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Machine learning-driven implementation of workflow optimization in cloud computing for IoT applications

INTERNET TECHNOLOGY LETTERS 2025年第3期8卷

作者： Jamal, Md Khalid Faisal, Mohammad Integral Univ Dept Comp Applicat Lucknow India

The optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML-driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large-scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

关键词： adaptive systems cloud computing internet of things (IoT) machine learning scalability workflow optimization

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workflow optimization in PAW 37

Workflow Optimization in PAW

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37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Filatov, Maxim Kantere, Verena Kiringa, Iluju Univ Geneva Geneva Switzerland Natl Tech Univ Athens Athens Greece Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9781538617915

Many industrial applications, from domains such as telecommunication, web and sales, require to perform complex analytics across several data processing systems. The performance of such analytics is usually expressed in workflows, and it is a task that is both labor-intensive and time-consuming. At the same time, with increasing amounts of data to be analysed, the optimization of analytics workflows becomes crucial for satisfying business objectives. This paper focuses on workflow optimization with respect to time efficiency, over multiple execution engines, such as a traditional DBMS, a MapReduce engine, and a scripting engine. This configuration is emerging as a common paradigm used to combine analysis of unstructured and structured data. We propose a novel optimization technique as part of our system called PAW (Platform for Analytics workflows). This technique creates alternative workflow structures and their execution plans based on equivalent combinations and orders of operators. The technique employs an exhaustive and a heuristic algorithm to search efficiently the space of equivalent workflow structures and select the one with the optimal execution plan. We present a thorough experimental study and we showcase the efficiency of the proposed optimization technique in a fully fledged multi engine system, applied on three real-world applications and their data, as well as on a synthetic benchmark.

关键词： Analytics workflows Multi-engine Systems workflow optimization

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Business workflow optimization Through Process Model Redesign

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年第6期69卷 3068-3084页

作者： Kumar, Akhil Liu, Rong Penn State Univ Smeal Coll Business State Coll PA 16802 USA Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

Business process redesign has garnered much interest because such processes must be continuously improved to realize greater efficiency in the midst of evolving changes in work practices, environment, and technology. This helps a business to stay competitive. A process model may be redesigned by a rearrangement of various activities as per best practices to satisfy predefined business rules and constraints for a specific objective. This objective is stated in terms of functional goals like desired or acceptable process behavior, and nonfunctional goals such as cost, time, quality of service, and flexibility. Thus, many potential improvement possibilities that emerge through restructuring can be explored. When many such alternatives exist, there is a need for a formal methodology for evaluating these designs on various metrics to gain a deeper understanding of the tradeoffs involved. In this article, we describe a novel, formal methodology for optimizing the cost-benefit considerations within a space of possible redesigns to develop a cost vs. time efficient frontier. We also illustrate our approach with two case studies, one based on a large, real dataset, and describe a proposal for implementing it. Several managerial insights emerge from our work.

关键词： Task analysis optimization Measurement Best practices Automobiles Routing Business process management mixed integer programming process redesign redesign matrix workflow optimization

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Use case-based evaluation of workflow optimization strategy in real-time computation system

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JOURNAL OF SUPERCOMPUTING 2020年第1期76卷 708-725页

作者： Ahmad, Saima Gulzar Khan, Hikmat Ullah Ijaz, Samia Munir, Ehsan Ullah COMSATS Univ Islamabad Wah Campus Islamabad Pakistan

With the start of big data era, data stream computing has emerged as a well-known approach to optimize data-intensive workflows. Apache STORM is an open-source real-time distributed computation system for processing data streams and has been opted by famous organizations such as Twitter, Yahoo, Alibaba, Baidu, Groupon. The workflows are implemented as topologies in STORM. The main aspect that controls the execution performance of a workflow in STORM is the strategy of scheduling the topology components (spout and bolts). In this paper, we evaluate and analyze the performance of our algorithm Partition-based Data-intensive workflow optimization Algorithm (PDWA) in Apache STORM using a use case workflow, EURExpressII. It is a real-world application-based workflow that builds a transcriptome-wide atlas of gene expression for the developing mouse embryo established by ribonucleic acid (RNA) in situ hybridization. Our proposed algorithm, PDWA, partitions the application task graph so that the data movement between partitions is minimum. Each partition is then mapped on one machine for the execution of tasks of that partition. It provides minimum execution time for that particular partition. Partial task duplication is also part of this algorithm that enhances the performance. A STORM-based computing cluster is developed in OpenStack cloud which is used as a computing environment. The performance of PDWA-based optimizer is evaluated with the data sets of different sizes. The achieved results show that PDWA performs with 21% improved average execution time for different sizes of data sets and varying execution nodes. In addition, the comparative results show that on average the efficiency of PDWA is 20.4% higher as compared to STORM default scheduler (SDS).

