arXiv subjects

Muhammad H. Hilman

Publications and source records attributed to Muhammad H. Hilman.

4 recordsLinked to original sources

arXiv2020

Resource-sharing Policy in Multi-tenant Scientific Workflow-as-a-Service Cloud Platform

Increased adoption of scientific workflows in the community has urged for the development of multi-tenant platforms that provide these workflow executions as a service. As a result, Workflow-as-a-Service (WaaS) concept has been created by researchers to address the future design of Workflow Management Systems (WMS) that can serve a large number of users from a single point of service. These platforms differ from traditional WMS in that they handle a workload of workflows at runtime. A traditional WMS is usually designed to execute a single workflow in a dedicated process while WaaS cloud platforms enhance the process by exploiting multiple workflows execution in a multi-tenant environment model. In this paper, we explore a novel resource-sharing policy to improve system utilization and to fulfil various Quality of Service (QoS) requirements from multiple users in WaaS cloud platforms. We propose an Elastic Budget-constrained resource Provisioning and Scheduling algorithm for Multiple workflows that can reduce the computational overhead by encouraging resource sharing to minimize workflows' makespan while meeting a user-defined budget. Our experiments show that the EBPSM algorithm can utilize the resource-sharing policy to achieve higher performance in terms of minimizing the makespan compared to the state-of-the-art budget-constraint scheduling algorithm.

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arXiv2020

Workflow-as-a-Service Cloud Platform and Deployment of Bioinformatics Workflow Applications

Workflow management systems (WMS) support the composition and deployment of workflow-oriented applications in distributed computing environments. They hide the complexity of managing large-scale applications, which includes the controlling data pipelining between tasks, ensuring the application's execution, and orchestrating the distributed computational resources to get a reasonable processing time. With the increasing trends of scientific workflow adoption, the demand to deploy them using a third-party service begins to increase. Workflow-as-a-service (WaaS) is a term representing the platform that serves the users who require to deploy their workflow applications on third-party cloud-managed services. This concept drives the existing WMS technology to evolve towards the development of the WaaS cloud platform. Based on this requirement, we extend CloudBus WMS functionality to handle the workload of multiple workflows and develop the WaaS cloud platform prototype. We implemented the Elastic Budget-constrained resource Provisioning and Scheduling algorithm for Multiple workflows (EBPSM) algorithm that is capable of scheduling multiple workflows and evaluated the platform using two bioinformatics workflows. Our experimental results show that the platform is capable of efficiently handling multiple workflows execution and gaining its purpose to minimize the makespan while meeting the budget.

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arXiv2019

Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions

The workflow is a general notion representing the automated processes along with the flow of data. The automation ensures the processes being executed in the order. Therefore, this feature attracts users from various background to build the workflow. However, the computational requirements are enormous and investing for a dedicated infrastructure for these workflows is not always feasible. To cater to the broader needs, multi-tenant platforms for executing workflows were began to be built. In this paper, we identify the problems and challenges in the multiple workflows scheduling that adhere to the platforms. We present a detailed taxonomy from the existing solutions on scheduling and resource provisioning aspects followed by the survey of relevant works in this area. We open up the problems and challenges to shove up the research on multiple workflows scheduling in multi-tenant distributed systems.

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arXiv2018

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.

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