Workflow Scheduling Using MGWO Algorithm in Cloud Computing

Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

Cloud computing provides powerful, highly scalable, flexible resources for real world applications. It also reduces the cost and operation expenses. Workflow scheduling is important for getting higher performance, reducing cost and using resources more efficiently in cloud computing. Workflow scheduling in cloud systems assigns tasks to resources available in the system and aims to utilize cloud resources by decreasing makespan of the workflow. In this study, an evolutionary algorithm is proposed to solve workflow scheduling problem. The main objective of this work is to minimize the makespan of the schedule. To achieve this goal, problem specific crossover operator and mutation operators are proposed in the evolutionary algorithm. The crossover operator will combine the problem-specific information stored in both parents to create a new individual. The mutation operators will explore neighbor solutions using some intelligent search mechanisms. This unique design of the operators increases the diversity of the search space and the quality of the solutions. As a result, the workflow schedules obtained from the evolutionary algorithm decreases the makespan of the workflow in the cloud system. The performance of the proposed study is measured using well-known scientific workflows and is compared with the algorithms from the literature. The proposed study outperforms all related algorithms in 67% of the test cases and obtains the same results in the remaining test cases.

Cloud computing has emerged as a dominant platform for hosting complex applications, offering scalable and flexible resources on demand. However, the dynamic and heterogeneous nature of cloud environments poses significant challenges for efficient workflow scheduling, particularly when aiming to minimize total execution time, energy consumption, and operational cost. In this research, we propose a novel hybrid approach that integrates the Heterogeneous Earliest Finish Time (HEFT) algorithm with an Improved Grey Wolf Optimizer (IGWO) enhanced by differential evolution strategies and survival-of-the-fittest mechanisms. These enhancements strengthen exploration and exploitation by adaptively mutating and refining task allocations while eliminating weaker solutions. The use of HEFT-based initialization provides a strong starting population, and the DE-driven IGWO refinement accelerates convergence and avoids premature stagnation. Together, these two-level optimization strategy ensures faster convergence and higher energy-efficient workflow scheduling compared to earlier HEFT metaheuristic approaches. To evaluate the effectiveness of the proposed hybrid method, extensive experiments were conducted on randomly generated workflows with varying task and dependency complexities. The performance analysis demonstrates that the hybrid HEFT-IGWO approach consistently outperforms standard HEFT, traditional GWO, and standalone metaheuristic techniques in terms of minimizing makespan, reducing energy consumption, and lowering cloud infrastructure costs. This study highlights the potential of combining heuristic initialization with evolutionary optimization to achieve energy-efficient, cost-effective workflow scheduling in cloud computing environments.

Workflow scheduling is one of the burning topics that has drawn enormous attention recently in the research community of cloud computing due to its wide applications in astronomy, physics, bioinformatics, health care and so on. This is a well-known NP-complete problem. It presents an interesting aspect of achieving minimum processing time of all the tasks and maximum resource utilization in cloud resources. Therefore, many algorithms have been developed for workflow scheduling. However, most of them consider a static priority of the tasks which is non-realistic for heterogeneous cloud computing environment. In this paper, we propose a workflow scheduling algorithm which considers dynamic priority of the tasks. The algorithm undergoes a process of min–max normalization followed by the calculation of the dynamic threshold to dispatch the tasks into one of the virtual machines. The algorithm is extensively simulated using various benchmark, scientific and real-life workflows. All the simulated results are compared with other four existing workflow scheduling algorithms. The simulated results confirm that the proposed algorithm lags behind all the four existing algorithms in terms of makespan and average cloud resource utilization. The simulation results are also validated through analysis of variance statistical test.

Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget $$ (erb) $$ for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan.

One of the issues in cloud computing is workflow scheduling. A workflow models the process of executing an application comprising a set of steps and its objective is to simplify the complexity of application management. Workflow scheduling maps each task to a proper resource and sorts tasks on each resource to meet some efficiency measures such as processing and transmission costs, load balancing, quality of service, and etc. Task scheduling is an NP-Complete problem. In this study, meta-heuristic firefly algorithm (FA) is used to present a workflow scheduling algorithm. The purpose of the proposed scheduling algorithm is to explore optimal schedules such that the cost of processing and transmission of the whole workflow are minimized while there will be load balancing among the processing stations. The proposed algorithm is implemented in MATLAB and its efficiency is compared with cat swarm optimization (CSO) algorithm. The evaluations show that the proposed algorithm outperforms CSO in finding better solutions.

