On construction of heuristic QoS bandwidth management in clouds

Abstract

In recent years, cloud computing has become popular and its applications widespread. Thus, there exists a common concern, that is, how to arrange and monitor various resources in the cloud computing environment. In the literature, Ganglia and Network Weather Service (NWS) were used to monitor and gather node status and network-related data, respectively. With supports of Ganglia and NWS, one can effectively administer available resources in the cloud computing environment. In order to achieve high performance of cloud computing, comprehensive monitoring and efficient management are critical. Ganglia is often used to gather status data of resources, such as live states of hosts, CPU or memory utilizations, and surely Ganglia is also capable of monitoring network-related information; however, instead of Ganglia, we used NWS services to gather network-related information such as end-to-end transmission control protocol/Internet protocol performance data. Compared with Ganglia, NWS services offer more selections and flexibility for measurement schemes. Besides, NWS services could be deployed with nonintruding manner that makes it easier and faster in deploying services to cloud nodes. The network-related information is acquired immediately after deployment. Although NWS services also provide measurements for CPU and memory utilizations, but less functionality is provided by them than Ganglia in these aspects. Therefore, we combine advantageous features of Ganglia and NWS to achieve the aims of effective monitoring and management of available resources in the cloud environment. Nevertheless, Ganglia and NWS services may not provide sufficient data in realistic situations due to diversified needs of users, especially application developers. For instance, users are not able to directly access utilizations or allocations of resources in the cloud environment via interfaces or channels of Ganglia or NWS. In addition, NWS services based on a domain-based network information model could greatly decrease overheads caused by unnecessary measurements. Hence, we propose a heuristic QoS measurement approach based on the domain-based information model. This measurement approach is capable of providing essential information to satisfy user requirements, and thus let users manage and monitor various resources in the cloud environment in a more efficient way.

WLAN interference self‐optimization using som neural networks

“…To simulate the power efficiency, we randomly choose six APs as the maximum power at a time, which is showed in Figure . Then we do the total energy efficiency and let the network be more efficient and available .…”

“…To simulate the power efficiency, we randomly choose six APs as the maximum power at a time, which is showed in Figure 12. Then we do the total energy efficiency and let the network be more efficient and available [21][22][23][24][25]. Here, as step 1 is showed in Figure 13, we can see that in the situation of the maximum power, one AP cannot guarantee its cellar normal communication, there has to be some neighborhood APs to help its communication.…”

Virtualization in Cloud Environment: Bandwidth Management

“…It also avoids opposition between lower priority traffic and the critical applications for the limited network. With cloud tenants making greater use of cloud networking infrastructure in a shared manner, there is an increased desire and a growing concern on how to arrange, reserve and monitor the bandwidth in the cloud computing environment [6], [7].…”

Special issue: 2011 international conference on cloud and green computing (CGC2011)

“…This special issue contains eight papers based on those that were presented at CGC2011. They are listed as . Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages.…”

“…Paper makes an effort in this area of monitoring resources with QoS in cloud environment. The paper then designs a heuristic QoS measurement constructed with domain‐based information model.…”