222 Hits in 8.8 sec

Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center

Jia Wang, Xiaoping Li, Rubén Ruiz, Hanchuan Xu, Dianhui Chu
2020 Service Oriented Computing and Applications  
With the adapted replica strategy in MapReduce workflow, a new task scheduling is proposed to improve data locality which assigns tasks to servers with the earliest completion time in order to ensure resource  ...  In this paper, an original MapReduce workflow scheduling with deadline and data locality is proposed to maximize total profit of resource providers.  ...  In this paper, n MapReduce workflow instances W = {W 1 , W 2 , . . . , W n } are processed by a cloud data center with m geo-distributed heterogeneous servers S = {S 1 , S 2 , . . . , S m }.  ... 
doi:10.1007/s11761-020-00290-1 fatcat:b7lwnpvci5bypd3me4lqrwtbci

Distributionin of Data Handling in Cloud Asset

2020 International Journal of Engineering and Advanced Technology  
In Cloud computing, task scheduling is one of the technique of specifying and assigning job to assets that finish the job.  ...  Scheduling is essential for computing and the notion of planning allows multitasking computers with single CPU as inner portion of a computer system's execution model.  ...  MapReduce implementation on such heterogeneous centers posed important difficulties relative to in-house devoted centers in attaining excellent application performance.  ... 
doi:10.35940/ijeat.c6031.029320 fatcat:5mx7tzbi6vcfnheqsqdbfvlyga

A Comprehensive View of MapReduce Aware Scheduling Algorithms in Cloud Environments

Hadi Yazdanpanah, Amin Shouraki, Abbas Ali
2015 International Journal of Computer Applications  
Cloud computing has emerged as a model that harnesses massive capacities of data centers to host services in a costeffective manner.  ...  It is designed to read large amount of data stored in a distributed file system such as Google File System (GFS), process the data in parallel, aggregate and store the results back to the distributed file  ...  However, since the reduce tasks are dependent on the map tasks, the data center has to determine a reasonable deadline for the map tasks with respect to the availability of the map slots in the data center  ... 
doi:10.5120/ijca2015906395 fatcat:m5fax4qzcbfgrhk6p7p2csxpw4


Neeraj Mangla, Manpreet Singh, Sanjeev Rana
2016 Advances in Science and Technology Research Journal  
In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided.  ...  With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative  ...  utilization of public Cloud services by up to 20% without any impact in the capa- city of meeting ap-plication deadlines [15] A PSO based scheduling of workflows on IaaS clouds Cost, Makespan,  ... 
doi:10.12913/22998624/62746 fatcat:dyky2j5kxbaulllhmtcidt6cjm

A Survey on Job Scheduling in Big Data

M. Senthilkumar, P. Ilango
2016 Cybernetics and Information Technologies  
Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing.  ...  In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce.  ...  Energy In Big Data applications large scale of data operations are held out by data centers with MapReduce.  ... 
doi:10.1515/cait-2016-0033 fatcat:psrtc3l3dzgkfklqjl6hr3qczi

A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks [article]

Sanaa Hamid Mohamed, Taisir E.H. El-Gorashi, Jaafar M.H. Elmirghani
2019 arXiv   pre-print
This survey article reviews the challenges associated with deploying and optimizing big data applications and machine learning algorithms in cloud data centers and networks.  ...  Moreover, we provide a brief review of data centers topologies, routing protocols, and traffic characteristics, and emphasize the implications of big data on such cloud data centers and their supporting  ...  All data are provided in full in the results section of this paper.  ... 
arXiv:1910.00731v1 fatcat:kvi3br4iwzg3bi7fifpgyly7m4

Scheduling Algorithms in Big Data: A Survey

2016 International Journal Of Engineering And Computer Science  
Scheduling is a technique of assigning jobs to available resources in a manner to minimize starvation and maximize resource utilization.  ...  It is not easy to quantify the large amount of data stored electronically. Data is in the unit of zettabytes or exabytes referred as Big Data. Hadoop system is used to process large datasets.  ...  ; and (3) in a heterogeneous data center, we need higher number of resources for the same request with respect to the deadline constraints.  ... 
doi:10.18535/ijecs/v5i8.53 fatcat:x4amgkxolrb6toe6fifofmzgfu

Application Layer Scheduling in Cloud: Fundamentals, Review and Research Directions

Vaibhav Pandey, Poonam Saini
2019 Computer systems science and engineering  
., a set of independent tasks, simple workflow, scientific workflow, and MapReduce jobs.  ...  For large-scale heterogeneous distributed systems like a cloud, scheduling is an essential component of resource management at the application layer as well as at the virtualization layer in order to deliver  ...  ACKNOWLEDGMENT The authors would like to thank Ministry of Electronics and Information Technology, Government of India, and Digital India Corporation for providing financial assistance to this work under  ... 
doi:10.32604/csse.2019.34.357 fatcat:ak6gdeqw4zdbzdvbot3sxlwwti

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Sukhpal Singh, Inderveer Chana
2016 Journal of Grid Computing  
In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies.  ...  Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications.  ...  To design a successful IaaS, initially understand the cloud workload (e.g. transactional database, file server, web server, application server and batch data processing) thoroughly.  ... 
doi:10.1007/s10723-015-9359-2 fatcat:cyhh4lnslfb6lhfcdif2pydkba

2021 Index IEEE Transactions on Services Computing Vol. 14

2022 IEEE Transactions on Services Computing  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  -Dec. 2021 2026-2040 Cloud computing A Distributed Truthful Auction Mechanism for Task Allocation in Mobile Cloud Computing. 2021 1306-1319 CALM: Survivable Virtual Data Center Allocation in Cloud Networks  ... 
doi:10.1109/tsc.2021.3135765 fatcat:cvny2iyuazb5tncphn74iq2o4e

A comprehensive view of Hadoop research—A systematic literature review

Ivanilton Polato, Reginaldo Ré, Alfredo Goldman, Fabio Kon
2014 Journal of Network and Computer Applications  
Lately, Apache Hadoop has attracted strong attention due to its applicability to Big Data processing.  ...  Solution: We conducted a systematic literature review to assess research contributions to Apache Hadoop.  ...  Table A1 and A2 Table A1 Studies with implementation and/or experiments (MapReduce and data storage & manipulation categories). Appendix A.  ... 
doi:10.1016/j.jnca.2014.07.022 fatcat:4xjveqy6mrctzjc4ou7llyy4u4

The Aneka platform and QoS-driven resource provisioning for elastic applications on hybrid Clouds

Rodrigo N. Calheiros, Christian Vecchiola, Dileban Karunamoorthy, Rajkumar Buyya
2012 Future generations computer systems  
By enhancing Desktop Grid infrastructures with Cloud resources, it is possible to offer QoS to users, motivating the adoption of Desktop Grids as a viable platform for application execution.  ...  The consolidation of this new paradigm in both enterprises and academia demanded reconsideration in the way IT resources are used, so Cloud computing can be used together with available resources.  ...  Acknowledgment The authors would like to thank Xingchen Chu for his contribution on the research and development of Aneka, which motivated the work presented in this paper.  ... 
doi:10.1016/j.future.2011.07.005 fatcat:hfqpomhbszatbb7u7wsc743lxi

Load Balancing Optimization Based on Deep Learning Approach in Cloud Environment

Amanpreet Kaur, Associate Professor, Chandigarh Engineering College, Landran (Mohali), Bikrampal Kaur, Parminder Singh, Mandeep Singh Devgan, Harpreet Kaur Toor
2020 International Journal of Information Technology and Computer Science  
A Framework for Workflow execution in cloud environment has been proposed and implemented, namely, Deep Learningbased Deadline-constrained, Dynamic VM Provisioning and Load Balancing (DLD-PLB).  ...  DL results in effective and accurate decision making of intelligent resource allocation to the incoming requests, thereby, choosing the most suitable resource to complete them.  ...  Algorithm: Prediction of schedule with effective resource utilization for deadline constrained workflow tasks Input: Cloud tasks with deadline constraint Output: predicting schedule with effective resource  ... 
doi:10.5815/ijitcs.2020.03.02 fatcat:ncsbiikxw5aldkrqgjr47q6s6e

Cloud Resource Allocation as Preemptive Scheduling Approach

Suhas YuvrajBadgujar, Anand Bone
2014 International Journal of Computer Applications  
The resources offered in the cloud are extremely dynamic and probably heterogeneous due to this dynamic load balancing, access balancing and scheduling of job is required.  ...  To achieve this many scheme are proposed, Nephele is one of the data processing framework which exploits the dynamic resource allocation offered by IaaS clouds for both task scheduling and execution.  ...  A data center with thousands of computers operates for processing of unstructured and heterogeneous data.  ... 
doi:10.5120/15452-3989 fatcat:dwzbdkziunbnhixsrzuzld247q

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Hyunjoo Kim, Yaakoub el-Khamra, Ivan Rodero, Shantenu Jha, Manish Parashar
2011 Scientific Programming  
In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures.  ...  To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and  ...  Experiments on the Amazon Elastic Compute Cloud (EC2) were supported by a grant from Amazon Web Services and CCT CyberInfrastructure Group grants.  ... 
doi:10.1155/2011/940242 fatcat:3zqirqk73vgsdlktcmbdfpxxze
« Previous Showing results 1 — 15 out of 222 results