Workflows In Cloud Environment Research Articles

GA-ETI: An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments

Over recent years, cloud computing has become one of the main sources of computer power to run scientific experiments. To cope with these demands, cloud providers need to efficiently match applications with computing resources to maintain an acceptable level of customer satisfaction. A correct match or scheduling of scientific workflows relies on the ability to fully analyze applications prior to execution, analyze characteristics of available computing resources, provide users with several scheduling configurations, and guide users to select the optimal configuration to execute workflows. To date, different schedulers have been proposed to execute complex applications on cloud environments; nevertheless, none exists, to the best of our knowledge, to provide all the aforementioned features. GA-ETI, the scheduler proposed in this work, is designed to address all aforementioned concerns by providing several efficient solutions (in a Pareto Front fashion) to run scientific workflows on cloud resources. Flexibility of optimization procedure of GA-ETI allows it to easily adapt to different types of scientific workflows and produce schedules that effectively exploit/consider the relationship between jobs and their required data. GA-ETI acts as an interface between cloud user and cloud provider in receiving an application, analyzing it, and distributing its tasks among selected resources. GA-ETI differs from the majority of proposed schedulers because it can adapt to the size of both jobs and virtual machines, it includes a monetary cost model (from a public cloud), and it considers complex interdependencies among tasks. We test GA-ETI with five well-known benchmarks with different computing and data transfer demands in our VMware-vSphere private cloud. Through experimentation, GA-ETI has been proved to reduce makespan of executing workflows between 11% and 85% when compared to three up-do-date scheduling algorithms without increasing the monetary cost. GA-ETI opens the way to develop a top-layer-scheduler for a workflow manager system to provide a complex analysis and include different optimizing objectives.

Scheduling Dynamic Parallel Loop Workflow in Cloud Environment

Scientific workflows have been employed to automate large scale scientific experiments by leveraging computational power provided on-demand by cloud computing platforms. Among these workflows, a parallel loop workflow is used for studying the effects of different input values of a scientific experiment. Because of its independent loop characteristic, a parallel loop workflow can be dynamically executed as parallel workflow instances to accelerate the execution. Such execution negates workflow traversal used in existing works to calculate execution time and cost during scheduling in order to maintain time and cost constraints. In this paper, we propose a novel scheduling technique that is able to handle dynamic parallel loop workflow execution through a new method for evaluating execution progress together with a workflow instance arrival control and a cloud resource adjustment mechanism. The proposed technique, which aims at maintaining a workflow deadline while reducing cost, is tested using 3 existing task scheduling heuristics as its task mapping strategies. The simulation results show that the proposed technique is practical and performs better when the time constraint is more relaxed. It also prefers task scheduling heuristics that allow for a more accurate progress evaluation.

Level Based Task Prioritization Scheduling for Small Workflows in Cloud Environment

Cloud computing is aimed at delivering computing services as a utility. One of the Cloud computing services is Workflow as a Service (WFaaS). Workflow scheduling is a vital area in WFaaS. The objective of this paper is to propose a scheduling algorithm that meets the Quality of Service constraints such as Makespan and Cost. The proposed algorithm Level Based Task Prioritization (LBTP) follows the list scheduling strategy. The LBTP algorithm alters task prioritization phase and the resource selection phase, where three different algorithms like Earliest Finish Time, Parent Resource Allocation and Round Robin are applied. The LBTP algorithm is tested for small workflows. The performance analysis is done by varying the Communication to Computation Ratio and number of tasks. The scheduling metrics, Makespan and Cost of the proposed algorithm are compared with the Customer Facilitated Cost based Scheduling (CFCSC) algorithm. The experimental results show that Earliest Finish Time resource selection procedure outperforms CFCSC algorithm with respect to Makespan for communication intensive graphs. For computation intensive graphs, the parent resource allocation procedure produces better Makespan. With respect to Cost parameter, irrespective of total number of tasks in the Direct Acyclic Graph, all the resource selection procedures have incurred minimum cost. The proposed algorithm helps the Cloud Provider to select the scheduling algorithm in accordance with the Quality of Service parameters. Further, the LBTP algorithm can be tested with Regular Scientific workflows like Montage and Cybershake.

