and energy

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A Meta-Heuristics-Based Solution for Multi-Objective workflow Scheduling in Fog Computing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2024年第9期15卷 989-1002页

作者： Singh, Gyan Dubey, Vivek Govt Engn Coll Dept Comp Applicat Ajmer 305001 India

recent years, there has been a significant increase in the volume of data generated by Internet of Things (IoT) applications, mostly driven by the rapid proliferation of IoT devices and advancements in communication technologies. The conventional cloud computing network was not specifically built to handle such a vast volume of data, leading to several issues, including increased processing time, higher costs, larger bandwidth usage, increased power usage, and communication delays. As a solution, conventional cloud servers have been expanded to additional layers of computing, storage, and network, termed as cloud-fog computing. The cloud-fog computing provides storage, processing, networking, and analytics capabilities in close proximity to IoT devices. The problem of scheduling workflow applications in cloud-fog environments to optimize several conflicting objectives is classified as computationally complex. Particle Swarm Optimization is the widely recognized evolutionary meta-heuristic and is the optimal method for implementing multi-objective solutions because of its user-friendly approach and quick converging capability. Despite its wide acceptance, it does have several drawbacks, such as early convergence and solution stagnation. In order to overcome these limitations, this paper establishes a comprehensive theoretical model to schedule workflow applications for cloud-fog systems. The proposed model employs various competing objectives, such as power usage, overall cost, and makespan. To achieve this, we introduce a novel algorithm, learning enhanced particle swarm optimization (LEPSO), which incorporates an inverse tangent inertia weight policy and adaptive learning factor methods. The efficiency of the LEPSO is subsequently assessed by employing an operational data set of scientific workflow applications within a cloudsim-based simulation and validated against GAMPSO, EMMOO, PSO, and GA state-of-the-art approaches. The workflow scheduling, we suggest achieve

关键词： Fog computing DAG workflow applications makespan energy PSO

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Optimal energy consumption and throughput for workflow applications on distributed architectures

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2014年第1期4卷 44-51页

作者： Ben Othman, Abdallah Nicod, Jean-Marc Philippe, Laurent Rehn-Sonigo, Veronika UTBM FEMTO ST Inst CNRS UFCENSMM Besancon France

In this article we study both the throughput and the energy optimization problems for a distributed system subject to failures that executes a workflow at different speed levels. The application is modeled as a directed acyclic graph composed of typed tasks linked by dependency constraints. A continuous flow, or a great number of application instances, has to be processed. Optimizing the collaborative system performance implies to increase the throughput - the number of application instances processed by time unit - or to decrease the period - the time needed to output one instance of the system. The system is designed as a collaborative platform of distributed machines. Each machine collaborates with others by performing all the instances of at least one task of the DAG. The problem we tackle is to optimize the configuration of the platform. In this paper we propose two polynomial algorithms that optimize the two objectives of period (i.e., throughput) minimization and energy minimization. We prove that the proposed algorithms give optimal results. Our optimization approach is hierarchic in that we either minimize the energy consumption for the optimal period or minimize the period for the optimal energy consumption. Moreover a minor modification of our algorithms allows to compute the Pareto front between the two optimal solutions. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Scheduling workflow applications Energy minimization Fault tolerance Throughput maximization Polynomial complexity

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Knowledge Transfer Enabled Diverse Task Scheduling for Individualized Requirements in Industrial Cloud Platform

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9117-9129页

作者： Zhou, Jiajun Gao, Liang Lu, Chao Li, Yun China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China i4AI Ltd London WC1N 3AX England

Nowadays, application providers often prefer to execute their workflows on heterogeneous distributed computing resources deployed on cloud infrastructure to achieve a high level of resilience and cost saving. Optimally scheduling workflow on computing resources is a well-known combinatorial optimization problem, where a trend of using evolutionary algorithm (EA) is emerging rapidly. However, conventional EA optimizes only one problem in a single run and suffers from a high computational burden. In practical scenario, cloud platform needs to handle massive amounts of scheduling requests from users, scheduling different workflows simultaneously is highly challenging. Bearing this in mind, we put forward a novel knowledge transfer enabled EA to schedule diverse workflows in tandem, where domain knowledge of scheduling one workflow is extracted to enhance the scheduling efficiency of other related workflows. In our design, the knowledge source selection and the intensity of performing knowledge transfer are adapted in a synergistic way. Furthermore, search operator is enhanced by exploiting both historical experience and heuristic information. Experimental results on real-life workflows and extensive synthetic applications demonstrate the competitiveness of our approach, in comparison to state-of-the-art contenders.

