Nature Inspired Optimizations in Cloud Computing: Applications and Challenges

Cloud computing and big data: Technologies and applications

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

In Healthcare big data, data is originated from various heterogeneous sources. Numerous novel base particular healthcare applications offered to handling source of data from electronic health record (EHR) to medical images. Imaging, Electronic Health Report, technology in light of sensor and numerous different procedures created an immense measure of Healthcare data. Cloud computing development was an excellent paradigm to substantiate big data which incited find of imperceptible examples. Cloud computing is a developing new registering design intended to answer different contending administrations on the Web. Fog Computing is a design style in which arrange segments amongst devices and the cloud execute application-particular rationale. We in this paper investigate, characterize, and talk about various application of cloud and fog computing. We talk about the impact of cloud computing and fog computing on healthcare big data. Cloud base framework for Homediagnosis Service, Fog computing architecture and the justification of moving from cloud to Fog presented comprehensively in this paper.

Cloud computing is a combination of computer technology and internet-based development. Cloud computing uses the term "cloud", or cloud, to describe the internet, which is often depicted on computer network diagrams. Cloud computing capacity is an information technology service that can be accessed via the internet without knowing the infrastructure behind it. This opens up the possibility of accessing and using technology more efficiently and flexibly. The development of information technology, especially cloud computing, has had a significant impact in various fields, including education. Cloud computing changes the way information technology is distributed and accessed, providing opportunities for educational institutions to access data and educational resources more efficiently and flexibly. It also helps educational institutions to focus more on core educational processes without having to manage technology infrastructure directly. Teaching about cloud computing is becoming increasingly important. Because through an introduction to the basic concepts, types, benefits and practical applications of cloud computing, students can understand the importance of this technology in the modern world. Thus, teaching and training measures such as those described in this study are expected to help increase the understanding and application of cloud computing among students, preparing them for the challenges and opportunities in the ever-evolving digital era.

This chapter considers a class of optimization techniques that were developed to imitate processes found in nature. Nature is a wonderful source of inspiration for global optimization because so many aspects of natural phenomenon can be mimicked and employed for solving challenging design problems, from the very process of evolution to the coordinated search behavior of various swarming organisms. Nature inspired search algorithms have played an important role in electromagnetic design, as they have proven to be very robust at solving complex problems with many design parameters. Also, as the field of metamaterials has developed, optimization has become an important tool in the quest to overcome performance limitations such as high loss and narrow bandwidth, which have limited the widespread use of metamaterials in practical device applications. In the first part of this chapter, three prominent nature inspired optimization algorithms are described in detail, including the genetic algorithm (GA), particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES). Following this, several examples of metamaterial surfaces are presented that have each been optimized by one of the three nature inspired techniques. Finally, two homogenization techniques that can be employed to invert scattering parameters for a slab of metamaterial to obtain isotropic or anisotropic effective medium parameters are examined and used in conjunction with a GA to overcome previous limitations in terms of loss and angular stability in metamaterials.

Execution of workflow application in cloud computing is widely applied, it is to apply heterogeneous resource in cloud for meeting the demand for large computational requirements. To maximize the efficiency of cloud computing, we have to effectively solve resources arrangement problem of workflow application in cloud computing, namely efficient task scheduling is needed. Cloud task scheduling problem is an NP-hard problem; therefore, a modified particle swarm optimization (PSO) is used to solve the task scheduling problem in the cloud, the grid task scheduling problems including how to distribute tasks to cloud resource and how to place a priority of tasks. In this study, two types of PSO are applied to solve these two issues, discrete particle swarm optimization is used to solve problem of distribution tasks in the cloud and a modified conventional PSO, named global topology controlling PSO (GTCPSO), is used in task priority assignments. In order to increase PSO searching efficiency, curve controlling is used to adjust the usage of global topology communication between particles so as to control search behavior of particle swarm. Thus, the swarm can perform global search at the initial stage, and then does fully local search at the last stage. The experiment results indicate that the curve controlling of topology communication between particles based on a designed global topology rate can efficiently solve the task scheduling problem and enhance performance of cloud computing.

Dynamic resource provisioning is a challenging technique to meet the service level agreement (SLA) requirements of multi-tier applications in virtualization-based cloud computing. Prior efforts have addressed this challenge based on either a cost-oblivious approach or a cost-aware approach. However, both approaches may suffer frequent SLA violations under flash crowd conditions. Because they ignore the benefit gained that a multi-tier application continuously guarantees the SLA in the new configuration. In this paper, we propose a benefit-aware approach with feedback control theory to solve this problem. Experimental results based on live workload traces show that our approach can reduce resource provisioning cost by as much as 30% compared with a costoblivious approach, and can effectively reduce SLA violations compared with a cost-aware approach.

