A Review on Cloud Computing Model

n recent year cloud computing has been an emerging computing model in the IT industry such as google, Amason, Microsoft. Cloud computing is emerging as a model of "everthing as a service" (XaaS). This paper present a study on service model and deployment model of cloud computing. The paper also attempts to layout the prons and cons of cloud computin

A Dynamic, Data-Driven, Decision Support System for Emergency Medical Services

Abstract. In crisis, decisions must be made in human perceptual timeframes under pressure to respond to dynamic uncertain conditions. To be effective management must have access to real time environmental data in a form that can be immediately understood and acted upon. The emerging computing model of Dynamic Data-Driven Application Systems (DDDAS) fits well in crisis situations where rapid decision-making is essential. We explore the value of a DDDAS (iRevive) in support of emergency medical treatment decisions in response to a crisis. This complex multi-layered dynamic environment both feeds and responds to an ever-changing stream of real-time data that enables coordinated decision-making by heterogeneous personnel across a wide geography at the same time.

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Enhancing Two Standard Privacy Preserving to Improve the Security Policy in Cloud Environment

Cloud computing is emerging computing model where the data owners are outsourcing their data into the cloud storage. By outsourcing the data files into the cloud, it gives many benefits to the large enterprises as well as individual users for accessing database security. In data base Storage management systems to cloud it still faces a number of fundamental and critical challenges, among which storage space and security is the top concern. We consider the problem of processing range query search over a confidential numeric attribute K in an outsourced setting of cloud computing. To ensure the correctness of user and user's data in the cloud, we propose a multilevel round random key policy third party authentication system in crypto policy standard. In addition to simplified data storage and secure data acquisition. Also, we consider processing range query search over a confidential numeric attribute with two main challenges. First, the confidentiality requires hiding the value and the relative order of the attribute in records from the cloud server. Finally, we will perform security and performance analysis which shows that the proposed scheme is highly efficient for maintaining secure data storage and acquisition

Multi-Agent-Based Cloud Architecture of Smart Grid

AbstractPower system is a huge hierarchical controlled network. Large volumes of data are within the system and the requirement of real-time analysis and processing is high. With the smart grid construction, these requirements will be further improved. The emergence of cloud computing provides an effective way to solve these problems low-costly, high efficiently and reliably. This paper analyzes the feasibility of cloud computing for the construction of smart grid, extends cloud computing to cloud-client computing. Through “Energy Hub”, Microgrid is separated into a network of three storeys that match with the conception of cloud-client computing. This paper introduces multi-agent technology to control each node in the system. On these bases, cloud architecture of smart grid is proposed. Finally, an example is given to explain the application of cloud computing in power grid CPS structure