关键词： Processor scheduling Job shop scheduling Knowledge transfer Optimization Multitasking Scheduling Cloud computing Search problems Schedules Vectors Industrial cloud platform workflow applications task scheduling knowledge transfer multi-task optimization

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Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第1期35卷 1-12页

作者： Lakhan, Abdullah Mohammed, Mazin Abed Ibrahim, Dheyaa Ahmed Abdulkareem, Karrar Hameed Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Anbar Coll Comp Sci & Informat Technol Anbar 31001 Iraq Imam Jaafar Al Sadiq Univ Commun Engn Tech Dept Informat Technol Collage Baghdad Iraq Al Muthanna Univ Coll Agr Samawah Iraq

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing pro-gressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors' performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to mini-mize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods min-imized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems. & COPY;2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Bio-inspired robotics Cost Searching Healthcare IoMT Blockchain Service cost workflow applications

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workflow scheduling of scientific workflows under simultaneous deadline and budget constraints

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND applications 2021年第4期24卷 3449-3467页

作者： Taghinezhad-Niar, Ahmad Pashazadeh, Saeid Taheri, Javid Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Karlstad Univ Dept Math & Comp Sci Karlstad Sweden

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers' and service providers' perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider both budget and deadline constraints for scheduling scientific workflows on cloud IaaS platforms. Methods for distribution of budget and deadlines under task leveling are proposed. Four metrics (success rate, time ratio, cost ratio, and utilization rate) are utilized to evaluate the proposed algorithms' performance. Results of our proposed algorithms are compared with the BDHEFT, DBCS, and BDSD algorithms under various scenarios. Simulation results demonstrate that BDDC outperforms other algorithms in achieving cheaper costs while earning a higher success rate and utilization rate, and BDC accomplishes higher success rates and faster makespan. The performance of the proposed methods is confirmed using a real cloud environment.

关键词： Scheduling workflow applications Deadline Budget Quality of services

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FaaScinating Resilience for Serverless Function Choreographies in Federated Clouds

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第3期19卷 2440-2452页

作者： Ristov, Sasko Kimovski, Dragi Fahringer, Thomas Univ Innsbruck Distributed & Parallel Syst Grp A-6020 Innsbruck Austria Klagenfurt Univ Inst Informat Technol A-9020 Klagenfurt Austria

Cloud applications often benefit from deployment on serverless technology Function-as-a-Service (FaaS), which may instantly spawn numerous functions and charges users for the period when serverless functions are running. Maximum benefit is achieved when functions are orchestrated in a workflow or function choreographies (FCs). However, many provider limitations specific for FaaS, such as maximum concurrency or duration often increase the failure rate, which can severely hamper the execution of entire FCs. Current support for resilience is often limited to function retries or try-catch, which are applicable within the same cloud region only. To overcome these limitations, we introduce rAFCL, a middleware platform that maintains reliability of complex FCs in federated clouds. In order to support resilient FC execution under rAFCL, our model creates an alternative strategy for each function based on the required availability specified by the user. Alternative strategies are not restricted to the same cloud region, but may contain alternative functions across five providers, invoked concurrently in a single alternative plan or executed subsequently in multiple alternative plans. With this approach, rAFCL offers flexibility in terms of cost-performance trade-off. We evaluated rAFCL by running three real-life applications across three cloud providers. Experimental results demonstrated that rAFCL outperforms the resilience of AWS Step Functions, increasing the success rate of entire FC by 53.45%, while invoking only 3.94% more functions with zero wasted function invocations. rAFCL significantly improves availability of entire FCs to almost 1 and survives even after massive failures of alternative functions.

关键词： FAA Resilience Codes Costs Schedules Runtime Python Availability disasters failures function-as-a-service HPC reliability workflow applications

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