Search

The demand for Blockchain innovation and the significance of its application has inspired ever-progressing exploration in various scientific and practical areas. Even though it is still in the initial testing stage, the blockchain is being viewed as a progressive solution to address present-day technology concerns, such as decentralization, identity, trust, character, ownership of data, and information-driven choices. Simultaneously, the world is facing an increase in the diversity and quantity of digital information produced by machines and users. While effectively looking for the ideal approach to storing and processing cloud data, the blockchain innovation provides significant inputs. This article reviews the application of blockchain technology for securing cloud storage.

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.

Secure pursuit strategies over encoded cloud information enable an approved client to inquiry information documents of enthusiasm by submitting scrambled question catchphrases to the cloud server in a protection safeguarding way. In any case, practically speaking, the returned question might be mistaken or deficient in the exploitative cloud condition. For instance, the cloud server may deliberately preclude some qualified outcomes to spare computational assets and correspondence overhead. In this manner, a well-working secure inquiry framework need to provide a question comes about check system that permits the knowledge client to verify comes about. In this paper we outline a protected, efficiently included, and fine-grained question comes about corroboration instrument, by which, given an prearranged inquiry comes about set, the query client not wholly can check the rightness of every in order record in the set yet in addition can as well check what number of or which qualified information documents are not returned if the set is inadequate before unscrambling. The check plot is free coupling to concrete secure inquiry procedures and may be effectively coordinated into any safe question conspire. We accomplish the target by building secure check question for scrambled cloud information. Moreover, a short mark strategy with greatly little stockpiling cost is proposed to ensure the credibility of confirmation protest and a check question ask for method is introduced to enable the inquiry client to safely get the coveted check protest. Execution assessment demonstrates that the proposed plans are down to earth and proficient.

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.

With the significant rise in the demand for infinite storage resource and high-quality retrieval service, cloud computing is deemed to be a highly remarkable technology. For privacy concerns, several research works on ranked multi-keyword search over the encrypted cloud data under multiple data owner model have been developed. However, most of these schemes are vulnerable to keyword guessing attacks and equivalence test attack. Besides, they cannot accurately return the top - $k$ ranked search results from the encrypted data of different data owners to the data users. Obviously, these drawbacks may incur leakage of privacy-sensitive keyword information and the inaccuracy of returned search results. Therefore, in this article, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, which empowers the cloud server to perform multi-keyword search over the cloud data and then, return the top - $k$ ranked search results to data users without leaking any keyword and trapdoor information. Additionally, we show through rigorous security analysis that our scheme is secure against the attacks launched by inside attackers and outside attackers. Finally, the performance evaluation indicates that our scheme has more satisfactory features than the existing ranked multi-keyword search schemes.

Advancement in the Information Communication and Technology (ICT) has resulted in new ways of teaching and learning, and has significantly improved expansion of education to remote areas. Innovation in the technology has enabled classrooms to become virtual meeting rooms, with data, methods and knowledge residing the cloud. The education sector faces the challenge to respond to the national and global changes by upgrading the knowledge base of the teachers as well as the gadgets needed to adapt to new ways. In many parts of the globe, education sector also faces the problem of improving education standards and controlling diminishing dropout rate. With the passage of time education cloud data has grown exponentially with advanced data analytics. This paper investigates the role of Big Data Analytics in solving problems of education sector in Saudi Arabia. In particular, this article provides the results of study into the challenge of governance and execution of Big Data in education sector of Saudi Arabia. The study in this article uses quantitative method by means of an extensive survey of the recipients and other concerned people with education sector in the Al Qasim region of Saudi Arabia. Given the national education system, this study can be generalized to all regions of Saudi Arabia.