关键词： workflow optimization STORM topology Partitions Data intensive Stream data processing

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Atrial fibrillation ablation workflow optimization facilitated by high-power short-duration ablation and high-resolution mapping

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EUROPACE 2024年第3期26卷 euae067页

作者： Fink, Thomas Sciacca, Vanessa Nischik, Flemming Bergau, Leonard Guckel, Denise El Hamriti, Mustapha Khalaph, Moneeb Braun, Martin Winnik, Stephan Didenko, Maxim Imnadze, Guram Sommer, Philipp Sohns, Christian Herz & Diabeteszentrum NRW Clin Electrophysiol Georgstr 11 D-32545 Bad Oeynhausen Germany Univ Hosp Zurich Univ Heart Ctr Dept Cardiol Zurich Switzerland

Aims Pulmonary vein isolation (PVI) for catheter ablation of atrial fibrillation (AF) is a time-demanding procedure. High-power short-duration (HPSD) ablation protocols and high-density mapping catheters have recently been introduced to clinical practice. We investigated the impact of high-density mapping and HPSD ablation protocols on procedural timing, efficacy, and safety by comparing different standardized set-ups. Methods and results Three electrophysiology (EP) laboratory set-ups were analysed: (i) circular catheter for mapping and HPSD ablation with 30/35 W guided by an ablation index (AI);(ii) pentaspline catheter for mapping an HPSD ablation with 50 W guided by an AI;and (iii) pentaspline catheter for mapping and HPSD ablation with 90 W over 4 s using a novel ablation catheter. All patients underwent PVI without additional left atrial ablation strategies. Procedural data and operating intervals in the EP laboratory were systematically analysed. Three hundred seven patients were analysed (30/35 W AI: n = 102, 50 W AI: n = 102, 90 W/4 s: n = 103). Skin-to-skin times [105.3 +/- 22.7 (30/35 W AI) vs. 81.4 +/- 21.3 (50 W AI) vs. 69.5 +/- 12.2 (90 W/4 s) min, P <= 0.001] and total laboratory times (132.8 +/- 42.1 vs. 107.4 +/- 25.7 vs. 95.2 +/- 14.0 min, P < 0.001) significantly differed among the study groups. Laboratory interval analysis revealed significant shortening of mapping and ablation times. Arrhythmia-free survival after 12 months was not different among the study groups (log-rank P = 0.96). Conclusion The integration of high-density mapping and HPSD protocols into an institutional AF ablation process resulted in reduced procedure times without compromising safety or efficacy. [GRAPHICS]

关键词： Atrial fibrillation Pulmonary vein isolation High power short duration High-density mapping workflow optimization Laboratory analysis

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The many faces of data-centric workflow optimization: a survey

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018年第2期6卷 81-107页

作者： Kougka, Georgia Gounaris, Anastasios Simitsis, Alkis Aristotle Univ Thessaloniki Thessaloniki Greece HP Labs Palo Alto CA USA

workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a key aspect is data passing through and getting manipulated by a sequence of steps. The large volume and variety of data, the complexity of operations performed, and the long time such workflows take to compute give rise to the need for optimization. In general, data-centric workflow optimization is a technology in evolution. This survey focuses on techniques applicable to workflows comprising arbitrary types of data manipulation steps and semantic inter-dependencies between such steps. Further, it serves a twofold purpose: firstly, to present the main dimensions of the relevant optimization problems and the types of optimizations that occur before flow execution and secondly, to provide a concise overview of the existing approaches with a view to highlighting key observations and areas deserving more attention from the community.

关键词： Data flows workflow optimization workflow management systems Data analysis Data science

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Data oriented analysis of workflow optimization

Data oriented analysis of workflow optimization

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3rd World Congress on Intelligent Control and Automation

作者： Ren, ML Zhu, WD Yang, SL Hefei Univ Technol Inst Comp Network Syst Hefei 230009 Anhui Peoples R China

ISBN: (纸本)7312012035

Recent progress in IT have greatly changed the way we use and organize Information Resources, thus make it possible to make a fundamental rethinking of business process that have complexity and redundancy built into it over the years. In this paper, we present a data oriented method to optimize the workflow, some rules are given to complete this analysis, by analyzing data input to and output from functions, this method gives an approach to BPR, and the right process can be guaranteed.

