scholarly journals ANCS: Achieving QoS through Dynamic Allocation of Network Resources in Virtualized Clouds

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Cheol-Ho Hong ◽  
Kyungwoon Lee ◽  
Hyunchan Park ◽  
Chuck Yoo
Keyword(s):  
Cloud Computing ◽  
Lightweight Design ◽  
Dynamic Allocation ◽  
Network Resources ◽  
Network Resource ◽  
Distribute System ◽  
Bandwidth Limitation ◽  
Scheduling Method ◽  
Minimum Bandwidth ◽  
Computing Platforms

To meet the various requirements of cloud computing users, research on guaranteeing Quality of Service (QoS) is gaining widespread attention in the field of cloud computing. However, as cloud computing platforms adopt virtualization as an enabling technology, it becomes challenging to distribute system resources to each user according to the diverse requirements. Although ample research has been conducted in order to meet QoS requirements, the proposed solutions lack simultaneous support for multiple policies, degrade the aggregated throughput of network resources, and incur CPU overhead. In this paper, we propose a new mechanism, called ANCS (Advanced Network Credit Scheduler), to guarantee QoS through dynamic allocation of network resources in virtualization. To meet the various network demands of cloud users, ANCS aims to concurrently provide multiple performance policies; these include weight-based proportional sharing, minimum bandwidth reservation, and maximum bandwidth limitation. In addition, ANCS develops an efficient work-conserving scheduling method for maximizing network resource utilization. Finally, ANCS can achieve low CPU overhead via its lightweight design, which is important for practical deployment.

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%.

Multi-Dimensional Performance Optimization in Cloud Computing

2017 ◽  
Vol 14 (1) ◽  
pp. 335-340
Author(s):  
Yu Shengji ◽  
Xiang Yanping
Keyword(s):  
Cloud Computing ◽  
Performance Optimization ◽  
Service Providers ◽  
Cloud Service ◽  
Function Technique ◽  
Service Time Distribution ◽  
Network Resource ◽  
Optimal Network ◽  
Multi Agent ◽  
Computing Platforms

The challenge of multi-dimensional performance optimization has been extensively addressed in the literature based on deterministic parameters. Since resources in Cloud Computing platforms are geographically separated and heterogeneous, it is rather difficult to apply a uniform distribution algorithm for achieving various optimization goals. Based on the analysis of cloud service performance measures, this paper proposes an approach for optimal network resource distribution managed by the multi-agent system (MAS), which is aimed to satisfy both the users’ and the service providers’ requirements. Moreover, a communication algorithm that uses the universal generating function technique is proposed to obtain the service time distribution efficiently.

scholarly journals Nxt-Freedom: Considering VDC-Based Fairness in Enforcing Bandwidth Guarantees in Cloud Datacenter

2020 ◽  
Vol 10 (21) ◽  
pp. 7874
Author(s):  
Shuo Wang ◽  
Zhiqiang Zhou ◽  
Hongjie Zhang ◽  
Jing Li
Keyword(s):  
Virtual Machine ◽  
Virtual Network ◽  
Specific Information ◽  
Network Resources ◽  
Network Resource ◽  
Free Network ◽  
Minimum Bandwidth ◽  
Cloud Datacenter ◽  
Bandwidth Guarantees ◽  
Cloud Network

In the cloud datacenter, for the multi-tenant model, network resources should be fairly allocated among VDCs (virtual datacenters). Conventionally, the allocation of cloud network resources is on a best-effort basis, so the specific information of network resource allocation is unclear. Previous research has either aimed to provide minimum bandwidth guarantee, or focused on realizing work conservation according to the VM-to-VM (virtual machine to virtual machine) flow policy or per-source policy, or both policies. However, they failed to consider allocating redundant bandwidth among VDCs in a fair way. This paper presents a bandwidth that guarantees enforcement framework NXT-Freedom, and this framework allocates the network resources on the basis of per-VDC fairness, which can achieve work conservation. In order to guarantee per-VDC fair allocation, a hierarchical max–min fairness algorithm is put forward in this paper. In order to ensure that the framework can be applied to non-congestion-free network core and achieve scalability, NXT-Freedom decouples the computation of per-VDC allocation from the execution of allocation, but it brings some CPU overheads resulting from bandwidth enforcement. We observe that there is no need to enforce the non-blocking virtual network. Leveraging this observation, we distinguish the virtual network type of VDC to eliminate part of the CPU overheads. The evaluation results of a prototype prove that NXT-Freedom can achieve the isolation of per-VDC performance, which also shows fast adaption to flow variation in cloud datacenter.

