scholarly journals AIMING: Resource Allocation with Latency Awareness for Federated-Cloud Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Wei ◽  
Ao Zhou ◽  
Jie Yuan ◽  
Fangchun Yang
Keyword(s):  
Resource Allocation ◽  
Quadratic Programming ◽  
Virtual Machines ◽  
Real Data ◽  
Network Resources ◽  
Resource Requirement ◽  
Total Latency ◽  
Real Time Applications ◽  
Cloud Applications ◽  
Allocation Approach

Federated-cloud has been widely deployed due to the growing popularity of real-time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated-cloud. Specifically, the network resources are deployed and selected according to k-means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Bottleneck-Aware Resource Allocation for Service Processes

2021 ◽  
Vol 18 (3) ◽  
pp. 1-21
Author(s):  
Shoulu Hou ◽  
Wei Ni ◽  
Ming Wang ◽  
Xiulei Liu ◽  
Qiang Tong ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Service Rate ◽  
Service Models ◽  
Service Rates ◽  
Simulation Results ◽  
5G Systems ◽  
Generic Service ◽  
The Impact ◽  
Process Simulator ◽  
Allocation Approach

In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

Sign in / Sign up

Export Citation Format

Share Document