An Analysis Report of Workflow Scheduling Algorithm for Cloud Environment

is almost an inseparable part of human's life, we are not aware but when we are sharing/storing our photographs online in our emails or social sites then we are using cloud services instead of storing them in our computer's hard drive or sharing them via hard devices. In the official works as well we many times comes into a situation to call a web service rather than themselves creating it in house and invoking, this comes under cloud computing. The workflows in cloud environment can be scheduled as needed based on the user's requirement as well as the scheduling algorithms concept. Different scheduling algorithm considers different parameters for the scheduling of task workflows and there outputs are also different. Some considers time or deadline as the base parameter and tries to complete the task within the provided deadline, they are also known as hard deadline based algorithms whereas some considers cost as there base parameter where they tries to reduce the cost as much as possible so that it will be economic to the end users. Whereas some are combination of both time and cost where they look after for a solution which can cover the deadlines as well as the solution will not cost much to the end users. There are some traditional scheduling algorithms also which schedules the task workflows like FCFS (First come first serve) and SJF (Shortest job first) which are also helpful in scheduling task as well as performance measurement of new algorithms. In this paper we have studies different and popular scheduling techniques of Workflows in different cloud models and we will analyze their experimental results.

Replica allocation policy of cloudy services based on social network properties

To improve the running efficiency of business workflow in cloud environment,a policy of replica allocation of cloudy services was proposed.Taking the advantage of social network analysis,the policy specified the central service nodes in a service network based on mining the social network properties such as connectivity and centralization for a service community.The host physical machine of the replica of the central service was specified according to the analysis of logical sequence between the central service and its pre-service,and the usage of other physical machines.The analysis and simulation show that the policy can improve the running efficiency of data intensive business workflow in a cloud environment by averaging the overload of physical machine and reducing the time wasted by long-distance service interaction.

Decentralized orchestration of data-centric workflows in Cloud environments

Data-centric and service-oriented workflows are commonly used in scientific research to enable the composition and execution of complex analysis on distributed resources. Although there are a plethora of orchestration frameworks to implement workflows, most of them are unsuitable for executing (enacting) data-centric workflows since they are based on a centralized orchestration engine which can be a bottleneck when handling large data volumes. In this paper, we propose a flexible and lightweight workflow framework based on the Object Modeling System (OMS). Moreover, we take advantage of the OMS architecture to deploy and execute data-centric workflows in a decentralized manner across multiple distinct Cloud resources, avoiding limitations of all data passing through a centralized engine. The proposed framework is implemented in the context of the Australian Urban Research Infrastructure Network (AURIN) project which is an initiative aiming to develop an e-Infrastructure supporting research in the urban and built environment domains. Performance evaluation results using spatial data-centric workflows show that we can reduce 20% of the workflow execution time when using Cloud resources in the same network domain.

Design and Deployment of Workflows in Cloud Environment

Cloud computing is a new benchmark towards enterprise application development that can effectively facilitate the execution of workflows in business process management systems. Workflow applications often require very complex execution environments that are difficult to create otherwise. Integration of the workflow management systems for application deployment with any of the cloud platforms is a tedious task. Therefore, various cloud services are required to execute workflows along with workflow management software in cloud environment. In this paper, a comparative analysis of workflow design approaches has been discussed .This paper presents the challenges for implementing workflows in cloud computing and state of the art of workflow design along with a comparison of various workflow engines available. Design of workflow for a real time application has also been discussed with the help of a case study. The experimental results demonstrate how workflow for enterprise applications can be designed, validated, implemented and deployed on any major cloud infrastructures.

An adaptive parallel execution strategy for cloud‐based scientific workflows

SUMMARYMany of the existing large‐scale scientific experiments modeled as scientific workflows are compute‐intensive. Some scientific workflow management systems already explore parallel techniques, such as parameter sweep and data fragmentation, to improve performance. In those systems, computing resources are used to accomplish many computational tasks in high performance environments, such as multiprocessor machines or clusters. Meanwhile, cloud computing provides scalable and elastic resources that can be instantiated on demand during the course of a scientific experiment, without requiring its users to acquire expensive infrastructure or to configure many pieces of software. In fact, because of these advantages some scientists have already adopted the cloud model in their scientific experiments. However, this model also raises many challenges. When scientists are executing scientific workflows that require parallelism, it is hard to decide a priori the amount of resources to use and how long they will be needed because the allocation of these resources is elastic and based on demand. In addition, scientists have to manage new aspects such as initialization of virtual machines and impact of data staging. SciCumulus is a middleware that manages the parallel execution of scientific workflows in cloud environments. In this paper, we introduce an adaptive approach for executing parallel scientific workflows in the cloud. This approach adapts itself according to the availability of resources during workflow execution. It checks the available computational power and dynamically tunes the workflow activity size to achieve better performance. Experimental evaluation showed the benefits of parallelizing scientific workflows using the adaptive approach of SciCumulus, which presented an increase of performance up to 47.1%. Copyright © 2011 John Wiley & Sons, Ltd.