Virtual Data Center Embedding Research Articles

Optimal network‐aware virtual data center embedding

SummaryRecently, the virtual data center embedding (VDCE) problem has drawn significant attention because of a growing need for efficient means of data center resource allocation. By ensuring a set of virtual data center (VDC) integration requests coming from his customers, among the main concern of an infrastructure provider is the maximization of the utilization rate of data center resources and benefits. However, existing VDCE solutions mostly focus on consolidating virtual machines in a single physical data center. Therefore, in this work, we improve the consolidated targets techniques, that consider only the virtual machines integration, by the consideration of network devices and fabrics (e.g., switches and paths/links). We consider new unreleased constraints such as multiple virtual nodes of the same request co‐location, and intermediate node requirements when a virtual link is mapped. To address the above problem, in this paper, we propose a binary linear programming‐based model, called BLP‐VDCE, to solve the VDCE problem with network‐aware consideration. This model ensures a simultaneous consolidated embedding of virtual nodes and virtual links. Extensive simulations show that solving the proposed BLP‐VDCE model can efficiently embed VDC requests with a high physical resource utilization rate.

Frequency-Adaptive VDC Embedding to Minimize Energy Consumption of Data Centers

The increasing popularity of cloud computing would require Data Centers (DCs) to be scaled up rapidly, as needed, to provide adequate computing and storage infrastructure with rapidly growing demands. Since energy costs would be crucially important for these DCs, various approaches have been proposed to operate them efficiently. Virtual data centers (VDCs) are a promising approach for this as they can efficiently provide computing and storage resources to users over a shared physical infrastructure. In the context of VDC services, this paper focuses on improving the energy efficiency of DCs by applying a Dynamic Frequency Scaling (DFS) mechanism to provision VDC services. Specifically, the frequencies applied in each hardware are adaptively adjusted as per the given service requirements. This is done to minimize the overall energy consumption in the DC hardware when embedding a specific VDC. To the best of our knowledge, this is the first work that incorporates this DFS mechanism in the VDC provisioning problem. To minimize the overall energy consumption, we develop both an integer linear programming (ILP) optimization model and efficient heuristic algorithms. Extensive simulations are conducted to show that incorporating the DFS mechanism in VDC service provisioning significantly improves the energy efficiency of a DC when compared with a scheme where this mechanism is not applied. The proposed heuristic algorithms are also efficient and perform almost as well as the optimum ILP model.

Efficient and Green Embedding of Virtual Data Centers with Mixture of Unicast and Multicast Services

The improved efficiency achieved by virtualizing data centers (DCs) has been well established. In this paper, we propose a mixed Virtual Data Center (VDC) capable of supporting both unicast and multicast services. We provide a new method to realize the embedding of these VDCs. We also provide a Mixed Integer Linear Programming (MILP) formulation and a scalable heuristic algorithm for efficiently embedding its demands. Numerical results show that mixed VDC embedding supporting both unicast and multicast services performs significantly better than existing embedding methods in terms of system cost, power consumption, link capacity utilization, and VDC acceptance ratio.

Fine granularity resource allocation of virtual data center with consideration of virtual switches

Virtual data center (VDC) embedding is being regarded as a critical issue to provide performance guarantees for the future success of cloud computing. Most existing works neglected the embedding of virtual switches (VSs), especially, on the physical switches (PSs), resulting in low utilization of physical resources. Moreover, most works assumed that multiple virtual nodes in the same request cannot be embedded in the same physical node, leading to low embedding efficiency in data centers. To address the above two problems, in this paper, we first propose two fine granularity models to formulate the VDC embedding problem efficiently with the consideration of two possible embedding positions of VSs. Then traditional flow conservation law is modified to allow multiple virtual nodes to coexist on the same physical node. Finally, we propose multiple efficient embedding algorithms to solve two NP-hard problems. Comparing with existing methods, our algorithms employing heuristic information from the relaxed model can find sub-optimal solutions in polynomial time. Extensive simulation results show that our proposals outperform the existing methods in terms of acceptance ratio, revenue, and utilization.

