FAIR: Fully-Adaptive Framework for Improving Resource Provisioning in Collaborative CPU-FPGA Cloud Environments

2021 ◽  
Author(s):  
Michael Guilherme Jordan ◽  
Guilherme Korol ◽  
Mateus Beck Rutzig ◽  
Antonio Carlos Schneider Beck
Keyword(s):  
Resource Provisioning ◽  
Fully Adaptive ◽  
Cloud Environments ◽  
Adaptive Framework

Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing

2019 ◽  
Vol 2 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Rajni Gupta
Keyword(s):  
Oil And Gas ◽  
Health Care Systems ◽  
Fog Computing ◽  
Fast Response ◽  
Resource Provisioning ◽  
Time Data ◽  
Computing Paradigm ◽  
Care Systems ◽  
Cloud Environments ◽  
Suitable Technique

Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

scholarly journals An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

Achieving Flexible SLA and Resource Management in Clouds

2012 ◽  
pp. 266-287 ◽  
Author(s):  
Vincent C. Emeakaroha ◽  
Marco A. S. Netto ◽  
Rodrigo N. Calheiros ◽  
César A. F. De Rose
Keyword(s):  
Resource Management ◽  
Large Scale ◽  
Management Strategies ◽  
Service Level ◽  
Resource Provisioning ◽  
Key Factors ◽  
Resource Monitoring ◽  
Efficient Management ◽  
Cloud Environments ◽  
Sla Management

One of the key factors driving Cloud computing is flexible and on-demand resource provisioning in a pay-as-you-go manner. This resource provisioning is based on Service Level Agreements (SLAs) negotiated and signed between customers and providers. Efficient management of SLAs and Cloud resources to reduce cost, achieve high utilization, and generate profit is challenging due to the large-scale nature of Cloud environments and complex resource provisioning processes. In order to advance the adoption of this technology, it is necessary to identify and address the issues preventing proper resource and SLA management. The authors purport that monitoring is the first step towards successful management strategies. Thus, this chapter identifies the SLA management and monitoring challenges in Clouds and federated Cloud environments, and proposes a novel resource monitoring architecture as a basis for resource management in Clouds. It presents the design and implementation of this architecture and presents the evaluation of the architecture using heterogeneous application workloads.

TRIPP: Transparent Resource Provisioning for Multi-Tenant CPU-GPU based Cloud Environments

2021 ◽  
Author(s):  
Julio Costella Vicenzi ◽  
Tiago Knorst ◽  
Michael G. Jordan ◽  
Guilherme Korol ◽  
Antonio Carlos Schneider Beck ◽  
...  
Keyword(s):  
Resource Provisioning ◽  
Cloud Environments

An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415 ◽  
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

Optimal Resource Provisioning in Federated-Cloud Environments

2015 ◽  
pp. 84-101
Author(s):  
Veena Goswami ◽  
Choudhury N. Sahoo
Keyword(s):  
Service Providers ◽  
Queueing Network ◽  
Resource Provisioning ◽  
New Paradigm ◽  
Management Framework ◽  
Software Applications ◽  
Optimal Resource ◽  
Cloud Environments ◽  
Optimal Resource Management ◽  
Multiple Clouds

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. Multiple Clouds can collaborate in order to integrate different service-models or service providers for end-to-end-requirements. Intercloud Federation and Service delegation models are part of Multi-Cloud environment where the broader target is to achieve infinite pool of resources. This chapter presents an optimal resource management framework for Federated-cloud environments. Each service model caters to specific type of requirements and there are already number of players with own customized products/services offered. They propose an analytical queueing network model to improve the efficiency of the system. Numerical results indicate that the proposed provisioning technique detects changes in arrival pattern, resource demands that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.

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