Power-Efficient Provisioning for Online Virtual Network Requests in Cloud-Based Data Centers

In the paradigm of cloud computing, multiple users can share the cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and submitted to the cloud-based data centers. How to map a virtual network onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Current researches about virtual network mapping problem mainly focus on resource efficiency, which can be classified into resource efficient VN mapping or cost efficient VN mapping. However, the amount of power or energy consumed by data centers takes a large part of all of the power consumption. This may not only leads to a higher expenditure on the operation of data centers but also contribute to carbon emissions and the greenhouse effect. In this work we study the problem of power efficient virtual network mapping and propose a framework and algorithm for solving this problem. The simulation results show that our approach performs well.

Aiming at the virtual network embedding problem under dynamic service request, Service Request Aware-based Dynamic Virtual Network Embedding (SRAD) algorithm is proposed. We first present virtual network embedding model, and then define virtual network construction cost according to the dynamic characteristic of service request. Our goal is to find the optimal reconfiguration policies by awaring service request type and changing trends, which can minimize the overall construction cost using substrate resource. Experimental results show that the proposed algorithm satisfies the service request, achieves higher success ratio and gains higher revenue/cost ratio for substrate network comparing with the existing algorithms.

Network softwarization technologies, such as software defined networking (SDN), network virtualization (NV), and network function virtualization (NFV), emerge and have been greatly promoted by their supporters. This softwarization scheme contributes to isolating network control from data panel, and allocating virtualized physical network resources flexibly. However, physical switches and links, supporting the softwarization scheme, may be attacked by malicious attackers. Even if these switches and links are softwarized and virtualized successfully, the problem of mapping various virtual network requests, known as virtual network embedding, is difficult to solve directly. Each customized VN request must be allocated and implemented in the appropriate manner. To deal with these issues, we research the virtual network mapping for secure SDN-enabled networks. Firstly, a hierarchical virtualization architecture for SDN- enabled networks is presented. Then, the security model for different malicious attacks is presented. Thirdly, we propose the novel and secure virtual network mapping framework, labeled as \textit{NoViSec}, for supporting SDN-enabled networks. Preliminary experiment results are plotted and discussed so as to validate the effectiveness of our secure \textit{NoViSec} framework.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

Network virtualization is a core technology in next-generation networks to overcome the ossification problem that is observed in the current Internet. The key idea of network virtualization is to split physical network resources into multiple logical networks, each supporting different network services and functionalities. One of the key challenges for virtual network infrastructure providers is to efficiently allocate network resources based on virtual network requests, which is referred to as the virtual network mapping problem. While several algorithms have been proposed previously to solve this mapping problem, their effectiveness is limited since virtual requests specify the internal topology of the virtual network. In this paper, we argue that such internal topologies lead to unnecessary constraints and less efficient solutions. Instead, we propose an alternate formulation of the problem that represents virtual network requests as traffic matrices. We provide a solutions to solving this traffic-matrix-based mapping problem using a mixed integer programming formulation. Our simulation results show that our approach can map significantly more virtual network requests on a physical network infrastructure than previous mapping algorithms and thus improves the efficient use of networking resources in virtual networks.

Attack-aware Security Function Management

A dynamic and resource sharing virtual network mapping algorithm

VHub: Single-stage virtual network mapping through hub location

The evolution of virtualization techniques is still changing operating principles in today's datacenters (DC). The virtualization of ordinary servers was just the first step, which increased the dynamic and flexibility of the DC. Providers are now able to offer different virtual machines (VM) faster and with less overhead to their customers. But this provision raises new problems for the providers. Aspects like isolation, security or multi-tenancy are increasingly relevant and demand new setups in the DC. Current network infrastructures are not able to handle these aspects with an acceptable effort, but the development of virtual networks offers new possibilities, with benefits for the provider and the user. Based on a physical underlay network, different virtual networks can be defined, either by a provider or the customer. Protocols like VXLAN or GENEVE appear to eliminate restrictions of current networks. New paradigms like Software-defined-Networks (SDN) or Network Function Virtualization (NFV) offer new capabilities to redesign the whole network infrastructure in the DC. But the need for digital investigation is still necessary regardless of all new paradigms and evolution. As a branch of digital investigation, network forensic investigation (NFI) is used to examine network traffic by capturing the data of a suspicious target system and analyzing this data. The modern virtual data centers and the implemented virtual networks impede the NFI, proved techniques and methods fail because of the increased complexity of the new logical networks. Not only the analysis of the new network protocols impede the NFI, even the the capture process of relevant data needs to be refined. In this paper, we analyze in detail new arising problems of digital investigation in virtual networks and explore the new challenges for NFI. Based on the discussion of network forensics and current utilized methodologies and the new techniques of network virtualization the arising problems are defined and classified in three categories. This classification helps to develop new methods and possible solutions, which might simplify further necessary investigations in cloud-computing environments.