Temperature-Aware Virtual Data Center Embedding to Avoid Hot Spots in Data Centers

Virtual Data Center (VDC) embedding has drawn significant attention recently because of the growing need for efficient and flexible means of Data Center (DC) resource allocation. Existing studies on VDC embedding mainly focus on improving DCs' resource utilization. However, an important problem that has not been considered in VDC embedding solutions is the creation of hot spots by excessive heat dissipation and hot air generation from racks in DCs, which have significant adverse effects on energy consumption of the cooling system and IT equipment lifespan. To address this issue, we propose a temperature-aware VDC embedding scheme to avoid hot spots by minimizing the maximum temperature of hot air drawn to each rack. Meanwhile, we also aim to reduce the total power consumption of IT equipment in this scheme. A Mixed Integer Linear Programming (MILP) model and a heuristic algorithm are developed to implement the proposed VDC embedding scheme. Numerical results show that the proposed temperature-aware embedding scheme can significantly outperform a load-balanced embedding scheme in terms of maximum rack temperature, total power consumption of IT equipment, and VDC rejection ratio. Compared with a scheme that focuses only on maximizing IT resource utilization, which we refer to as IT-only energy-efficient scheme, our proposed scheme can significantly reduce maximum rack temperature and VDC rejection ratio while keeping the total power consumption of IT equipment close to that of an IT-only energy-efficient scheme.

Efficiently Consolidating Virtual Data Centers for Time-Varying Resource Demands

Data center virtualization is a flexible and efficient way to enable multiple users to share the common resources of a physical data center (DC). For efficient sharing, virtual data center (VDC) embedding is a vital problem that should be carefully addressed. However, existing studies on VDC embedding mostly assume that the capacity of each VDC is fixed, but do not consider the time-varying feature of resource demands. Considering the fact that the resource demands of most enterprise IT services exhibit the time-varying feature, resource allocation based on the fixed capacity assumption would cause a great inefficiency. To overcome this inefficiency, we propose a new VDC consolidation scheme that takes into account the time-varying feature of resource demands when embedding VDCs. We first develop a resource demand prediction model for each VDC using the Long Short-Term Memory (LSTM) neural network, which is used to predict the real-time resource demands of VDCs at different future moments. Based on the predicted resource demands, we then embed VDCs whose peaks and valleys of resource demands stagger each other onto common physical servers and links, such that the required physical resources can be minimized under the condition that all the resource demands of different VDCs are satisfied at all the different moments. An integer linear programming (ILP) model and a resource demand correlation-based heuristic algorithm are also developed for the proposed scheme. Simulation results show that the proposed consolidation scheme can significantly improve resource utilization in a DC. It can save up to 25 percent of physical servers and 29 percent of physical links used for accommodating the same requests as compared to a scheme assigning resources based on the fixed capacity assumption.

A maximally robustness embedding algorithm in virtual data centers with multi-attribute node ranking based on TOPSIS

The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which deals with the efficient mapping of required virtual network resources from the shared resources of the infrastructure provider (InP). In this paper, we propose a new VDC embedding algorithm that is different from previous works in many aspects. First, the provision of robustness for data center infrastructure is one of the critical requirements of cloud technology; however, this challenge has not been considered in the related literature. In order to analyze and evaluate the robustness of the infrastructure network, the classical and spectral graph robustness metrics are employed. Second, in order to avoid imbalance mapping and increase the efficiency of infrastructure resources, besides the resource dynamic capacity, four node attributes are exploited to compute the nodes mapping potential. The TOPSIS technique for nodes ranking has been used to increase the compatibility with the ideal solution. Third, unlike previous works in which the mapping phases of nodes and links are getting used to being separated, in the proposed algorithm, the virtual network is mapped to a physical network in a single step. Fourth, we also consider resources for network nodes (switches or routers). For these purposes, a multi-objective mathematical optimization problem is extracted with two goals of maximizing infrastructure network robustness and minimizing the long-term average cost-to-revenue ratio mapping for InPs. Finally, a new single-stage (non-dominated sorting-based genetic algorithm) NSGAII-based online VDCE algorithm is presented, where node mapping is TOP-MANR based and edge mapping is based on the shortest path. The fat-tree topology is considered for the substrate and virtual networks, and these two networks are modeled as a weighted undirected graph.

A survey and taxonomy on virtual data center embedding

Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important problem in data center network virtualization technology is virtual data center (VDC) embedding, which handles the physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, storage and network bandwidth) are both taken into account, the VDC embedding (VDCE) problem becomes NP-hard. The VDCE is so crucial that took wide consideration since it directly affects the execution, resource use and power consumption of data centers. To the best of our knowledge, there is no published work that precisely outlines open challenges connected with VDCE problem including all of its variants. On this point, this work tries to articulate this problem and bring research taxonomy for succinct classification of existing works. Moreover, we summarize the possible techniques already presented in the literature and we establish a classification based on a taxonomy study. At the end, we examine the limitations of existing solutions and identify the related open challenges.

