Search

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.

Virtual network embedding (VNE) refers to allocating reasonable substrate network resources for virtual network (VN) requests that include computing resources and network resources, so as to obtain optimal income from leasing virtual resources. Such a way of providing virtual resources is the key technology of cloud computing and can greatly save the operating cost of enterprises and provide flexibility of application deployment. However, the existing VNE algorithms are mostly oriented to traditional stochastic network topologies. Due to the high connectivity and server density of data centers and the complexity of the user's resource requirements, the traditional VNE algorithms suffer from low resource utilization rate and revenues in the VNE on the data centers. Different from the existing algorithms which are often based on heuristic algorithms, this paper proposes a VNE algorithm for data center topology based on the Q-learning algorithm which is a typical reinforcement learning method. The algorithm an agent for each VN designs a reward function related to the effect of virtual link embedding, which is used to update the Q-matrix through unsupervised learning process. Then, the agent can find the optimal embedding strategy based on the Q-table from each learning. Simulation results demonstrate that the proposed algorithm can improve the resource utilization ratio and obtain a better revenue/cost ratio of the substrate network compared with the traditional heuristic algorithms. [ABSTRACT FROM AUTHOR]

The amount of traffic in the data center grows exponentially due to the emergence of many resource-starvation applications. Thus, effective resource allocation in the data center becomes vitally important. In this letter, the tenants’ resource demands are abstracted as Virtual Data Center (VDC) requests and fulfilled by mapping virtual nodes and links of VDC onto physical servers and fiber links. Software Defined Networking (SDN) is leveraged to build a multi-tenant oriented resource allocation framework, in which the resource allocation problem is transformed to a VDC mapping problem crossing different domains, and a cost and security aware mechanism is proposed to address this problem. The results indicate that the proposed mechanism can effectively increase the resource utilization and cut down the probability of servers being attacked. [ABSTRACT FROM AUTHOR]

Virtual data center (VDC) solutions provide an environment that is able to quickly scale up, and where virtual machines and network resources can be quickly added on-demand through self-service procedures. VDC providers must support multiple simultaneous tenants with isolated networks on the same physical substrate. The provider must make efficient use of its available physical resources while providing high-bandwidth and low-latency connections to tenants with a variety of VDC configurations. This paper utilizes state-of-the-art optical network elements to provide high-bandwidth optical interconnections and develop a VDC architecture to slice the network and the compute resources dynamically, to efficiently divide the physical network between tenants. We present a data center virtualization architecture with a softwaredefined networking controlled all-optical data plane combining optical circuit switching and a time-shared optical network. Developed network orchestration dynamically translates and provisions VDCs requests onto the optical physical layer. The experimental results show the provisioned bandwidth can be varied by adjusting the number of time slots allocated in the time-division multiplexing (TDM) network. These results lead to recommendations for provisioning TDM connections with different performance characteristics. Moreover, application-level optical switch reconfiguration time is also evaluated to fully understand the impact on application performance in VDC provision. The experimental demonstration confirmed that the developed VDC approach introduces negligible delay and complexity on the network side. [ABSTRACT FROM AUTHOR]

Abstract Today, telecommunication networks are the basis for deploying different types of cloud applications and services in data centers. The growing use of the cloud computing concept to provide access to applications and services every year increases the amount of converged network traffic. Existing approaches for traffic routing which using in the software defined-network don't provide enough flexible solutions, able to adapt to changes in traffic flows in real time. At peak load, this leads to an overabundance of traffic to specific physical network nodes which are not prepared to handle a large data flow. One of the approaches that make it possible to solve this problem is the organization of adaptive traffic routing. In this paper are describes the developed approaches for effective control of traffic flow in the virtual data center. Our approaches are using the methods of the data mining and machine learning to more accurately classify and identify traffic flows of cloud applications and network services. This information helps for optimization traffic routes in real time. Another element of the proposed complex solution is solving the task of identifying routes in the virtual network of a data center. Within the framework of the research, the problem of route identification has been decomposed into two consecutive subproblems. The first part is aimed at the classification of communication channels in a virtual data center for a number of features that are characteristic of all superimposed networks. At the second part, based on the received data, the routes are clustered in accordance with the QoS requirements imposed by the current traffic flows. The received data are combined with information about the current flows of the circulating network and are fed to the input of the neural network to decide on the choice of a suitable route. Thus, the solution of the task of adaptive routing allows determining the priorities when creating the bandwidth of traffic flows, as well as establishing rules for managing the network of the data center. [ABSTRACT FROM AUTHOR]