<p>The security of web applications is a significant issue because of their widespread use in everyday activities. Although machine learning has shown success in identifying attacks, it can face difficulties when dealing with wide datasets. Algorithms inspired by nature, renowned for their optimization capabilities, offer potential solutions to this difficulty.</p><p> This paper analyses the use of Nature-Inspired Optimization Algorithms (NIOAs) to identify web application threats and assess their performance in detecting web application attackers.</p><p> This paper involves a comprehensive review of several kinds of patented algorithms that are based on nature- inspired algorithms to measure their performance in recognizing new types of web attacks. Additionally, this paper compares these methods with conventional machine learning approaches to reveal their strengths and weaknesses when used for web security purposes.</p><p> The study emphasizes the effectiveness of Nature-Inspired Optimization Algorithms (NIOAs) in enhancing detection mechanisms for evolving threats. These algorithms demonstrate adaptability and often outperform traditional methods in specific scenarios. The results reveal that optimization approaches like WOA, EO, GWO, and BAT achieved outstanding classification accuracy. WOA-stacking classifier gives the best performer with a classification accuracy of 99.28% and a leader fitness score of 99.17%. EO-Xgboost also stood out with a perfect accuracy of 100%. While traditional classifiers like Logistic Regression and SVM performed well, NIOA-based methods showed superior results.</p><p> Nature-inspired optimisation Algorithms can improve the security of web applications. The findings suggest that nature-inspired algorithms have potential applications across different problem domains, This empirical study provides valuable insights for researchers seeking accurate classifiers for detecting website attacks.</p>

Streaming media is widely adopted by thousands of applications in cloud computing, how to effectively protect streaming media contents is a new challenge. In this paper, we propose an efficient online digital rights management (DRM) scheme supporting dynamic license in cloud computing. The content provider encrypts media content and outsources the encrypted content to cloud storage, while the user acquires dynamic license from the license server and consumes the streaming media content from the cloud storage. Further, we present a secure key distribution protocol based on proxy re-encryption, which protects the confidentiality of content encryption key, and reduces the work of key management in the cloud, and also supports domain key to realize user domain management. In addition, we develop a prototype system with Google cloud storage APIs based on the proposed scheme, and the implementation and comparison results show that the proposed scheme satisfies the requirements of online media content protection in cloud computing.

Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).

Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues.

Cost efficiency is an often mentioned strength of cloud computing. In times of decreasing educational budgets virtual labs provided by cloud computing might be therefore an int eresting option for higher education organizations or IT training facilit ies. An analysed use case of a web technology lecture and a corresponding practical course of a co mputer science study programme shows that is not possible to answer the question in general whether cloud computing approaches are economical or not. The general implication of this finding for h igher education is, that the application of cloud computing can be only answered fro m a course specific point of view. This contribution shows why. But also how universities, colleges or other IT training facilit ies can make pro found and course specific decisions for or against cloud based virtual labs from an economic point of view. The presented approach is inspired by Weinmans mathematical proof of the inevitability of cloud co mputing. The key idea is to co mpare peak to average usage of virtual labs and relate this ratio to costs of classical dedicated labs . The rat io of peak and average usage indicates whether a use case (fro m a pure economical point of view) is cloud compatible or not. This contribution derives also some findings when cloud computing in h igher education has economical advantages or disadvantages. Regarding the analysed use case it turned out that virtual labs are able to provide a more than 25 times cost advantage compared to classical dedicated approaches . Virtual labs can be applied frict ionless to classical as well as distance study programmes and virtual labs provide a convenient infrastructure for project as well as problem based learning in co mputer science. Nevertheless provider of virtual labs should always consider usage and resulting cost characteristics. This article shows how to do this.

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. Cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing.

Advances in cloud computing have aroused many researchers’ interest in privacy-preserving feature extraction over outsourced multimedia data, especially private image data. Since block truncation coding (BTC) is known as a simple and efficient technology for image compression, this paper focuses on privacy-preserving feature extraction in BTC compressed domain. We propose a privacy-preserving computation of BTC feature extraction over massive encrypted images (also called PPBTC). First, all images are uploaded to the cloud after encryption. The privacy-preserving image encryption process consists of block permutation, pixel diffusion, and a bit-plane random shift. BTC features remain unchanged after encryption and the cloud server can directly extract BTC features from the encrypted images. Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTC-compressed images is efficient and secure, and it can be applied to secure image computation applications in cloud computing.

As a convenient, on-demand access service model for network based, configurable shared calculation resource pool, cloud computing has many advantages. At the same time, there are many challenges in the development and application of cloud computing, especially in cloud security. For quantification demand of cloud security services, drawing on cloud computing risk control security assess framework related to the international organizations, based on China's classification protection assessment requirements, the cloud computing security evaluation index system is built using of Delphi method, and the weight of each index entry is calculated using the AHP(Analytic Hierarchy Process). The cloud security assessment model has been validated through examples, and the results show that the proposed model can make quantitative assessment on secure cloud platform effectively, with much more efficiency.