Efficient virtual data center request embedding based on row-epitaxial and batched greedy algorithms

Data centers are becoming the main backbone of and centralized repository for all cloud-accessible services in on-demand cloud computing environments. In particular, virtual data centers (VDCs) facilitate the virtualization of all data center resources such as computing, memory, storage, and networking equipment as a single unit. It is necessary to use the data center efficiently to improve its profitability. The essential factor that significantly influences efficiency is the average number of VDC requests serviced by the infrastructure provider, and the optimal allocation of requests improves the acceptance rate. In existing VDC request embedding algorithms, data center performance factors such as resource utilization rate and energy consumption are not taken into consideration. This motivated us to design a strategy for improving the resource utilization rate without increasing the energy consumption. We propose novel VDC embedding methods based on row-epitaxial and batched greedy algorithms inspired by bioinformatics. These algorithms embed new requests into the VDC while reembedding previously allocated requests. Reembedding is done to consolidate the available resources in the VDC resource pool. The experimental testbed results show that our algorithms boost the data center objectives of high resource utilization (by improving the request acceptance rate), low energy consumption, and short VDC request scheduling delay, leading to an appreciable return on investment.

Toward SLAs Guaranteed Scalable VDC Provisioning in Cloud Data Centers

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.

Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization

Cloud computing has emerged in recent years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). As the number of cloud service provider increases, there exists a demand to dynamically provision virtual data centers (VDC) on top of the infrastructure provider’s physical data centers. This research addresses problems related to energy and resource efficiently embedding virtual data centers inside physical data centers under dynamic resource allocation conditions, in which VDCs continuously join and leave the system. Dynamic VDC embedding is challenging as it is an NP-hard problem that should meet multiple objectives. In this article, we propose heuristic joint VDC embedding – server consolidation approaches as one solution for that problem. Evaluation results show that the joint approach outperforms existing ones in terms of resource and energy efficiency and can keep system complexity acceptable.

Towards Robust Green Virtual Cloud Data Center Provisioning

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC’s long-term energy consumption in light-load CDCs, and in terms of CSP's long-term revenue in heavy-load CDCs.

Resource management framework for virtual data center embedding based on software defined networking

In cloud computing, service providers can lease resources as needed from infrastructure providers and deploy services. Service providers only need to focus on their own services without considering the equipment cost and maintenance cost. However, traditional infrastructure providers do not guarantee the network performance and bandwidth and only provide resources with virtual machines. With the development of isolation network virtualization technology, especially the concept of software defined network, some researchers suggested that infrastructure providers should provide resources to service providers with the way of virtual data centers so as to solve these problems in the traditional data centers. Although there are many advantages to allocate resources with virtual data centers, it also brings a new challenge that how to meet the diverse needs of service providers and allocate resources at the minimum cost and maximum profit. In order to solve the embedding problem of virtual data centers based on software defined networking (SDN), this paper presents a novel resource management framework for the embedding problem of virtual data centers. It is a heuristic embedding algorithm based on topological potential and modularity and used to improve the acceptance ratio and the infrastructure providers’ revenue. Finally, a dynamic monitoring strategy is proposed to select the virtual data center request with a high revenue cost ratio and further maximize the profit of infrastructure providers. A large number of simulation experiments prove that the proposed algorithm can accept more requests with the minimum cost, and improve the revenues of infrastructure providers.

Virtual Data Center Embedding: A Survey

Data centers have been gaining increasing interest due to their capacity to store a large amount of information; as a result they have become the main support for the deployment of cloud computing. Currently, data centers face performance problems, such as: lack of guaranteed quality of service, security risks, management complexity and inflexibility. Virtualized data centers are an emerging tool to address these issues. However, their implementation brings a major challenge: How to optimally allocate the resource demands of virtual data centers on top of the physical infrastructure, so that the data center operating costs are reduced, improving the revenue for infrastructure providers and fulfilling the agreements of quality of service with the end users? This challenge is known as the Virtual Data Center Embedding problem. This paper presents survey of current research in this problem, and also proposes a taxonomic classification of the main approaches to solve it. Finally, opportunities for future research in resource allocation for virtualized data centers are discussed.