A Review of Workflow Scheduling in Cloud Computing Environment

Abstract Over the years, distributed environments have evolved from shared community platforms to utility-based models; the latest of these being Cloud computing. This technology enables the delivery of IT resources over the Internet and follows a pay-as-you-go model where users are charged based on their usage. There are various types of Cloud providers each of which has different product offerings. They are classified into a hierarchy of as-a-service terms: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). There are a mass of researches on the issue of scheduling in cloud computing, most of them, however, are about workflow and job scheduling. A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Many scheduling policies have been proposed till now which aim to maximize the amount of work completed while meeting QoS constraints such as deadline and budget. However many of them are not optimal to incorporate some basic principles of Cloud Computing such as the elasticity and heterogeneity of the computing resources. Therefore our work focuses on studying various problems and issues related to workflow scheduling

The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing

The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA2019;s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today2019; svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling

Optimized Load Balancing based Task Scheduling in Cloud Environment

The fundamental issue of Task scheduling is one important factor to load balance between the virtual machines in a Cloud Computing network. However, the optimal broadcast methods which have been proposed so far focus only on cluster or grid environment. In this paper, task scheduling strategy based on load balancing Quantum Particles Swarm algorithm (BLQPSO) was proposed. The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. Furthermore, the performance of proposed algorithm had been compared with existing PSO and shows their effectiveness in balancing the load

Fuzzy logic-based algorithm resource scheduling for improving the reliability of cloud computing

Cloud computing is an important infrastructure for distributed systems with the main objective of reducing the use of resources. In a cloud environment, users may face thousands of resources to run each task. However, allocation of resources to tasks by the user is an impossible endeavor. Accurate scheduling of system resources results in their optimal use as well as an increase in the reliability of cloud computing. This study designed a system based on fuzzy logic and followed by an introduction of an efficient and precise algorithm for scheduling resources for improving the reliability of cloud computing. Waiting and turnaround times of the proposed method were compared to those of previous works. In the proposed method, the waiting time is equal to 26.99 and the turnaround time is equal to 82.99. According to the results, the proposed method outperforms other methods in terms of waiting time and turnaround time as well as accuracy

QoS-aware Scientific Application Scheduling Algorithm in Cloud Environment

Many complex scientific applications are modeled in the form of workflows to carry out large-scale experiments. Because of complexity of scientific processes, scientific workflows need intensive computation and data requirements. Clouds make opportunity for scientific that need high performance computing infrastructure. So scientific can run their application on cloud by their desired QoS. We propose an algorithm that able scientific to select execute plan based on their preference QoS, like time and cost. Proposed algorithm ranks the tasks in workflow and then use UPFF function for select accurate resource, based on user’s QoS. We compared our proposed algorithm with the same work by several scenarios and results show proposed algorithm has better efficiency. Keywords Scientific application, Workflow scheduling, Cloud computin

RSCCGA: Resource Scheduling for Cloud Computing by Genetic Algorithm

Cloud computing, also known as on-the-line computing, is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources, which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centers. It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over a network. the scheduling problem is an important issue in the management of resources in the cloud, because despite many requests the data center there is the possibility of scheduling manually. Therefore, the scheduling algorithms play an important role in cloud computing, because the goal of scheduling is to reduce response times and improve resource utilization. The computing resources, either software or hardware, are virtualized and allocated as services from providers to users. The computing resources can be allocated dynamically upon the requirements and preferences of consumers. Traditional system-centric resource management architecture cannot process the resource assignment task and dynamically allocate the available resources in a cloud computing environment. This paper proposed a resource scheduling model for cloud computing based on the genetic algorithm. Experiments show that proposed method has more performance than other methods.Keywords: Cloud Computing, Resource Management, Scheduling, Bandwidth Consumption, Waiting Time, Genetic algorith

Effective Workflow Scheduling in Cloud using Constriction Factor based Inertia Weight Particle Swarm Optimization

Cloud computing allows rapid provision of resources based on the need. This enables users to execute the independent tasks and dependent tasks called workflows on the cloud system. Workflow scheduling is a crucial problem that is NP Hard and is still a challenging problem. Particle Swarm Optimization (PSO) is one of the commonly used metaheuristic algorithms for solving task scheduling problems, but it has issues with premature convergence and lack of diversity. In recent years, chaotic maps have been employed in PSO to enhance its performance. This study proposes a Constriction factor-based inertia weight in PSO for workflow scheduling (CFPSO). The proposed algorithm utilizes a constriction factor for updating the inertia weight, which enhances the exploration ability of the algorithm thereby avoid local optima. The algorithm considers a fitness function with an aim to minimize makespan, service cost, and maximize load balance. The proposed algorithm is evaluated using a set of benchmark workflows, and the obtained results are compared with the standard PSO algorithm, Grey Wolf Optimizer (GWO) algorithm and Chaotic PSO algorithm. The extensive experimentation performed show that the proposed algorithm outperforms the other algorithms in terms of makespan, service cost, and load balance. The proposed CFPSO shows reduction of 20% of makespan, 2% of the service cost and 18% load balance rate compared to the conventional algorithms on Montage workflow with 1000 tasks. The use of constriction factor enhances the performance of the algorithm and makes it suitable for solving complex problems with multiple objectives. The proposed algorithm can be used in real-world applications to optimize workflow scheduling in cloud computing environments