The ease and affordability offered by the cloud computing has attracted large number of customers towards it. Cloud service providers offer its services, to the cloud customers, usually in form of Virtual Machines (VMs). With the growth in the number of customers, cloud data centers encounter overwhelming number of VM requests. These requests need to be mapped on the real cloud hardware and therefore, VM placement has been an important research area in the cloud research community. Virtual machine placement, being an NP hard problem, is modelled as an optimization problem with the objective to optimize resource wastage. Dragonfly Algorithm (DA), a nature inspired technique, originates from static and dynamic swarming behavior of dragonfly and is well suited to solve VM placement problem. Therefore, in the proposed work, a modified dragonfly algorithm is applied for VM placement for better resource utilization at cloud data centers. The performance of the proposed model is analyzed through simulation and comparative study. Observations, obtained from the experiments, exhibit the superiority of the proposed model in solving VM placement problem.

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

Cloud computing provides utility computing in which clients pay the cost according to their demands and service use. There are some challenges to this technology. One of these issues in data centers is virtual machine (VM) placement so that mapping of these VMs to hosts is executed for a variety of objectives such as load balancing, reducing energy consumption, increasing resource utilization, shortening response time, etc. In this paper, a strategy is presented based on machine learning for VM replacement which aims to balance the load in host machines (HM). In this proposed strategy, the learning agent, in each learning episode by selecting an action from among the permissible actions and executing it on the environment receives a reward according to the desirability of the solution obtained by doing that action in the environment. Receiving a reward from the environment and updating the action value table enable the learner agent to learn in the following episodes that in each environment state, selecting and executing which action is better in the environment and this leads to further enhancement. Our proposed algorithm has, on average, improved the inter-HM load balance in terms of processor, memory, and bandwidth by 25%, 34%, and 32%, respectively, prior to the implementation of the algorithm. Our strategy was compared from diffrent aspects in three scenarios to the MOVMrB strategy. Finally, it was concluded that our proposed algorithm can be more effective in load balancing by having much less runtime and turning off more HMs.

The cloud services are very useful and demanded in an information technology industry these days. Cloud services are universally delivered with the help of the internet. Cloud services provide more...

Cloud has become a dominant computing platform, and cloud data centers have been widely deployed all over the world. Naturally, cloud data centers become the targets of cyber attacks due to the feature of publicity. In addition, the price of renting resources from cloud constantly gets cheaper and cheaper. Therefore, attackers can rent hosts from cloud data centers to initiate attacks with rather low cost. As a result, hosts in a cloud center could be either victims or attackers. However, most existing researches only treat the hosts as the targets or the sources of attacks, either protecting the hosts from being attacked or identifying the malicious hosts, which is insufficient to protect the cloud data centers comprehensively. In this paper, we hire the novel techniques of SDN to protect the cloud data centers in both directions. Aiming at mitigating DDoS attacks, we propose HostWatcher, a system that watches and protects every host in cloud data center. HostWatcher leverages the advantages of SDN techniques and distributed processing. Caching and round-robin-resending scheme is introduced to the proposed system. Our goal is to protect the hosts comprehensively with QoS guarantee. The extensive experiments show that HostWatcher can effectively mitigate the DDoS attacks that target the hosts. Meanwhile, HostWatcher can also significantly limit the packet rate of hosts that are controlled by attackers. Also, the comprehensive evaluations show that the overheads of our system are trivial, and that our system is practical to implement and deploy in the cloud data centers.

Now a day’s Security in cloud computing is one of the broad areas for researchers. Cloud computing is a term that involve to deliver the services over the Internet. So, it requires the security upgrade in data transmission approach. One of the ways by which data in the Cloud be secured is cryptography. In fact, the high-quality cloud security can be achieved by efficient encrypting techniques. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve cryptography and DNA computing. Many researchers have tried to exploit the features of ECC field for security applications. In this paper, we attempt to develop a fast hybrid cryptosystem based on Elliptic Curve and DNA computing for providing security service such as confidentiality in the cloud services. The security of the proposed scheme is based on Elliptic Curve Discrete Logarithm Problem (ECDLP). Existing DNA based cryptography technique need more computational power and more processing time with larger key sizes to provide higher level of security. The main goal of our construction is to enhance the security of elliptic curve cryptosystem using DNA Computing. In this approach data stored on the cloud server in the encrypted form and even if data is accessed by the attacker, the attacker can't get the current data.

