Dynamic Data Security Assurance In Cloud Computing

We create cloud-helped remote detecting systems for empowering dispersed agreement estimation of obscure parameters in a given geographic range. We first propose an appropriated sensor system virtualization calculation that looks for, chooses, and directions Internet-available sensors to perform a detecting undertaking in a particular locale. The emergence of yet more cloud offerings from a multitude of service providers calls for a meta cloud to smoothen the edges of the jagged cloud landscape. This meta cloud could solve the vendor lock-in problems that current public and hybrid cloud users face.    The cloud computing paradigm has achieved widespread adoption in recent years. Its success is due largely to customers’ ability to use services on demand with a pay-as-you go pricing model, which has proved convenient in many respects. Low costs and high flexibility make migrating to the cloud compelling. Despite its obvious advantages, however, many  companies hesitate to  “move  to  the  cloud,” mainly because of concerns related to service availability,  data  lock-in,  and  legal  uncertainties. Lock in is particularly problematic Our reproduction results demonstrate that the proposed calculation, when contrasted with traditional ADMM (Alternating Direction Method of Multipliers), diminishes correspondence overhead essentially without trading off the estimation mistake. Furthermore, the joining time, however builds somewhat, is still straight as on account of ordinary ADMM

A QoS-based Resource Selection Approach for Virtual Networks

The Internet has gained an outstanding success in a short amount of time and it became a critical infrastructure for accessing information and global commerce. With the help of the Internet and its new channels for connecting people a new way of communication has been established. Meanwhile, its great success leads to new limitations. The Internet consists of various network infrastructure providers with different objectives which makes emerging of new technologies or major architectural changes that require cooperative agreements, relatively impractical. While the current Internet architecture is not suitable for supporting many types of applications, network virtualization is considered as promising, yet challenging solution of these limitations. Network virtualization separates the role of traditional internet service providers (ISPs) into physical infrastructure providers (PIPs) responsible for deploying the physical infrastructure and service providers (SPs) offering end-to-end services to end users. Another motivation for network virtualization is the possibility to add value in the virtualization layer aiming to make use of new technologies (e.g. QoS schemes) and customizing existing technologies to adapt specific services (i.e. customizable networks). This provides the means to run multiple virtual networks on a shared substrate network simultaneously while each virtual network is customized for a specific use. The key challenge in virtual networks is the problem of assigning virtual nodes and links to physical resources. Virtual network mapping/embedding consists in finding the most suitable physical nodes and links in the physical network in order to map virtual network requests with certain constraints on virtual nodes and links. The goal of this thesis is to design and implement substrate network resource selection scheme to increase the overall efficiency of the virtual network embedding process and satisfy the set of predefined resource constraints. This work assumes the existence of a virtual infrastructure provider requesting virtual networks from physical infrastructure providers and proposes a selection algorithm based on service-oriented architecture. Our proposed virtual network embedding algorithm is a heuristic algorithm that considers static attributes along with dynamic attributes of nodes and links as well as end-to-end QoS constraints

Empowering Elegant Cloud Services Owing To Remote Signification

We create cloud-helped remote detecting systems for empowering dispersed agreement estimation of obscure parameters in a given geographic range. We first propose an appropriated sensor system virtualization calculation that looks for, chooses, and directions Internet-available sensors to perform a detecting undertaking in a particular locale. The calculation focalizes in linearithmic time for expansive scale organizes, and obliges trading various messages that is at most direct in the quantity of sensors. Second, we outline an awkward, appropriated calculation that depends on the chose sensors to gauge an arrangement of parameters without obliging synchronization among the sensors. Our reproduction results demonstrate that the proposed calculation, when contrasted with traditional ADMM (Alternating Direction Method of Multipliers), diminishes correspondence overhead essentially   Without trading off the estimation mistake. Furthermore, the joining time, however builds somewhat, is Still straight as on account of ordinary ADMM

A novel performance metric for Virtual Network Embedding combining aspects of Blocking Probability and Embedding Cost

Network Virtualization offers a solution for Future Internet and it is a key enabler for cloud computing applications. Virtual Network Embedding (VNE) problem deals with resource allocation of a physical infrastructure to Virtual Network Requests (VNRs). Several performance metrics are employed in order to evaluate the efficiency of specific VNE approaches. These existing metrics, mostly related to Infrastructure Provider profit, are computed at the end of the VNE process, after embedding many VNRs. This work proposes a novel performance metric, VNE-NP (VNE Normalized Profit) which combines aspects of the two metrics most used in the literature: Blocking Probability and Embedding Cost.XII Workshop de Arquitecturas, Redes y Sistemas Opearativos (WARSO).Red de Universidades con Carreras en Informática (RedUNCI

Dynamic node allocation in Network Virtualization

International audienceNetwork virtualization is a new technology that provides a transparent abstraction of the networking resources. The most important challenge in network virtualization is the allocation of the physical substrate network among the pool of the active virtual networks (VNs). Our work in progress aims at allocating the node resources in a virtualized networking infrastructure. We believe that this allocation should be dynamic to lead to higher performance and better utilization of the physical resources. In this paper, we propose two models for dynamic node allocation for multiple VNs. The first model uses game theory and market-based approach in order to better allocate the physical node. The second one proposes a dynamic weighted round robin (WRR) approach where each VN receives a fraction of the physical node according to an estimation of its current number of waiting packets and its weight. Both models use a distributed approach to minimize the packet delays inside the physical router and to fairly allocate the nodes between different VN