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Danielle V. Handel ◽  
Anson T. Y. Ho ◽  
Kim P. Huynh ◽  
David T. Jacho-Chávez ◽  
Carson H. Rea
Keyword(s):  
Cloud Computing ◽  
Data Science ◽  
Internet Access ◽  
Next Generation ◽  
Web Browser ◽  
Computer Laboratory ◽  
Economics Students ◽  
Computing Platforms

AbstractThis paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

scholarly journals Predictability of IP Address Allocations for Cloud Computing Platforms

2020 ◽  
Vol 15 ◽  
pp. 500-511 ◽  
Author(s):  
Hussain M. J. Almohri ◽  
Layne T. Watson ◽  
David Evans
Keyword(s):  
Cloud Computing ◽  
Ip Address ◽  
Computing Platforms

Layered virtual machine migration algorithm for network resource balancing in cloud computing

2017 ◽  
Vol 12 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Xiong Fu ◽  
Juzhou Chen ◽  
Song Deng ◽  
Junchang Wang ◽  
Lin Zhang
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machine Migration ◽  
Network Resource

A Research of GIS Software Application Based on Cloud Computing

2014 ◽  
Vol 513-517 ◽  
pp. 2107-2110 ◽  
Author(s):  
Zhi Jian Diao ◽  
Song Guo
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Information Storage ◽  
Application Layer ◽  
Cloud Infrastructure ◽  
Network Resource ◽  
Software Application ◽  
Computing Model ◽  
Two Layer Model ◽  
Gis Software

Cloud computing is a novel network-based computing model, in which the cloud infrastructure is constructed in bottom level and provided as the support environment for the applications in upper cloud level. The combination of clouding computing and GIS can improve the performance of GIS, and it can also provide a new prospect of GIS information storage, processing and utilization. By integrating cloud computing and GIS, this paper presented a cloud computing based GIS model based on two features of cloud computing: data storage and transparent custom service. The model contains two layers: service layer and application layer. With this two-layer model, GIS can provide stable and efficient services to end users by optimized network resource allocation of underlying data and services in cloud computing.

Network resource mobilisation limitations and the alliance portfolio network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Miguel Bolivar ◽  
Ignacio Castro-Abancéns ◽  
Cristóbal Casanueva ◽  
Angeles Gallego
Keyword(s):  
Financial Performance ◽  
Curvilinear Relationship ◽  
Network Resources ◽  
Network Resource ◽  
Alliance Portfolio ◽  
Value Capture ◽  
Content Type ◽  
Network Parameters ◽  
Resource Mobilisation ◽  
Practical Implications

PurposeThe purpose of this study is to examine how access and mobilisation of network resources influence a firm's performance. It has been established that alliance portfolio (AP) network parameters shape the access to network resources; however, resource access understood as value creation differs from resource mobilisation understood as value capture. Hence, the paper contributes towards the comprehension of AP performance by examining the extent to which a firm's level of network resource mobilisation (NRM) plays a role in improving financial performance and how this strategy conditions the benefits obtained from a firm's AP.Design/methodology/approachThis study employs an interorganisational network approach to describe the APs of firms; subsequently, it examines how AP network parameters and resource mobilisation determine financial performance. To this end, sequential multiple regression models are applied to a sample from the Top International Airlines database, covering 135 portfolios that correspond to 1117 codeshare partnerships.FindingsThe analyses show that the NRM level has an inverted U-shaped relationship with revenue performance, thereby revealing the limitations and considerations in the strategic alliance strategy. In addition, the authors show how the resource mobilisation decision moderates the faculty of AP parameters to influence a firm's financial performance, thereby exposing the nuanced relationship between AP size, diversity and redundancy. The findings convey strategic and practical implications for managers regarding how to capture value from their APs.Practical implicationsThe findings suggest the need for NRM to form part of a firm's AP management capability, so that, by acquiring superior strategic knowledge in NRM, the firm is able to extract value from its AP through the optimal exploitation of complementary assets.Originality/valuePrevious research has highlighted the multidimensional nature of APs at the theoretical level; however, no simultaneous empirical analysis of various AP parameters has yet been produced. The research empirically analyses an AP network and how its parameters affect financial performance in the presence of a resource mobilisation strategy. Not only do the authors introduce the analysis of the curvilinear relationship between the level of NRM and a firm's performance, but also of its role in advancing the AP literature.

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