The continuous and swift progress in the number of the cloud data centers have led to establishment of multitudes of the computational nodes and the huge paradigm. But the assuring the quality of services through these paradigms is still questionable. So tit has become a prominent areas of research. As the quality of service of the data centers plays a vital role in the user satisfaction. The present work carried out in the paper survey the service quality rendered in the previous similar work, identifies the drawbacks and proposes a strategy of migration taking into consideration the multiple metrics. The proposed structure is validated through the cloud simulator to evince its capability in efficiently handling the resources and guaranteeing the quality of service.

Cloud security is becoming more essential than ever with the tremendous development of delicate cloud data. The cloud information and services are located in massively scalable data centers and can be accessed anywhere. Unfortunately, the development of cloud users has been followed by an increase in cloud malicious activity. More and more vulnerabilities are being found, and fresh safety advisories are being released almost every day. Millions of customers surf the cloud for different reasons, so they need extremely secure and persistent services. The cloud storage system interconnect with the a load of potential security risks. So the cross encryption of Ciphertext Policy Attribute Based Encryption (CPAB) and Key Policy Attribute-based encryption algorithm which increases the security level in the encryption side. A segmentation part helps in splitting the encrypted file in storing the data in the cloud side, the Desegmentation part in the receiver side can easily combines spitted data into the single file for validation examine an authentication level in the received data. Here the cloud storage easily with the file fragmentation processes. This processes research over the storing mass amount of data on off- site installation, which can eliminate the cost in maintaining the physical hardware. Cloud's future includes a much greater degree of privacy and authentication, particularly in extending the variety of apps. We suggest a straightforward data protection model where data is encrypted before it is introduced in the cloud using key policy attribute-based encryption to ensure data confidentiality and safety. The storing data is the most common application for the cloud server.

These days Cloud Computing is in demand and the latest technology used by the organisation to handle large amount of data. This paper will explain the concept of Cloud Computing, its models, advantages, disadvantages and also the challenges. The type of testing use for cloud computing will also be discussed in this paper. The organisations may have their own cloud data centre or also can tie up with other data service providers who provides the facility of cloud computing. The data is shared among various servers and then can be accessed from there. It helps to increase the efficiency of the business organisation.

A significant declining trend in rainfall over the Congo basin has been observed over the past three decades. Since the Madden–Julian oscillation (MJO) is a major forcing mechanism for tropical convection and rainfall, the interannual variability and trend in rainfall over the Congo may be partly attributable to variability or changes in the MJO. This study explores the long-term (1979–2018) relationship between the active MJO diagnosed by the real-time multivariate (RMM) MJO phase index data and observed rainfall and cloud data over the Congo during October–March. Since the MJO may significantly enhance rainfall during the wet phases or suppress rainfall during the dry phases, the crux of this paper includes how trends in MJO activity may impact the overall observed precipitation trend over the Congo. The relationship between MJO activity and rainfall over the Congo was documented using statistical techniques and composite analysis. A new, yet simple approach was developed to partition seasonal rainfall depending on the MJO phase (i.e., wet, dry, inactive, and other). Results show a significant correlation between the number of wet and dry MJO days, and rainfall enhancement and suppression over the Congo. While there exists considerable interannual variability in MJO activity and rainfall over the Congo, there is a significant increase in the number of dry MJO days (3.47 days decade−1) which tends to intensify the large-scale drying trend over the Congo during October–March. The increasing trend in the number of dry MJO days is likely enhancing the net drying trend by 13.6% over the Congo.