Cloud computing is an emerging area of research and is useful for all level of users from end users to top business companies. There are several research areas of cloud computing including load balancing, cost management, workflow scheduling etc., which has been the current research interest of researchers. To deal with such problems, some conventional methods are developed, which are not so effective. Since, last decade the use of nature inspired optimization in cloud computing is a major area of concern. In this chapter, a detailed (yet brief) survey report on the applicability of nature inspired algorithms in various cloud computing problems is highlighted. The chapter aims at providing a detailed knowledge about nature inspired optimization algorithms and their use in the above mentioned problems of cloud computing. Some future research directions of cloud computing and other application areas are also discussed.

Workflow systems have become a major vehiclefor easy and efficient development of scientific applications. This type of systems can benefit from the resource provisioningtechnology offered by the cloud computing. In fact, the latteroffers on-demand virtualized resources to its users. These virtualresources can be added and released dynamically. Also, usersare charged on a pay-per-use basis. How to make appropriatedecisions when allocating resources to the tasks and dispatchingthe computing tasks to resource pool has become the main issuein cloud computing. The amount of allocated resources affects theexecution time of the applications and the cost incurred by the user. In fact, resource under-provisioning will necessarily affectthe performance. In contrast, over-provisioning can result in idleinstances and cause additional costs. Then, efficient scheduling algorithms are required for selection of best suitable resources for task execution. This paper focuses on some of the important workflow scheduling strategies. It brings out an exhaustive survey of such strategies in cloud computing and includes a detailed classification of them. Then, it presents a comparative analysis of the studied approaches. Finally, it stands out a critical challenge for further research.

Users may access virtual, scalable, and dynamic resources using cloud computing, which is a novel technology that charges them only for the resources they use. This technology is reliant on a network to function. Several services, like as e-commerce apps, use the whole network as a result of the sheer scope of the network. Cloud computing has progressed through three phases in terms of practical development: distributed computing, parallel computing, and grid computing. Cloud computing is a cutting-edge technology that installs networks of computers, many of which are situated in faraway locations, to conduct operations on massive amounts of data in real time, main challenge in cloud workflow Scheduling in edge context. In proposed approach we used hybrid approach using Tabu and Grey Wolf Optimization. In experiment improved results of approx. 5-10% average in all performance metrics are obtained.

SummaryThe cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.

Edge computingZivkovic, Miodrag Bezdan, Timea Strumberger, Ivana Bacanin, Nebojsa Venkatachalam, K. is a relatively novel technology, which is closely related to the concepts of the Internet of things and cloud computing. The main purpose of edge computing is to bring the resources as close as possible to the clients, to the very edge of the cloud. By doing so, it is possible to achieve smaller response times and lower network bandwidth utilization. Workflow scheduling in such an edge–cloud environment is considered to be an NP-hard problem, which has to be solved by a stochastic approach, especially in the scenario of multiple optimization goals. In the research presented in this paper, a modified Harris hawks optimization algorithm is proposed and adjusted to target cloud–edge workflow scheduling problem. Simulations are carried out with two main objectives—cost and makespan. The proposed experiments have used real workflow models and evaluated the proposed algorithm by comparing it to the other approaches available in the recent literature which were tested in the same simulation environment and experimental conditions. Based on the results from conducted experiments, the proposed improved Harris hawks optimization algorithm outperformed other state-of-the-art approaches by reducing cost and makespan performance metrics.

Workflow scheduling is one of the most popular and challenging problems in cloud computing. However, among the studies on cloud workflow scheduling, very few consider the fairness among workflow tasks which could significantly delay the workflows and hence deteriorates user satisfaction. In this paper, we propose a workflow scheduling algorithm based on stable matching game theory to minimize workflow makespan and ensure the fairness among the tasks. The local optimization methods based on critical path and task duplication are developed to improve the performance of the algorithm. In addition, a novel evaluation metric is proposed to measure the fairness among workflow tasks. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with other four representative algorithms. Experimental results demonstrate that our algorithm outperforms the other compared algorithms in terms of all three performance metrics under different workflow applications.

Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues

Load balancing in cloud computing assigning the all load over each node of a mutual or shared system to make resource usage effective and to perform the response time to the job. This has used Round Robin (RR) and shortest job first (SJF) algorithm. Scheduling is the main part of cloud computing and jobs or workflow scheduling under the platform as a service. The work under infrastructure as a service work to virtual machine scheduling which machine is decided by scheduler should go on which job or virtual machine. Simply RR is a time quantum-based preemptive algorithm, and SJF is a burst time-based non-preemptive and preemptive algorithm. But RR work in two-phase like expected finish time adapted for each process. Each process with generally minimum completion times scheduled. This algorithm result as no longer waiting for the smallest task.