In recent years Cloud computing has become the infrastructure which small and medium-sized businesses are increasingly adopting for their IT and computational needs. It provides a platform for high performance and throughput oriented computing, and massive data storage. Subsequently, novel tools and technologies are needed to handle this new infrastructure. One of the biggest challenges in this evolving field is Cloud storage security, and accordingly we propose new optimized techniques based on encryption process to achieve better storage system security. This paper proposes a symmetric block algorithm (CHiS-256) to encrypt Cloud data in efficient manner. Also, this paper presents a novel partially encrypted metadata-based data storage. The (CHiS-256) cipher is implemented as part of the Cloud data storage service to offer a secure, high-performance and throughput Cloud storage system. The results of our proposed algorithm are promising and show the methods to be advantageous in Cloud massive data storage and access applications.

In recent years Cloud computing has become the infrastructure which small and medium-sized businesses are increasingly adopting for their IT and computational needs. It provides a platform for high performance and throughput oriented computing, and massive data storage. Subsequently, novel tools and technologies are needed to handle this new infrastructure. One of the biggest challenges in this evolving field is Cloud storage security, and accordingly we propose new optimized techniques based on encryption process to achieve better storage system security. This paper proposes a symmetric block algorithm (CHiS-256) to encrypt Cloud data in efficient manner. Also, this paper presents a novel partially encrypted metadata-based data storage. The (CHiS-256) cipher is implemented as part of the Cloud data storage service to offer a secure, high-performance and throughput Cloud storage system. The results of our proposed algorithm are promising and show the methods to be advantageous in Cloud massive data storage and access applications.

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

Many circuit design applications rely on an intermediate sequence to carry a decision to the next circuit stage. The decision may be carried by a weighted pattern of N bits, with the weights being selected in a way that optimizes the circuit implementation or some aspect of performance. For example, when the weights are consecutive powers of 2 beginning with 1 = 2 0 , we have the standard binary representation. As another example, when all the weights are 1, we have the unary representation that encodes a value k by k asserted bits and N-k unasserted bits (a weightk bit vector of length N). In this paper, we present the design of a circuit that screens a unary representation to verify that the represented value falls between preset lower and upper limits l and u, passing through any string that represents a value in the interval [l, u] and outputting the all-0 s bit pattern otherwise. Our mixed analog-digital circuit implementation, based on switched-capacitor arrays, provides a decision output within a clock cycle of 4 ns for 16-bit unary representation, when realized with 0.15 lm TSMC technology. The latter results were obtained with normal, per-bit capacitance of 200 fF and single-clock-cycle operation. As an added benefit, our filtering circuit can form the basis for designing a cost-effective Hamming decoder circuit.

Abstract-MicroRNA is an oligonucleotide that plays a role in the pathogenesis of several diseases (mentioning Cancer). It is a non-coding RNA that is involved in the control of gene expression through the binding and inhibition of mRNA.In this study, three algorithms were implemented in WEKA software using two testing modes to analyze five datasets of miRNA families. The data mining techniques are used to compare the interactions of miRNA-mRNA that it either belongs to the same gene-family or to different families, and to establish a biological scheme that explains how the biological parameters are involved or less involved in miRNA-mRNA prediction.The factors that were involved in the prediction process includs match, mismatch, bulge, loop, and score to represent the binding characteristics, while the position, 3'UTR length, and chromosomal location and chromosomal categorizations represent the characteristics of the target mRNA. These attributes can provide an empirical guidance for study of specific miRNA family to scan the whole human genome for novel targets. This research provides promising results that can be utilized for current and future research in this field.