Developing a Workflow Management System Simulation for Capturing Internal IaaS Behavioural Knowledge

Al-Haboobi, Ali; Kecskeméti, Gábor

doi:10.1007/s10723-022-09638-7

Cited by 6 publications

(9 citation statements)

References 41 publications

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“…Our experiment evaluated DSAWS with other competitive algorithms like CGA and Dyna for scheduling the selected scientific workflow applications. experiment was conducted in the DISSECT-CF-WMS [27] simulator, which is an extension of the DISSECT-CF simulator. It is useful for running scientific workflows on cloud resources.…”

Section: Discussionmentioning

confidence: 99%

Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud

Al-Haboobi,

Kecskemeti

2024

IJACSA

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Cloud computing provides pay-per-use IT services through the Internet. Although cloud computing resources can help scientific workflow applications, several algorithms face the problem of meeting the user's deadline while minimising the cost of workflow execution. In the cloud, selecting the appropriate type and the exact number of VMs is a major challenge for scheduling algorithms, as tasks in workflow applications are distributed very differently. Depending on workflow requirements, algorithms need to decide when to provision or de-provision VMs. Therefore, this paper presents an algorithm for effectively selecting and allocating resources. Based on the workflow structure, it decides the type and number of VMs to use and when to lease and release them. For some structures, our proposed algorithm uses the initial rented VMs to schedule all tasks of the same workflow to minimise data transfer costs. We evaluate the performance of our algorithm by simulating it with synthetic workflows derived from real scientific workflows with different structures. Our algorithm is compared with Dyna and CGA approaches in terms of meeting deadlines and execution costs. The experimental results show that the proposed algorithm met all the deadline factors of each workflow, while the CGA and Dyna algorithms met 25% and 50%, respectively, of all the deadline factors of all workflows. The results also show that the proposed algorithm provides more cost-efficient schedules than CGA and Dyna.

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Section: Discussionmentioning

confidence: 99%

Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud

Al-Haboobi,

Kecskemeti

2024

IJACSA

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“…The authors 21 developed a WMS emulator on DISSECT‐CF called DISSECT‐CF‐WMS that makes the inner workings of cloud infrastructures available. DISSECT‐CF‐WMS enables the analysis of schedulers for physical machines, which enhances energy awareness.…”

Section: Review Of Literaturementioning

confidence: 99%

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

Aljohani

2024

Concurrency and Computation

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SummaryThe data‐intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big‐data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow execution landscape, there is a rising emphasis on resolving Quality of Service (QoS) and security concerns within homogeneous execution settings. Workflow execution on diverse computing platforms has received limited research. We are developing an edge‐cloud‐specific Multi‐Layer Security and Quality‐Aware (MLSQA) framework to solve research issues in this area. Node attacks are detected via reputation‐based security features in the MLSQA framework, whereas link attacks are detected using machine learning. A unique trade‐off metrics technique optimizes workflow security and QoS parameters, reducing energy usage and makespan. In addition, a fault‐tolerant, energy‐efficient job offloading technique is described in detail to improve the system's resilience to errors. The experiment is conducted using complex (i.e., memory and CPU intensive) scientific procedures with intermediate job dependencies, namely Inspiral workflow. The MLSQA framework excels in threat detection, demonstrating lower false alarms, reduced energy consumption, and quicker implementation than the recent secure workflow execution architecture.

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“…In our earlier work, we introduced DISSECT-CF-WMS [ 15 ], an extension of DISSECT-CF, which does not support workflow-based simulation. It is designed to perform scientific workflow simulations and analyse internal IaaS behavioural knowledge.…”

Section: Research Backgroundmentioning

confidence: 99%

Simulating IoT Workflows in DISSECT-CF-Fog

Márkus

Al-Haboobi

Kecskeméti

et al. 2023

Sensors

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The modelling of IoT applications utilising the resources of cloud and fog computing is not straightforward because they have to support various trigger-based events that make human life easier. The sequence of tasks, such as performing a service call, receiving a data packet in the form of a message sent by an IoT device, and managing actuators or executing a computational task on a virtual machine, are often associated with and composed of IoT workflows. The development and deployment of such IoT workflows and their management systems in real life, including communication and network operations, can be complicated due to high operation costs and access limitations. Therefore, simulation solutions are often applied for such purposes. In this paper, we introduce a novel simulator extension of the DISSECT-CF-Fog simulator that leverages the workflow scheduling and its execution capabilities to model real-life IoT use cases. We also show that state-of-the-art simulators typically omit the IoT factor in the case of the scientific workflow evaluation. Therefore, we present a scalability study focusing on scientific workflows and on the interoperability of scientific and IoT workflows in DISSECT-CF-Fog.

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Developing a Workflow Management System Simulation for Capturing Internal IaaS Behavioural Knowledge

Cited by 6 publications

References 41 publications

Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud

Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

Simulating IoT Workflows in DISSECT-CF-Fog

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