Scheduling In Cloud Environment Research Articles

A Workflow Scheduling in Cloud Environment Using a Combination of Moth-Flame and Salp Swarm Algorithms

A Workflow Scheduling in Cloud Environment Using a Combination of Moth-Flame and Salp Swarm Algorithms

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

RVEA-based multi-objective workflow scheduling in cloud environments

Real-Time Multiple-Workflow Scheduling in Cloud Environments

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors that strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

Pareto based ant lion optimizer for energy efficient scheduling in cloud environment

The tremendous growth of cloud-based data centres leads to significant amount of energy. Thus, the prime concern for the cloud service providers is to generate environment friendly solution by minimizing the energy consumption. Further, due to increased demand of scientific workflow applications, cloud providers face a challenging issue of efficient workflow scheduling by minimizing the makespan. To address the above-mentioned contradictory issues, we proposed a Pareto based multi-objective discrete ant lion optimization algorithm (PBMO-DALO) to solve workflow-scheduling problem in cloud data centres with minimizing the conflicting objectives of makespan and energy consumption simultaneously. Distinguished from the original ant lion optimization algorithm, the proposed algorithm involves new encoding scheme for ants and antlions, their random walk and selection of fitter antlion for trap building to address the discrete nature of the workflow scheduling problem. Subsequently, the PBMO-DALO uses the Pareto dominance and crowding distance approach to tackle optimization of multiple objectives and to achieve the optimal solutions. The simulation results indicate that the proposed PBMO-DALO algorithm overwhelms other competing algorithms and generates good trade-off solutions with better convergence and uniform diversity.

A workflow based approach for task scheduling in cloud environment

A Cloud computing environment provides a huge repository of computing resources which can be utilized for executing user tasks over the Internet. One of the challenges in such a computing paradigm is the optimal use of resources both from the cloud provider as well as consumer’s point of view. Techniques have been developed to execute tasks with different level of complexity and size by assigning the tasks to the available resource pool which may span over a large geographical area. In this paper, we suggested a workflow approach to execute large number of interdependent tasks using a set of computing resources with heterogeneous capacity. The objective of this work is to allocate the tasks in such a way that the total time required to complete is minimized. The proposed technique is an extension of max–min algorithm applied on a Directed Acyclic Graph representing a set of interdependent tasks. The technique has been tested on standard scientific workflows and the results are found to be better compared to other similar works in terms of makespan.

TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING

Cloud computing is a new technology recently being developed seriously. Scheduling is an essential issue in the area of cloud computing. There is an extensive literature concerning scheduling in the area of distributed systems. Some of them are applicable for cloud computing. Traditional scheduling methods are unable to provide scheduling in cloud environments. According to a simple classification, scheduling algorithms in the cloud environment are divided into two main groups: batch mode and online heuristics scheduling. This paper focuses on the trust of cloud-based scheduling algorithms. According to the literature, the existing algorithm examinee latest algorithm is related to an algorithm trying to optimize scheduling using the Trust method. The existing algorithm has some drawbacks, including the additional overhead and inaccessibility to the past transaction data. This paper is an improvement of the trust-based algorithm to reduce the drawbacks of the existing algorithms. Experimental results indicate that the proposed method can execute better than the previous method. The efficiency of this method depends on the number of nods and tasks. The more trust in the number of nods and tasks, the more the performance improves when the time cost increases

Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.

Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm

Task scheduling in cloud environment is a hot topic in current research. Effective scheduling of massive tasks submitted by users in cloud environment is of great practical significance for increasing the core competitiveness of companies and enterprises and improving their economic benefits. Faced with the urgent need for an efficient scheduling strategy in the real world, this paper analyzed the process of cloud task scheduling, and proposed a particle swarm optimization genetic hybrid algorithm based on phagocytosis PSO_PGA. Firstly, each generation of particle swarm is divided, and the position of the particles in the sub population is changed by using phagocytosis mechanism and crossover mutation of genetic algorithm, so as to expand the search range of the solution space. Then the sub populations are merged, which ensures the diversity of particles in the population and reduces the probability of the algorithm falling into the local optimal solution. Finally, the feedback mechanism is used to feed back the flight experience of the particle itself and the flight experience of the companion to the next generation particle population, so as to ensure that the particle population can always move towards the direction of excellent solution. Through simulation experiments, the proposed algorithm is compared with several other existing algorithms, and the results show that the proposed algorithm significantly improves the overall completion time of cloud tasks, and has higher convergence accuracy. It shows the effectiveness of the algorithm in cloud task scheduling.

