Scheduling Multi-tenant Cloud Workloads on Accelerator-Based Systems

2014 ◽  
Author(s):  
Dipanjan Sengupta ◽  
Anshuman Goswami ◽  
Karsten Schwan ◽  
Krishna Pallavi
Keyword(s):  
Cloud Workloads

utmem: Towards Memory Elasticity in Cloud Workloads

2018 ◽  
pp. 173-183
Author(s):  
Aimilios Tsalapatis ◽  
Stefanos Gerangelos ◽  
Stratos Psomadakis ◽  
Konstantinos Papazafeiropoulos ◽  
Nectarios Koziris
Keyword(s):  
Cloud Workloads

scholarly journals RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Lu ◽  
John Panneerselvam ◽  
Lu Liu ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Service Providers ◽  
Cloud Service ◽  
Backpropagation Neural Network ◽  
Environmental Implications ◽  
Cloud Service Providers ◽  
Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

scholarly journals Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model

2020 ◽  
Vol 171 ◽  
pp. 158-167
Author(s):  
Eva Patel ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Cloud Workloads

scholarly journals Security-Aware Autonomic Allocation of Cloud Resources

2020 ◽  
Vol 32 (3) ◽  
pp. 15-22
Author(s):  
Sukhpal Singh Gill ◽  
Arash Shaghaghi
Keyword(s):  
Denial Of Service ◽  
Future Research ◽  
Allocation Of Resources ◽  
Foreseeable Future ◽  
Dos Attacks ◽  
Allocation Model ◽  
Cloud Workloads ◽  
Key Factor ◽  
Security And Reliability ◽  
Heterogeneous Cloud

Cloud computing has emerged as a dominant platform for computing for the foreseeable future. A key factor in the adoption of this technology is its security and reliability. Here, this article addresses a key challenge which is the secure allocation of resources. The authors propose a security-based resource allocation model for execution of cloud workloads called STARK. The solution is designed to ensure security against probing, User to Root (U2R), Remote to Local (R2L) and Denial of Service (DoS) attacks whilst the execution of heterogeneous cloud workloads. Further, this paper highlights the promising directions for future research.

Micro-architectural characterization of desktop cloud workloads

2012 ◽  
Author(s):  
Tao Jiang ◽  
Rui Hou ◽  
Lixin Zhang ◽  
Ke Zhang ◽  
Licheng Chen ◽  
...  
Keyword(s):  
Cloud Workloads

Economical and Robust Provisioning of N-Tier Cloud Workloads: A Multi-level Control Approach

2011 ◽  
Author(s):  
Pengcheng Xiong ◽  
Zhikui Wang ◽  
Simon Malkowski ◽  
Qingyang Wang ◽  
Deepal Jayasinghe ◽  
...  
Keyword(s):  
Level Control ◽  
Control Approach ◽  
Cloud Workloads ◽  
Multi Level
Sign in / Sign up

Export Citation Format

Share Document