scholarly journals TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING

2021 ◽  
Vol 4 (1) ◽  
pp. 39-47
Author(s):  
Farshad Rezaei ◽  
◽  
Shamsollah Ghanbari
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
New Technology ◽  
Scheduling Algorithms ◽  
Previous Method ◽  
Traditional Scheduling ◽  
Extensive Literature ◽  
Batch Mode ◽  
Cloud Environments ◽  
Better Than

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

An API for Development of User-Defined Scheduling Algorithms in Aneka PaaS Cloud Software

2019 ◽  
pp. 170-187 ◽  
Author(s):  
Rajinder Sandhu ◽  
Adel Nadjaran Toosi ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Scheduling Algorithms ◽  
Research Area ◽  
Programming Models ◽  
Hybrid Cloud ◽  
Main Research ◽  
Cloud Application ◽  
Cloud Environments ◽  
Cloud Infrastructures

Cloud computing provides resources using multitenant architecture where infrastructure is created from one or more distributed datacenters. Scheduling of applications in cloud infrastructures is one of the main research area in cloud computing. Researchers have developed many scheduling algorithms and evaluated them using simulators such as CloudSim. Their performance needs to be validated in real-time cloud environments to improve their usefulness. Aneka is one of the prominent PaaS software which allows users to develop cloud application using various programming models and underline infrastructure. This chapter presents a scheduling API developed for the Aneka software platform. Users can develop their own scheduling algorithms using this API and integrate it with Aneka to test their scheduling algorithms in real cloud environments. The proposed API provides all the required functionalities to integrate and schedule private, public, or hybrid cloud with the Aneka software.

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

scholarly journals A hybrid approach for scheduling applications in cloud computing environment

2020 ◽  
Vol 10 (2) ◽  
pp. 1387
Author(s):  
Ahmed Subhi Abdalkafor ◽  
Khattab M. Ali Alheeti
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Positive Impact ◽  
Scheduling Algorithm ◽  
Hybrid Approach ◽  
Scheduling Algorithms ◽  
Vital Role ◽  
Experimental Result ◽  
Computing Performance ◽  
Cloud Applications

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.

scholarly journals Using Kubernetes as an ATLAS computing site

2020 ◽  
Vol 245 ◽  
pp. 07025
Author(s):  
Fernando Harald Barreiro Megino ◽  
Jeffrey Ryan Albert ◽  
Frank Berghaus ◽  
Kaushik De ◽  
FaHui Lin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Lessons Learned ◽  
Tier 2 ◽  
Cloud Infrastructure ◽  
Atlas Experiment ◽  
Computing Services ◽  
The Status ◽  
Cloud Environments ◽  
The University

In recent years containerization has revolutionized cloud environments, providing a secure, lightweight, standardized way to package and execute software. Solutions such as Kubernetes enable orchestration of containers in a cluster, including for the purpose of job scheduling. Kubernetes is becoming a de facto standard, available at all major cloud computing providers, and is gaining increased attention from some WLCG sites. In particular, CERN IT has integrated Kubernetes into their cloud infrastructure by providing an interface to instantly create Kubernetes clusters, and the University of Victoria is pursuing an infrastructure-as-code approach to deploying Kubernetes as a flexible and resilient platform for running services and delivering resources. The ATLAS experiment at the LHC has partnered with CERN IT and the University of Victoria to explore and demonstrate the feasibility of running an ATLAS computing site directly on Kubernetes, replacing all grid computing services. We have interfaced ATLAS’ workload submission engine PanDA with Kubernetes, to directly submit and monitor the status of containerized jobs. We describe the integration and deployment details, and focus on the lessons learned from running a wide variety of ATLAS production payloads on Kubernetes using clusters of several thousand cores at CERN and the Tier 2 computing site in Victoria.

scholarly journals HAMM: A Hybrid Algorithm of Min-Min and Max-Min Task Scheduling Algorithms in Cloud Computing

2020 ◽  
Vol 9 (4) ◽  
pp. 209-218
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Scheduling Algorithms ◽  
Cloud Computing Environment ◽  
Concurrent Execution ◽  
Optimization Parameters ◽  
Better Than

Nowadays, with the huge development of information and computing technologies, the cloud computing is becoming the highly scalable and widely computing technology used in the world that bases on pay-per-use, remotely access, Internet-based and on-demand concepts in which providing customers with a shared of configurable resources. But, with the highly incoming user’s requests, the task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing of a workload among cloud resources to enhance the overall cloud system performance. For these reasons, various types of task scheduling algorithms are introduced such as traditional, heuristic, and meta-heuristic. A heuristic task scheduling algorithms like MET, MCT, Min-Min, and Max-Min are playing an important role for solving the task scheduling problem. This paper proposes a new hybrid algorithm in cloud computing environment that based on two heuristic algorithms; Min-Min and Max-Min algorithms. To evaluate this algorithm, the Cloudsim simulator has been used with different optimization parameters; makespan, average of resource utilization, load balancing, average of waiting time and concurrent execution between small length tasks and long size tasks. The results show that the proposed algorithm is better than the two algorithms Min-Min and Max-Min for those parameters

Job Scheduling in Cloud Using Seagull Optimization Algorithm

2021 ◽  
pp. 27-40
Author(s):  
Meenakshi Garg ◽  
Gaurav Dhiman
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Job Scheduling ◽  
Scheduling Algorithms ◽  
It Services ◽  
On Demand ◽  
Resource Dynamics ◽  
Np Complete ◽  
Application Requirements ◽  
The Web

In recent years, cloud computing technology has gained a great deal of interest from both academia and industry. Cloud computing's success benefited from its ability to offer global IT services such as core infrastructure, platforms, and applications to cloud customers around the web. It also promises on-demand offerings and new ways of pricing packages. However, cloud job scheduling is still NP-complete and has become more difficult due to certain factors such as resource dynamics and on-demand customer application requirements. To fill this void, this chapter presents the seagull optimization algorithm (SOA) for scheduling work in the cloud world. The efficiency of the SOA approach is compared to that of state-of-the-art job scheduling algorithms by having them all implemented in the CloudSim toolkit.

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