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

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.

GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters

Allocating bandwidth guarantees to applications in the cloud has become increasingly demanding and essential as applications compete to share cloud network resources. However, cloud-computing providers offer no network bandwidth guarantees in a cloud environment, predictably preventing tenants from running their applications. Existing schemes offer tenants practical cluster abstraction solutions emulating underlying physical network resources, proving impractical; however, providing virtual network abstractions has remained an essential step in the right direction. In this paper, we consider the requirements for enabling the application-aware network with bandwidth guarantees in a Virtual Data Center (VDC). We design GANA-VDC, a network virtualization framework supporting VDC application-aware networking with bandwidth guarantees in a cloud datacenter. GANA-VDC achieves scalability using an interceptor to translate OpenFlow features to prompt fine-grained Quality of Service (QoS). Facilitating the expression of diverse network resource demands, we also propose a new Virtual Network (VN) to Physical Network (PN) mapping approach, Graph Abstraction Network Architecture (GANA), which we innovatively introduce in this paper, allowing tenants to provide applications with cloud networking environment, thereby increasing the preservation performance. Our results show GANA-VDC can provide bandwidth guarantee and achieve low time complexity, yielding higher network utility.

Scalable Constraint-based Virtual Data Center Allocation

Constraint-based techniques can solve challenging problems arising from highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To solve this problem, we introduce NETSOLVER, which is based on the general-purpose constraint solver MONOSAT. NETSOLVER represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. NETSOLVER scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, NETSOLVER can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Essential to our solution efficiency is our formulation of VDC allocation using monotonic theories, illustrating the practical value of the recently proposed SAT modulo monotonic theories approach.