CWOA: Hybrid Approach for Task Scheduling in Cloud Environment

Abstract A cloud computing system typically comprises of a huge number of interconnected servers that are organized in a datacentre. Such servers dynamically cater to the on-demand requests put forward by the clients seeking solutions to their applications through an interface. The scheduling activity concerned with scientific applications is designated under the NP hard problem category since they make use of heterogeneous resources of dynamic capabilities. Recently cloud computing researchers had developed numerous meta-heuristic approaches for providing solutions to the challenges arising in the task scheduling activities. Scheduling of tasks poses a major concern in cloud computing environment. This decreases the efficiency of the system considerably, if not handled properly. Hence, an improvised task scheduling algorithm that enhances the performance of the cloud is needed. There are two factors that affect the cloud environment: service quality and energy usage. To increase the performance in above suggested factors (memory, makespan and energy efficiency), an efficient hybridized algorithm, obtained by integrating the Cuckoo Search Algorithm (CSA) and Whale Optimization Algorithm (WOA), called the CWOA had been proposed in this work. The performance of our proposed CWOA algorithm had been compared with Ant Colony Optimization, CSA and WOA and it was found to produce an improvement of 5.62%, 4.36% and 2.27% with respect to makespan, 16.36%, 19.19% and 13.13% with respect to memory utilization and 19.08%, 19.34% and 16.75% with respect to energy consumption parameters, respectively. Comprehensive results have been tabulated in the result section of this article.

Plummeting Makespan by Proficient Workflow Scheduling in Cloud Environment

Plummeting Makespan by Proficient Workflow Scheduling in Cloud Environment

Enhanced Time Quantum based Round Robin Algorithm for Cloudlet Scheduling in Cloud Environment

The contemporary organization integrates most of the required resources into their business environment by adopting or leasing the cloud resources that are offered by many of the remote cloud service providers. The cloud computing renders hassle free installation, deployment and maintenance of the resources. The cloud resources and services are observed to be pervasive in nature and therefore efficient technologies are to be adopted in order ensure the effective resource utilization and optimal service delivery to the cloud users. Generally virtualization, optimal scheduling techniques like cloudlet scheduling, virtual machine scheduling and load balancing strategies maximize the resource utilization and service delivery. This paper outlines the foundation concepts of cloud computing and proposes an algorithm named Enhanced Time Quantum based Round Robin Algorithm (ETQRRA). The ETQRRA is proposed to reduce the response time, waiting time, turnaround time and the number of context switching. The result of ETQRRA algorithm is compared with various versions of the existing Round Robin algorithms like Classic Round Robin Algorithm (CRR), Improved Shortest Remaining Burst Round Robin (ISRBRR), Half Life Variable Quantum Time Round Robin (HLVQTRR), Improved Round Robin Varying Quantum (IRRVQ) and Dynamic Time Quantum Round Robin (DTQRR) algorithms to prove the efficiency of ETQRRA. The comparative study shows the performance of ETQRRA outdo the other algorithms taken for the comparison.

