Fragmentation and Replication Using Drops Methodology

Cloud computing is one of the most prominent service for  remote accessing of data that practice of using a network of remote servers hosted on the internet to store, manage and process data, rather than a local server or a personal computer. For state individuality of cloud computing make it a prominent candidate for businesses, organizations, and individual users for acceptance. We cooperatively loom the issue of security and presentation as a secure data replication trouble. We present Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that sensibly fragments user files into portions and replicates them at tactical locations within the cloud. The consequences of the reproduction exposed that the immediate meeting point on the safety and presentation, resulted in augmented security level of data escort by a minor presentation fall

Impact Factor: 2.265 Global

ABSTRACT The third-party administrative control is done in cloud computing which gives rise to security concerns the attacks may happens by data of other users and nodes within the cloud hence, high security measures are required to protect data within the cloud. In this paper we propose (DROPS) Division and Replication of Data in the Cloud for Optimal Performance and Security that will collectivel1y approaches the security and performance issues. Here we divide a file into fragments and replicate the fragmented data over the cloud nodes. The nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack and so no meaningful information is revealed to the attacker. Furthermore, the traditional cryptographic techniques for the data security is not used by DROP which reduces cost. Then we also compare the performance of the DROPS methodology with ten other schemes for providing higher level of security

Stochastic Modeling and Performance Analysis of Energy-Aware Cloud Data Center Based on Dynamic Scalable Stochastic Petri Net

The characteristics of cloud computing, such as large-scale, dynamics, heterogeneity and diversity, present a range of challenges for the study on modeling and performance evaluation on cloud data centers. Performance evaluation not only finds out an appropriate trade-off between cost-benefit and quality of service (QoS) based on service level agreement (SLA), but also investigates the influence of virtualization technology. In this paper, we propose an Energy-Aware Optimization (EAO) algorithm with considering energy consumption, resource diversity and virtual machine migration. In addition, we construct a stochastic model for Energy-Aware Migration-Enabled Cloud (EAMEC) data centers by introducing Dynamic Scalable Stochastic Petri Net (DSSPN). Several performance parameters are defined to evaluate task backlogs, throughput, reject rate, utilization, and energy consumption under different runtime and machines. Finally, we use a tool called SPNP to simulate analytical solutions of these parameters. The analysis results show that DSSPN is applicable to model and evaluate complex cloud systems, and can help to optimize the performance of EAMEC data centers

Chip-based Brillouin processing for carrier recovery in coherent optical communications

Modern fiber-optic coherent communications employ advanced spectrally-efficient modulation formats that require sophisticated narrow linewidth local oscillators (LOs) and complex digital signal processing (DSP). Here, we establish a novel approach to carrier recovery harnessing large-gain stimulated Brillouin scattering (SBS) on a photonic chip for up to 116.82 Gbit/sec self-coherent optical signals, eliminating the need for a separate LO. In contrast to SBS processing on-fiber, our solution provides phase and polarization stability while the narrow SBS linewidth allows for a record-breaking small guardband of ~265 MHz, resulting in higher spectral-efficiency than benchmark self-coherent schemes. This approach reveals comparable performance to state-of-the-art coherent optical receivers without requiring advanced DSP. Our demonstration develops a low-noise and frequency-preserving filter that synchronously regenerates a low-power narrowband optical tone that could relax the requirements on very-high-order modulation signaling and be useful in long-baseline interferometry for precision optical timing or reconstructing a reference tone for quantum-state measurements.Comment: Part of this work has been presented as a postdealine paper at CLEO Pacific-Rim'2017 and OSA Optic

Multicast routing from a set of data centers in elastic optical networks

This paper introduces the Multi-Server Multicast (MSM) approach for Content Delivery Networks (CDNs) delivering services offered by a set of Data Centers (DCs). All DCs offer the same services. The network is an Elastic Optical Network (EON) and for a good performance, routing is performed directly at the optical layer. Optical switches have heterogeneous capacities, that is, light splitting is not available in all switches. Moreover, frequency slot conversion is not possible in any of them. We account for the degradation that optical signals suffer both in the splitting nodes, as well as across fiber links to compute their transmission reach. The optimal solution of the MSM is a set of light-hierarchies. This multicast route contains a light trail from one of the DCs to each of the destinations with respect to the optical constraints while optimizing an objective (e.g., minimizing a function). Finding such a structure is often an NP-hard problem. The light-hierarchies initiated from different DCs permit delivering the multicast session to all end-users with a better utilization of the optical resources, while also reducing multicast session latencies, as contents can be delivered from such DCs closer to end-users. We propose an Integer Linear Programming (ILP) formulation to optimally decide on which light-hierarchies should be setup. Simulation results illustrate the benefits of MSM in two reference backbone networks.Peer ReviewedPostprint (author's final draft

On energy consumption of switch-centric data center networks

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to esnure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power related metrics were derived and adapted for the information technology equipment (ITE) processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness (PUE) and data center infrastructure efficiency (DCIE), known to DCs. This study suggests that despite in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods