Scheduling In Cloud Environment Research Articles

Fitness rate-based rider optimization enabled for optimal task scheduling in cloud

ABSTRACT Not long ago, there has been a dramatic augment in the attractiveness of cloud computing systems that depends computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. It is considered as an essential pool of resources, which are offered to users through Internet. Without troubling the fundamental infrastructure, pay-per-use computing resources are provided to the users by the cloud computing technology. Scheduling is a significant dilemma in cloud computing as a cloud provider has to serve multiple users in cloud environment. This proposal plans to implement an optimal task scheduling model in cloud sector as a challenge over the existing technologies. The proposed model solves the task scheduling problem using an improved meta-heuristic algorithm called Fitness Rate-based Rider Optimization Algorithm (FR-ROA), which is the advanced form of conventional Rider Optimization Algorithm (ROA). The objective constraints considered for optimal task scheduling are the maximum makespan or completion time, and the sum of the completion times of entire tasks. Since the proposed FR-ROA has attained the advantageous part of reaching the convergence in a small duration, the proposed model will outperform the other conventional algorithms for accomplishing the optimal task scheduling in cloud environment.

I-MaxStd: Toward Improving MaxStd Task Scheduling Technique for Cloud Computing Environment

Job scheduling process under the roof of Cloud is consisting of three phases: Resource Discovery, Resource Selection and Task Scheduling (Yousif, A., et al., 2011. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing, 4(3), pp.107.117). Among them, task scheduling is always treated a cumbersome activity because it mapped task(s) to their assigned resource(s) based on various constraints and impositions according to requirements. Task scheduling in Cloud environment is broadly categorized into two streams that are: Heuristic and Meta-Heuristic (sometimes combination of both). Heuristic approach is further categorized into two streams: Immediate mode or online approach and Batch mode or offline scheduling technique. MaxStd, heuristic mapping, is one of the efficient batch modes scheduling technique for independent task(s) due to its inherent efficiency and performance. In this paper, we have proposed an improved version of MaxStd (I-MaxStd) that refines the mapping process of conventional MaxStd to yields an efficient output in the form of reduced makespan and better resource average utilization rate without compromising its legacy. The validation of proposed work has been done for heterogeneous types of ETC matrices being used as dataset.

A hybrid genetic algorithm for scientific workflow scheduling in cloud environment

Nowadays, we live an unprecedented evolution in cloud computing technology that coincides with the development of the vast amount of complex interdependent data which make up the scientific workflows. All these circumstances developments have made the issue of workflow scheduling very important and of absolute priority to all overlapping parties as the provider and customer. For that, work must be focused on finding the best strategy for allocating workflow tasks to available computing resources. In this paper, we consider the scientific workflow scheduling in cloud computing. The main role of our model is to optimize the time needed to run a set of interdependent tasks in cloud and in turn reduces the computational cost while meeting deadline and budget. To this end, we offer a hybrid approach based on genetic algorithm for modelling and optimizing a workflow-scheduling problem in cloud computing. The heterogeneous earliest finish time (HEFT), an heuristic model, intervenes in the generation of the initial population. Based on results obtained from our simulations using real-world scientific workflow datasets, we demonstrate that the proposed approach outperforms existing HEFT and other strategies examined in this paper. In other words, experiments show high efficiency of our proposed approach, which makes it potentially applicable for cloud workflow scheduling. For this, we develop a GA-based module that was integrated to the WorkflowSim framework based on CloudSim.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

Analytical Approaches for Dynamic Scheduling in Cloud Environments

Brief Biography: Seyyed Ahmad Javadi is a researcher in the Computer Laboratory (CompAcctSys group1) at the University of Cambridge, His primary research interests include cloud computing and the Internet of Things (IoT). His current research involves addressing audit logging challenges in IoT environments, online social platforms, and cloud computing environments.

An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

Performance Evaluation of Task Scheduling using Hybrid Meta-heuristic in Heterogeneous Cloud Environment

Cloud computing is a ubiquitous platform that offers a wide range of online services to clients including but not limited to information and software over the Internet. It is an essential role of cloud computing to enable sharing of resources on-demand over the network including servers, applications, storage, services, and database to the end-users who are remotely connected to the network. Task scheduling is one of the significant function in the cloud computing environment which plays a vital role to sustain the performance of the system and improve its efficiency. Task scheduling is considered as an NP-complete problem in many contexts, however, the heterogeneity of resources in the cloud environment negatively influence on the job scheduling process. Furthermore, on the other side, the heuristic algorithms have satisfying performance but unable to achieve the desired level of the efficiency for optimizing the scheduling in a cloud environment. Thus, this paper aims at evaluating the effectiveness of the hybrid meta-heuristic that incorporate genetic algorithm along with DE algorithm (GA-DE) in terms of make-span. In addition, the paper also intends to enhance the performance of the task scheduling in the heterogeneous cloud environment exploiting the scientific workflows (Cybershake, Montage, and Epigenomics). The proposed algorithm (GA-DE) has been compared against three heuristic algorithms, namely: HEFT-Upward Rank, HEFT – Downward Rank, and HEFT – Level Rank. The simulation results prove that the proposed algorithm (GA-DE) outperforms the other existing algorithms in all cases in terms of make-span.

Research Scope and Tools for Workflow Scheduling in Cloud Environment

Workflow is a prototype that executes the behavior of scientific and engineering applications for which the sequence of tasks needs to be automated based on the input parameters specified. Difficulties arise for CSPs primarily during the execution environment due to its direct impact on various QoS parameters. Existing workflow scheduling techniques has research focus with dimensions that include undetermined demands, task failure,l delay cost, ambiguous deadlines, bandwidth, cache inclusion, scheduler policies, VM cycles, QoS impact, OS support, fault-tolerant and virtualization level. The comparative analysis made in this paper for workflow scheduling strategies and tools used in the cloud environment towards QoS parameters would have a greater impact on industry task automation which in turn provides the researchers to set their objectives and tools to be used in-order to bring forth new approaches and solutions. This paper can be extended considering the research scope given in the comparative analysis and the data size pertaining to each task in workflow and data migration issues which cannot be done without cost, legal policies and technical issues.

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO.

Load balancing in cloud computing using water wave algorithm

SummaryCloud computing as of late is a rising innovation for giving various administrations through internet to fulfil the requisites of clients dependent on needs with minimal expense. The client can benefit all kind of administration without building, observing, and keeping up the assets. Researchers developed distinctive asset planning calculation to satisfy the client requisites as per their condition. This article proposes a Water Wave Algorithm (WWA) for resource scheduling in cloud environment. The results for throughput, Response time, Turnaround time, Migration time, Resource utilization, Fault tolerance, and scalability over FCFS, MCT, MET, and OLB algorithms and the results prove that WWA provides better results for throughput, response time, resource utilization, and scalability.

Hybrid Optimization EHO-GA for Task scheduling in Cloud Environments

Cloud computing is an emerging technology with highly scalable service adopted by different kinds of people from around the world. In cloud environments one of the major problems is task scheduling; most of existing algorithm is not optimal. The proposed hybrid optimization method has combination of Elephant Herd Optimization (EHO) and Genetic Algorithm (GA) for find an optimal resource to schedule task in the Cloud. This proposed method has improves the performance of task scheduling by considering the parameters of response time, makespan, and cost of the cloud. The proposed method has implemented in CloudSim 3.0 toolkit and evaluated the performance with existing algorithm. The Experimental results were proven that proposed algorithm has given better performance compared to other scheduling algorithm.

Workflow Scheduling in Cloud Environment Using Firefly Optimization Algorithm

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.

Enhancement of Security and Energy Efficiency in Cloud Computing

This paper focuses on the job scheduling in cloud environment. Here the task has been scheduled in cloud and fog. Cloud provides services to heavy application while fog provides service to lighter application. The job scheduler would be helpful to reduce burden of cloud and help in energy optimization. The jobs are scheduled according to their types and priority. Various job scheduling algorithm such as gang scheduling, FCFS and round robin mechanism have been discussed in this research for load balancing and improve the compilation time. The simulation has been made using Matlab on virtual machines.

Modified Artificial Fish Swarm Algorithm for Efficient Task Scheduling in Cloud Environment

Modified Artificial Fish Swarm Algorithm for Efficient Task Scheduling in Cloud Environment

A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization

In cloud computing, varied demands are placed on the constantly changing resources. The task scheduling place very vital role in cloud computing environments, this scheduling process needs to schedule the tasks to virtual machine while reducing the makespan and cost. The task scheduling problem comes under NP hard category. Efficient scheduling method makes cloud computing services better and faster. In general, optimization algorithms are used to solve the scheduling issues in cloud. So, in this paper we combined two optimization algorithms namely called as Cuckoo Search (CS) and Particle Swarm Optimization (PSO).The new proposed hybrid algorithm is called as, CS and particle swarm optimization (CPSO). Our main purpose of the proposed paper is to reduce the makespan, cost and deadline violation rate. The performance of the proposed CPSO algorithm is evaluated using cloudsim toolkit. From the simulation results our proposed works minimize the makespan, cost, deadline violation rate, when compared to PBACO, ACO, MIN–MIN, and FCFS.

Enhanced Particle Swarm Optimization for Scientific Workflow Scheduling in Cloud Environments

Enhanced Particle Swarm Optimization for Scientific Workflow Scheduling in Cloud Environments

TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment

Cloud computing is an emerging paradigm that offers various services for both users and enterprisers. Scheduling of user tasks among data centers, host and virtual machines (VMs) becomes challenging issues in cloud due to involvement of vast number of users. To address such issues, a new multi-criteria approach i.e., technique of order precedence by similarity to ideal solution (TOPSIS) algorithm is introduced to perform task scheduling in cloud systems. The task scheduling is performed in two phases. In first phase, TOPSIS algorithm is applied to obtain the relative closeness of tasks with respect to selected scheduling criteria (i.e., execution time, transmission time and cost). In second phase the particle swarm optimization (PSO) begins with computing relative closeness of the given three criteria for all tasks in all VMs. A weighted sum of execution time, transmission time and cost used as an objective function by TOPSIS to solve the problem of multi-objective task scheduling in cloud environment. The simulation work has been done in CloudSim. The performance of proposed work has been compared with PSO, dynamic PSO (DPSO), ABC, IABC and FUGE algorithms on the basis of MakeSpan, transmission time, cost and resource utilization. Experimental results show approximate 75% improvement on average utilization of resources than PSO. Processing cost of TOPSIS–PSO reduced at approximate 23.93% and 55.49% than IABC and ABC respectively. The analysis also shows that TOPSIS–PSO algorithm reduces 3.1, 29.1 and 14.4% MakeSpan than FUGE, ant colony optimization (ACO) and multiple ACO respectively. Plotted graphs and calculated values show that the proposed work is very innovative and effective for task scheduling. This TOPSIS method to calculate relative closeness for PSO has been remarkable.

Reliability and energy efficient workflow scheduling in cloud environment

Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms.

MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

Job Scheduling in Cloud Environment Using Lion Algorithm

Job Scheduling in Cloud Environment Using Lion Algorithm