Review on Workflow Scheduling In Cloud Environment: A Comprehensive Study

Review on Workflow Scheduling In Cloud Environment: A Comprehensive Study

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Energy Efficient and Reliability Aware Workflow Task Scheduling in Cloud Environment

With the rising demand for cloud services, the high energy consumption of cloud data centers is a significant problem that needs to be handled. The Dynamic Voltage and Frequency Scaling approach has been identified as one of the efficient techniques to conserve energy, particularly while scheduling real-world scientific workflows. Moreover, scientific workflows demand high-availability of the system. The computational systems in the cloud data centers are not failure-free and further frequency scaling impacts negatively on the reliability of the system by increasing the transient fault rate. A trade-off is required between energy conservation and the reliability of the computational machine. In this paper, we propose an energy-efficient and reliability aware workflow task scheduling in a cloud environment (EERS) algorithm, which conserves energy and maximizes the system reliability. The EERS comprises five sub-algorithms. First, we apply a task rank calculation algorithm to preserve the task dependencies. Second, a task clustering algorithm to reduce the communication cost which reduces energy consumption. The third is the sub-target time distribution algorithm to define the sub_makespan for each task. Further, we propose a cluster-VM mapping algorithm that reduces energy and maximizes system reliability and finally, a slack algorithm to reclaim slack associated with the non-critical tasks. The performance of the EERS evaluated on the WorkflowSim simulator using two real-world scientific workloads CyberShake and Montage. The results indicate that it surpasses the related existing approaches.

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

SummaryWorkflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

Load Balanced and Energy Aware Cloud Resource Scheduling Design for Executing Data-intensive Application in SDVC

Cloud computational platform provisions numerous cloud-based Vehicular Adhoc Network (VANET) applications. For providing better bandwidth and connectivity in dynamic manner, Software Defined VANET (SDVN) is developed. Using SDVN, new VANET framework are modeled; for example, Software Defined Vehicular Cloud (SDVC). In SDVC, the vehicle enables virtualization technology through SDVN and provides complex data-intensive workload execution in scalable and efficient manner. Vehicular Edge Computing (VEC) addresses various challenges of fifth generation (5G) workload applications performance and deadline requirement. VEC aid in reducing response time, delay with high reliability for workload execution. Here the workload tasks are executed to nearby edge devices connected to Road Side Unit (RSU) with limited computing capability. If the resources are not available in RSU, then the task execution is offloaded through SDN toward heterogeneous cloud server. Existing workload scheduling in cloud environment are designed considering minimizing cost and delay; however, very limited work has been done considering energy minimization for workload execution. This paper presents a Load Balanced and Energy Aware Cloud Resource Scheduling (LBEACRS) design for heterogeneous cloud framework. Experiment outcome shows the LBEACRS achieves better makespan and energy efficiency performance when compared with standard cloud resource scheduling design.

Orthogonal Grey Wolf Optimization Algorithm for Task Scheduling in Cloud Environment

Orthogonal Grey Wolf Optimization Algorithm for Task Scheduling in Cloud Environment

Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment

Mapping of workflow tasks to computational resources in the cloud environment has engendered research interest in workflow scheduling. As workflow scheduling belongs to NP-complete problem, so building an optimum workflow scheduler with reasonable performance and computation speed is very challenging in the heterogeneous distributed environment of clouds. Many existing studies deal with cloud workflow scheduling as a single or bi-objective optimization problem without considering some important requirements of the users or the providers. Therefore, it is highly desirable to formulate scheduling of the workflow applications as a Multi-objective Optimization Problem (MOP) taking into account the requirements from the user and the service provider. For example, the cloud workflow scheduler might wish to consider user’s Quality of Service (QoS) objectives, such as makespan and cost, as well as provider’s objectives, such as energy efficiency over the Virtual Machines (VMs). In addition, early convergence in existing algorithms is a problem that increases the number of repetitions for reaching a global optimum. To overcome these drawbacks, in this paper, an enhanced multi-objective co-evolutionary algorithm, called ch-PICEA-g, is proposed as an effective heuristic algorithm, where the logistic and tent maps as two chaotic systems are applied in generating chaotic values to overcome the permute convergence in the initial population and the genetic operators. Also, an improved fitness function is applied to increase the performance of original PICEA-g. The functionality of the proposed algorithm is validated by extensive experiments. The obtained results indicate that this proposed algorithm outperforms its counterparts in terms of different performance metrics.

Enhanced multi-verse optimizer for task scheduling in cloud computing environments

Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization.