Shared Resource Monitoring and Throughput Optimization in Cloud-Computing Datacenters

2011 ◽  
Author(s):  
Jaideep Moses ◽  
Ravi Iyer ◽  
Ramesh Illikkal ◽  
Sadagopan Srinivasan ◽  
Konstantinos Aisopos
Keyword(s):  
Cloud Computing ◽  
Throughput Optimization ◽  
Resource Monitoring ◽  
Shared Resource

Virtual resource monitoring in cloud computing

2011 ◽  
Vol 15 (5) ◽  
pp. 381-385 ◽  
Author(s):  
Fang-fang Han ◽  
Jun-jie Peng ◽  
Wu Zhang ◽  
Qing Li ◽  
Jian-dun Li ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Monitoring

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

scholarly journals Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shyamala Loganathan ◽  
Saswati Mukherjee
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Model ◽  
Scheduling Algorithm ◽  
Private Cloud ◽  
Resource Monitoring ◽  
Efficient Resource ◽  
Efficient Data ◽  
Guaranteed Service

Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

scholarly journals Cross-VM Branch Prediction Analysis Attack: Scope Assessment and Simulation

2019 ◽  
Vol 8 (2) ◽  
pp. 4868-4873
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Cloud Security ◽  
Branch Prediction ◽  
Side Channel ◽  
Shared Resource ◽  
Prediction Analysis ◽  
Private Key ◽  
Cryptographic Algorithm ◽  
Primary Focus

Growing scope of cloud computing has made cloud security a challenging parameter. Among all the security parameters, virtualization security requires primary focus as it hides internal resource sharing details of the system. Side Channel Attack (SCA) is an attack that exploits the shared resource for extracting the private key of a cryptographic algorithm. Considering the significance of virtualization security, we need to analyze the SCA in a virtualization environment. In this paper, we target the Branch Prediction Analysis (BPA) Attack, one type of SCA. We have carried out an analysis to verify the scope of various BPA attack launching methods in virtualization environment along with the simulation. We have also analyzed the scope of existing solutions handling BPA attack.

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