Towards self-resource discovery and selection models in grid computing

Global computational grids nowadays are suffered from ossification problems due to the following fundamental challenges related to different existing solutions in grid computing: scalability, adaptability, security, reliability, availability and manageability.The management difficulty is due to heterogeneity, dynamicity and locality of the resources within global grid networks.Large-scale grids make the fundamental problem of resource discovery a great challenge.This paper presents a self-resource discovery mechanism (SRDM) that achieves efficient grid resource discovery and takes advantage of the strengths of both hierarchy and decentralized approaches that were previously developed for grid based P2P resource discovery.P2P systems offer potential strengths such as self-organization, self-healing, and robustness to failure or attacks. Unfortunately, the majority of existing Distributed Hash Table (DHT) based P2P overlays are lacking of attributes range queries that are familiar in resource discovery lookups.The proposed model builds an effective distributed hierarchy that providing scalable, decentralized resource discovery and allocation as well as load balancing for distributed computing using large scale pools of heterogeneous computers. Fundamentally, SRDM employs the spatial index and partitions the overlay space to build a distributed quad tree; each computational resource in the network can calculate its Nodepower.Next, it encodes the information about each node’s available computational resources power in the structure of the links connecting the nodes in the network.This distributed encoding is self-organized, with each node managing its in-degree and local connectivity via its available Nodepower.Assignment of incoming jobs to nodes with the freest resources is also accomplished by sampling it

Towards self-resource discovery and selection models in grid computing

Global computational grids nowadays are suffered from ossification problems due to the following fundamental challenges related to different existing solutions in grid computing: scalability, adaptability, security, reliability, availability and manageability.The management difficulty is due to heterogeneity, dynamicity and locality of the resources within global grid networks.Large-scale grids make the fundamental problem of resource discovery a great challenge.This paper presents a self-resource discovery mechanism (SRDM) that achieves efficient grid resource discovery and takes advantage of the strengths of both hierarchy and decentralized approaches that were previously developed for grid based P2P resource discovery.P2P systems offer potential strengths such as self-organization, self-healing, and robustness to failure or attacks. Unfortunately, the majority of existing Distributed Hash Table (DHT) based P2P overlays are lacking of attributes range queries that are familiar in resource discovery lookups.The proposed model builds an effective distributed hierarchy that providing scalable, decentralized resource discovery and allocation as well as load balancing for distributed computing using large scale pools of heterogeneous computers. Fundamentally, SRDM employs the spatial index and partitions the overlay space to build a distributed quad tree; each computational resource in the network can calculate its Nodepower.Next, it encodes the information about each node’s available computational resources power in the structure of the links connecting the nodes in the network.This distributed encoding is self-organized, with each node managing its in-degree and local connectivity via its available Nodepower.Assignment of incoming jobs to nodes with the freest resources is also accomplished by sampling it

Content-aware resource allocation model for IPTV delivery networks

Nowadays, with the evolution of digital video broadcasting, as well as, the advent of high speed broadband networks, a new era of TV services has emerged known as IPTV. IPTV is a system that employs the high speed broadband networks to deliver TV services to the subscribers. From the service provider viewpoint, the challenge in IPTV systems is how to build delivery networks that exploits the resources efficiently and reduces the service cost, as well. However, designing such delivery networks affected by many factors including choosing the suitable network architecture, load balancing, resources waste, and cost reduction. Furthermore, IPTV contents characteristics, particularly; size, popularity, and interactivity play an important role in balancing the load and avoiding the resources waste for delivery networks. In this paper, we investigate the problem of resource allocation for IPTV delivery networks over the recent architecture, peer-service area architecture. The Genetic Algorithm as an optimization tool has been used to find the optimal provisioning parameters including storage, bandwidth, and CPU consumption. The experiments have been conducted on two data sets with different popularity distributions. The experiments have been conducted on two popularity distributions. The experimental results showed the impact of content status on the resource allocation process

Automated Controllers for Bandwidth Allocation in Network Virtualization

International audienceThe concept of network virtualization was introduced to facilitate flexible service deployment for the future Internet. This recent technology provides a powerful tool to run multiple logical networks on the same physical substrate defined as virtual networks (VNs). Each physical link is split into virtual links and each VN receives a fraction of the available capacity. Bandwidth allocation for multiple VMs aims at sharing the physical links among multiple VNs. It is a critical challenge for both service providers (SPs) and infrastructure providers (InPs). This allocation should take into account the quality of service (QoS) requirements of the flows that are crossing each VN. In this paper, we consider a virtualized network environment where the SP deploys multiple VNs with different links' capacity demands and QoS requirements. Each VN competes with other VNs to receive fractions of physical links managed by multiple InPs. We present a two-layer controller system that adapts to the dynamic change of the workload of each VN. The system uses a prediction-based approach in order to find the optimal request for each VN. The request depends on the estimation of the relationship between the VN performance in terms of packet delays and the actual and past allocations. Then, due to the capacity constraint of the physical link, the system adjusts the offered bandwidth for each of them. Our model offers flexible distributed autonomous control of the bandwidth allocation to maintain the offered QoS to each VN at the desired level in response to the dynamics of the workload. Our mechanism provides an optimum allocation of the physical links by distributing the bandwidth periodically. It also offers the possibility of adjusting the VNs' parameters to take into account the current network behaviour to avoid bottleneck virtual links