5G networks represent a paradigm shift from their 4G predecessors, engineered to cater to the burgeoning demands of densely populated areas and the high data throughput requirements of the Internet of Things (IoT) and smart city infrastructures. Beyond enhancing fundamental network attributes such as reliability, availability, and security, 5G introduces architectural innovations to accommodate these advanced needs. This paper delves into the architectural nuances of 5G, focusing primarily on the Standalone (SA) deployment method, distinguished by its utilization of network slicing. This approach leverages cutting-edge virtual network technologies, including Software-Defined Networking (SDN) and Network Function Virtualization (NFV), to forge virtual network slices tailored to diverse consumer needs. Such customization is imperative for meeting the expansive and varied connectivity demands characteristic of 5G networks.To navigate the complexities of 5G architecture, this study advocates for the adoption of Reference Architectures (RAs), which provide a high-level abstraction framework for dissecting and understanding intricate systems. By proposing a model that outlines the standalone architecture of 5G networks, our research lays the groundwork for evolving this model into a comprehensive RA for 5G. This foundational work is instrumental in equipping system designers with the tools necessary for generalizing systems to manage design and deployment challenges effectively, as well as the heterogeneity inherent in system components. Additionally, our proposed model facilitates the establishment of a common vocabulary, promoting a coherent understanding of component interactions and offering a unified system perspective. Ultimately, this paper contributes a pivotal model that not only enhances our comprehension of 5G network architecture but also paves the way for future advancements in network design and deployment, promising to significantly impact the development of smart cities and IoT ecosystems.Keywords: 5G, 5G architecture, Reference architecture, patterns. Network slicing, Network Function Virtualization, Virtual Network Function.

The design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software-defined networking (SDN) and network functions virtualization (NFV) are emerging as the most indispensable key catalysers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor-independent hardware substrate. One of the most important considerations in NFV deployment is how to allocate the virtual resources that are needed to provide flexible virtual network services in an NFV-based network infrastructure. Thus, the most important prerequisite for NFV deployment is achieved fast, scalable and dynamic composition and allocation of networks functions (NFs) to implement network services (NSs). We have proposed a revised RA algorithm that integrates embedding and scheduling of virtual network functions (VNFs) simultaneously. In this paper, performance evaluation of the revised RA algorithm is performed and the proposed algorithm is applied more effectively in NFV environment where the demand for resources is flexible and concentrated.

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

Green technology is the emerging new sustainable technology which paves the way for lowering the raise in the earth’s temperature. The increase in toxic waste from the electronic devices in every level of usage increases the global warming and thus the need of green technology is inevitable. The contributions towards green computing in global community will save the environment by minimizing the energy consumption. Emerging technologies in Internet of things, pervasive applications, cloud computing provides effective consumer, scientific and business domains. Due to the massive data storage and computation of cloud computing, there is an exponential increment in the demand of cloud computing. The growth of enormous amount of data brings a huge challenge to the cloud computing environment. Data centers are the main module of the cloud computing environment, but the major issue is the management of power computing in those data centers. Green data centers are established with limited power consumption and carbon-di-oxide emission. The research work mainly focusses on the delay time of the data centers in Internet cloud which in turn increases the usage of power consumption in data centers. The analysis is simulated with Internet cloud and data centers.

A virtualized network infrastructure is composed of multiple virtualized network functions (VNF) interconnected by well defined interfaces, and thus forming a VNF-graph. The initial deployment of such a VNF-graph inside a data center (DC) is a complex task with multidimensional aspects compared to deploying a single VNF that may represent a single network function. The problem space becomes more complex when each VNF is further decomposed into multiple VNF components (VNFC), where each VNFC embodies a subset of network functions attributed to the respective VNF. The challenge is to ensure that the deployment strategy meets the intra-functional constraints between the multiple VNFCs constituting the VNF-graph while ensuring service, performance and operational integrity, and also ensures optimal utilization of the underlying resources of the DC infrastructure (DCI). In this article we analyze the cost incurred by two “constraint-based heuristically applied” initial VNF/VNFC deployment strategies with reference to a virtualized mobile network infrastructure providing EPCaaS (evolved packet core as a service) while taking into consideration functional and administrative constraints. The cost of deployment is measured in terms of the utilization of DC infrastructure resources such as compute and networking. We also present the discussion in view of the ETSI NFV MANO framework, undergoing standardization, that is responsible for management and orchestration of NFV systems including VNF deployment.

Cloud computing (CC) could be a massive distributed computing driven by business, during which the services and resources are area unit delivered on request to external consumer via the Web. The distributed computing environment comprises of physical servers, virtual machines, data centers, and load balancers which are appended in an efficient way. With the increasing size of a number of physical servers and utilization of cloud services in data centers (DC), the power consumption is a critical and challenging research problem. Minimizing the operational cost and power in a DC becomes essential for cloud service provider (CSP). To resolve this problem, we introduced a novel approach that leads to nominal operational cost and power consumption in DCs. We propose a multi-objective modified differential evolution algorithm for first placement of virtual machine (VM) in the physical hosts and optimize the power consumption during resource allocation using live migration. The experimental results reveal that our proposed method is significantly better against state-of-the-art techniques in terms of limited power consumption and SLA for any given workload.