Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

An Extensive Exploration of Techniques for Resource and Cost Management in Contemporary Cloud Computing Environments

Resource and cost optimization techniques in cloud computing environments target minimizing expenditure while ensuring efficient resource utilization. This study categorizes these techniques into three primary groups: Cloud and VM-focused strategies, Workflow techniques, and Resource Utilization and Efficiency techniques. Cloud and VM-focused strategies predominantly concentrate on the allocation, scheduling, and optimization of resources within cloud environments, particularly virtual machines. These strategies aim at a balance between cost reduction and adhering to specified deadlines, while ensuring scalability and adaptability to different cloud models. However, they may introduce complexities due to their dynamic nature and continuous optimization requirements. Workflow techniques emphasize the optimal execution of tasks in distributed systems. They address inconsistencies in Quality of Service (QoS) and seek to enhance the reservation process and task scheduling. By employing models, such as Integer Linear Programming, these techniques offer precision. But they might be computationally demanding, especially for extensive problems. Techniques focusing on Resource Utilization and Efficiency attempts to maximize the use of available resources in an energy-efficient and cost-effective manner. Considering factors like current energy levels and application requirements, these models aim to optimize performance without overshooting budgets. However, a continuous monitoring mechanism might be necessary, which can introduce additional complexities

Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenarioinfo:eu-repo/semantics/publishedVersio

Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources

[EN] We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.This work is sponsored by the National Natural Science Foundations of China (Nos. 71401079, 61572127, 61472192), the National Key Research and Development Program of China (No. 2017YFB1400801) and the Collaborative Innovation Center of Wireless Communications Technology. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Zhu, J.; Li, X.; Ruiz García, R.; Xu, X. (2018). Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources. IEEE Transactions on Parallel and Distributed Systems. 29(6):1401-1415. https://doi.org/10.1109/TPDS.2018.2793254S1401141529

Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing

Scientific workflows benefit from the cloud computing paradigm, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis. Minimizing resources costs to meet user’s budget is very important in a cloud environment. Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing. However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and metaheuristic as an optimization method. Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature. Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed. In this approach, first, the datasets and workflow tasks are modeled as a conditional probability matrix (CPM). Second, several data transformation and hierarchical clustering are applied to the CPM structure to determine the minimum number of virtual machines needed for the workflow execution. In this approach, the hierarchical clustering is done with respect to the budget imposed by the user. After data transformation and hierarchical clustering, the amount of data transmitted between clusters can be reduced, which can improve cost and makespan of the workflow by optimizing the use of virtual resources and network bandwidth. The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches. The results demonstrate that our approach reduces resources cost with respect to the user budgets

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research