Data centers are widely used for the placement of highly loaded applications and solutions used to process large amounts of data (Big Data). The study developed a model of cloud application and services. The novelty of this model is the use of methods of intellectual analysis and prediction of dynamic characteristics in the study of multicomponent systems. In order to provide flexibility built models used agent-oriented programming. Plurality objects of data center modeled as agents are discussed. Each node in this case is a platform of agents, which controls other agents. Agents that act as data resources for cloud applications and cloud services. A distinctive feature of the proposed model is a set of individual parameters needed to perform tasks. It formalizing reflected in the quality of service compliance requirements as part of resource characteristics. [ABSTRACT FROM AUTHOR]

This paper considers the distributed access and control problem of massive wireless sensor networks' data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate. [ABSTRACT FROM AUTHOR]

The cost of power and its associated delivery are becoming significant factors in the total expenditure of large-scale data centers. Numerous techniques have been proposed to address the energy efficiency issue in cloud systems. Recently, some efforts have been made to decentralize the cloud via distributing data centers in diverse geographical positions, at different scales. In this paper, we elaborate on the energy effectiveness of service provisioning on different cloud architectures, from a mega-data center to a nano data center, which provides the extreme decentralization in terms of cloud architecture, as well as P2P-clouds or community network clouds. We study the energy consumption through an analytical and simulation framework for video streaming and MapReduce applications. [ABSTRACT FROM AUTHOR]

Modern high-performance data centers are responsible for delivering a huge variety of cloud applications to the end-users, which are increasingly pushing the limits of the currently deployed computing and network infrastructure. All-optical dynamic data center network (DCN) architectures are strong candidates to overcome those adversities, especially when they are combined with an intelligent software defined control plane. In this paper, we report the first harmonious integration of an optical flexible hardware framework operated by an agile software and virtualization platform. The LIGHTNESS deeply programmable all-optical circuit and packet switched data plane is able to perform unicast/multicast switch-over on-demand, while the powerful software defined networking (SDN) control plane enables the virtualization of computing and network resources creating a virtual data center and virtual network functions (VNF) on top of the data plane. We experimentally demonstrate realistic intra DCN with deterministic latencies for both unicast and multicast, showcasing monitoring, and database migration scenarios each of which is enabled by an associated network function virtualization element. Results demonstrate a fully functional complete unification of an advanced optical data plane with an SDN control plane, promising more efficient management of the next-generation data center compute and network resources. [ABSTRACT FROM PUBLISHER]

With the massive adoption of cloud-based services, high energy consumption and carbon footprint of cloud infrastructures have become a major concern in the IT industry. Consequently, many governments and IT advisory organizations have urged IT stakeholders (i.e., cloud provider and cloud customers) to embrace green IT and regularly monitor and report their carbon emissions and put in place efficient strategies and techniques to control the environmental impact of their infrastructures and/or applications. Motivated by this growing trend, we investigate, in this paper, how cloud providers can meet Service Level Agreements (SLAs) with green requirements. In such SLAs, a cloud customer requires from cloud providers that carbon emissions generated by the leased resources should not exceed a fixed bound. We hence propose a resource management framework allowing cloud providers to provision resources in the form of Virtual Data Centers (VDCs) (i.e., a set of virtual machines and virtual links with guaranteed bandwidth) across a geo-distributed infrastructure with the aim of reducing operational costs and green SLA violation penalties. Extensive simulations show that the proposed solution maximizes the cloud provider's profit and minimizes the violation of green SLAs. [ABSTRACT FROM PUBLISHER]

Virtual data centers allow the hosting of virtualized infrastructures (networks, storage, machines) that belong to several customers on the same physical infrastructure. Virtualization theoretically provides the capability for sharing the infrastructure among different customers. In reality, however, this is rarely (if ever) done because of security concerns. A major challenge in allaying such concerns is the enforcement of appropriate customer isolation as specified by high-level security policies. At the core of this challenge is the correct configuration of all shared resources on multiple machines to achieve this overall security objective. To address this challenge, this paper presents a security architecture for virtual data centers based on virtualization and Trusted Computing technologies. Our architecture aims at automating the instantiation of a virtual infrastructure while automatically deploying the corresponding security mechanisms. This deployment is driven by a global isolation policy, and thus guarantees overall customer isolation across all resources. We have implemented a prototype of the architecture based on the Xen hypervisor. [ABSTRACT FROM AUTHOR]

Constraint-based techniques can solve challenging problems arising in highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To address this problem, we introduce Netsolver , a system for VDC allocation that is based on constraint solving. 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. Finally, we show how Netsolver can be extended with additional optimization constraints, such as VM affinity and hotspot minimization, demonstrating the flexibility of our approach. The performance and flexibility of Netsolver are made possible by our formalization of the VDC allocation problem in terms of multi-commodity flows, and the corresponding efficient handling of network flow problems in the underlying constraint solvers. This shows the importance of supporting flow-based constraints, which are more mature in ILP- vs. SMT-based constraint solving. [ABSTRACT FROM AUTHOR]