关键词： BPR data flow workflow optimization MIS

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Data-Intensive workflow optimization based on Application Task Graph Partitioning in Heterogeneous Computing Systems 4

Data-Intensive Workflow Optimization based on Application Ta...

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IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ahmad, Saima Gulzar Liew, Chee Sun Rafique, M. Mustafa Munir, Ehsan Ullah Khan, Samee U. Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia IBM Res Dublin Ireland COMSATS Inst Informat Technol Dept Comp Sci Wah Cantt Pakistan North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA

ISBN: (纸本)9781479967193

Stream based data processing model is proven to be an established method to optimize data-intensive applications. Data-intensive applications involve movement of huge amount of data between execution nodes that incurs large costs. Data-streaming model improves the execution performance of such applications. In the stream-based data processing model, performance is usually measured by throughput and latency. optimization of these performance metrics in heterogeneous computing environment becomes more challenging due to the difference in the computing capacity of execution nodes and variations in the data transfer capability of communication links between these nodes. This paper presents a dual objective Partitioning based Data-intensive workflow optimization Algorithm (PDWA) for heterogeneous computing systems. The proposed PDWA provides significantly reduced latency with increase in the throughput. In the proposed algorithm, the application task graph is partitioned such that the interpartition data movement is minimal. Such optimized partitioning enhances the throughput. Each partition is mapped to the execution node that gives minimum execution time for that particular partition. PDWA also exploits partial task duplication to reduce the latency. We evaluated the proposed algorithm with synthesized benchmarks and workflows from the real-world workloads, and the proposed algorithm shows 60% reduced latency with 47% improvement in the throughput as compared to the approach when workflows are not partitioned.

关键词： workflow optimization Partitioning task graph Heterogeneous computing Stream-data processing

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Design of a workflow optimization System for Urban Water Utility Maintenance

Design of a Workflow Optimization System for Urban Water Uti...

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Systems and Information Engineering Design Symposium (SIEDS)

作者： Khan, Adnan Orozco, Rebekah Khattab, Mohamed Alqahtani, Fahad Laboratoire Recherche Informatique (LRI-CNRS UMR 8623) Université Paris-Sud Orsay France Laboratoire d’Informatique Université Paris Descartes Paris France Department of Information Engineering Wuhan University of Technology Wuhan Hubei China

ISBN: (纸本)9781538618486

The Water Distribution System (WDS) in the District of Columbia is one of the most complex in the world, spanning 1,440 dense urban miles. Providing water to 700,000 residents and 16.6 million annual visitors, the system consists of approximately 40,000 valves, 9,500 fire hydrants and 130,000 service connections. Originally developed in the 1800s, the median age of pipe in the WDS has reached 78 years. Ruptures in water main pipes carrying over 200 psi of pressure can buckle roadways, flood streets and cause widespread service disruptions. In winter months, this can create icy conditions which are extremely hazardous. The District's water utility reports approximately 500 water main breaks per year. Since the implementation of a priority system designed to expedite processing of high-impact emergency failures, the utility's operational data has identified high response times for incidents classified as low priority. The average is approximately 84 days, nearly six times higher than emergency incidents. The scheduling phase of the workflow is responsible for 98 percent of the total delay, indicating the need for a streamlined scheduling methodology. Emergency incident volume increases twofold in winter months, with sporadic failures caused by temperature fluctuations. This has forced the utility to increase reliance on contracted crews by 20 percent, and indicated an urgent need for weather-dependent failure forecasting tools. A queueing model of the utility's workflow was used to derive standard scheduling protocols for groups of low priority incident types. Results indicate that achieving performance goals is feasible without additional resources. Recommended workflow configurations and operational improvements reduced the low priority response time to 12.4 days. Additionally, an Artificial Neural Network (ANN) was trained and deployed to generate main break forecasts for scheduling purposes. Validation tests using the utility's observed main breaks reported R-2 =

关键词： Water Distribution System Failure Prediction workflow optimization Main Breaks Water Utility Priority System Artificial Neural Network

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Annualized workflow optimization ROI strategies utilizing JDF technology

Annualized workflow optimization ROI strategies utilizing JD...

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65th Annual Technical Conference of the Technical Association of the Graphic Arts, TAGA: Disseminating Graphic Arts Research Internationally since 1948

作者： Iskey, Brandon Harvey, Jim Bohan, Mark EFI Inc. United States CIP4 Organization United States Printing Industries of America United States

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