scholarly journals qCon: QoS-Aware Network Resource Management for Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3444 ◽  
Author(s):  
Cheol-Ho Hong ◽  
Kyungwoon Lee ◽  
Minkoo Kang ◽  
Chuck Yoo
Keyword(s):  
Resource Management ◽  
Network Performance ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Small Data ◽  
Network Resources ◽  
Network Resource ◽  
Network Bandwidth ◽  
Computing Paradigm ◽  
Network Resource Management

Fog computing is a new computing paradigm that employs computation and network resources at the edge of a network to build small clouds, which perform as small data centers. In fog computing, lightweight virtualization (e.g., containers) has been widely used to achieve low overhead for performance-limited fog devices such as WiFi access points (APs) and set-top boxes. Unfortunately, containers have a weakness in the control of network bandwidth for outbound traffic, which poses a challenge to fog computing. Existing solutions for containers fail to achieve desirable network bandwidth control, which causes bandwidth-sensitive applications to suffer unacceptable network performance. In this paper, we propose qCon, which is a QoS-aware network resource management framework for containers to limit the rate of outbound traffic in fog computing. qCon aims to provide both proportional share scheduling and bandwidth shaping to satisfy various performance demands from containers while implementing a lightweight framework. For this purpose, qCon supports the following three scheduling policies that can be applied to containers simultaneously: proportional share scheduling, minimum bandwidth reservation, and maximum bandwidth limitation. For a lightweight implementation, qCon develops its own scheduling framework on the Linux bridge by interposing qCon’s scheduling interface on the frame processing function of the bridge. To show qCon’s effectiveness in a real fog computing environment, we implement qCon in a Docker container infrastructure on a performance-limited fog device—a Raspberry Pi 3 Model B board.

A Discovery Technology Based on Distributed Resource Management Domain

2015 ◽  
Vol 713-715 ◽  
pp. 2195-2198
Author(s):  
Jun Li Mao ◽  
Xiang Luo ◽  
Xiao Zhen Wang ◽  
Chao Hong Yang
Keyword(s):  
Resource Management ◽  
Resource Selection ◽  
Resource Discovery ◽  
Network Resources ◽  
Network Resource ◽  
Management Domain ◽  
Network Resource Management ◽  
Long Time ◽  
Resource Organization ◽  
Resource Description

Resource discovery is the key of network resource management, which includes multiple aspects, such as resource description, resource organization, and resource discovery and resource selection. For a long time, communication network resourcehas been lack of unified and standardized description, causing users difficult to precisely find related resources in demand. This paper presents a distributed resource query methods based on management domain, including distributed resource query architecture, the basic process of resource discovery, update method,query methods and so on. The method of network resources makes use of collaborative queries to realize network resource discovery according to need.

scholarly journals A Virtual Network Resource Allocation Framework Based on SR-IOV

2019 ◽  
Vol 9 (1) ◽  
pp. 137
Author(s):  
Zhiyong Ye ◽  
Yuanchang Zhong ◽  
Yingying Wei
Keyword(s):  
Network Performance ◽  
Virtual Machines ◽  
Dynamic Allocation ◽  
Network Resources ◽  
Network Resource ◽  
Throughput Rate ◽  
Network Bandwidth ◽  
On Demand ◽  
Performance Requirements ◽  
And Performance

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

Software Defined Cognitive Radio Network Framework

2015 ◽  
Vol 7 (1) ◽  
pp. 15-31 ◽  
Author(s):  
Yaser Jararweh ◽  
Mahmoud Al-Ayyoub ◽  
Ahmad Doulat ◽  
Ahmad Al Abed Al Aziz ◽  
Haythem A. Bany Salameh ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Resource Management ◽  
Dynamic Spectrum Access ◽  
Network Performance ◽  
Base Station ◽  
Network Resources ◽  
Network Resource ◽  
Management Framework ◽  
Control Overhead ◽  
Control Scheme

Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

scholarly journals Bilateral Teleoperation Performance Model for Network Resource Management

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29106-29117
Author(s):  
Konstantinos Antonakoglou ◽  
Maliheh Mahlouji ◽  
Toktam Mahmoodi
Keyword(s):  
Resource Management ◽  
Performance Model ◽  
Bilateral Teleoperation ◽  
Network Resource ◽  
Network Resource Management
Sign in / Sign up

Export Citation Format

Share Document