Cloud computing becomes more sophisticated to provide different services at different levels of user access. Even though various services are accessed at a different level, the security of data being accessed is a highly challenging one. However, there is a number of encryption approaches discussed toward the problem of cloud security; they suffer to achieve higher security as required. The previous ABFD (attribute-based flexible delegation) algorithm uses a set of policies in encrypting the data with specific keys mentioned in the policy. However, the leakage of encryption policy would introduce poor security which can be overcome by adopting multitype encryption standards in different time windows. According to this, an efficient time-variant attribute-based multitype encryption algorithm (TAM) is presented in this paper. The TAM algorithm maintains a taxonomy of attributes and related keys to be used for encryption and decryption. The corresponding keys have been used to generate the ciphertext. The content of taxonomy has been dynamically changing in each time window which makes the difference in integrity management and security performance than previous algorithms. The TAM approach introduces a higher security performance up to 89.6%. The method also reduces the time complexity up to 21 s and increases the throughput performance up to 96%.

Today’s era most of the data required for large businesses are stored on cloud. Cloud systems have number of advantages such as it provides space for store large amount of data and shared resources for processing the stored data. Security of the files stored on the cloud is one of the major problems of cloud system. Because data stored on cloud may leaks to unauthorized user or cloud system itself can modify the data or delete the data. So mainly whatever the data stored on cloud by the owner is retrieved by the users who have the permission to access the data which provides the superior access control to the data. Also need to make provision that the data cannot be accessed by cloud itself that is we need to provide integrity auditing of data. This work proposes cipher text-policy ABE scheme with integrity auditing to provide excellent access control and confidentiality of data.

Nowadays, due to the expansion of people’s living ranges and the impact of human life on the natural environment, climate changes fiercely than before. In order to observe the changing climate environment accurately, multi-modal sensors are used to collect the various data around us, and we could analyze and predict the weather based on these collected data. One of the applications is 3D visualization simulation, and the 3D visualization simulation of cloud data has always been the research hotspot in the field of computer graphics and meteorology. Currently, it is a key challenge to resolve the problems of 3D cloud simulation, such as reducing complexity of modeling and computation and improving the real-time performance. Technically, a method for data modeling and optimizing based on Weather Research and Forecasting (WRF) is proposed in this paper, aiming to solve the problems of the existing 3D cloud simulation and realize 3D virtual simulation of real-world cloud data. According to the characteristics (e.g., color, size, shape) of the cloud, the spherical particle system is designed to model, and the initial color, size, shape, and other attributes are given to these spherical particles to realize the modeling of WRF cloud data. From the perspective of new particles’ generation, the level of detail (LOD) technique, based on the relationship between the quantity of new generated spherical particles and the distance of the viewpoint, is used to change the quantity of new particles generated in real time according to the distance of the simulated scene distance. Finally, illumination model is introduced to render and simulate the modeling particles. Experimental simulation results verify the effectiveness of this method in improving the modeling and rendering speed of cloud data as well as the fidelity of the 3D virtualization model.

Nowadays, information security is a challenge especially when transmitted or shared in public clouds. Many of researchers have been proposed technique which fails to provide data integrity, security, authentication and another issue related to sensitivity data. The most common techniques were used to protect data during transmission on public cloud are cryptography, steganography, and compression. The proposed scheme suggests an entirely new approach for data security on public cloud. Authors have suggested an entirely new approach that completely makes secret data invisible behind carrier object and it is not been detected with the image performance parameters like PSNR, MSE, entropy and others. The details of results are explain in result section of paper. Proposed technique have better outcome than any other existing technique as a security mechanism on a public cloud. Primary focus of suggested approach is to minimize integrity loss of public storage data due to unrestricted access rights by uses. To improve reusability of carrier even after data concealed is really a challenging task and achieved through suggested approach.

The most demanded advanced technology throughout the world is cloud computing. It is one of the most significant topics whose application is being researched in today’s time. Cloud storage is one of the eminent services offered in cloud computing. Data is stored on multiple third-party servers, rather than on the dedicated server used in traditional networked data storage in the cloud storage. All data stored on multiple third-party servers is not bothered by the user and no one knows where exactly data saved. It is minded by the cloud storage provider that claims that they can protect the data but no one believes them. Data stored over the cloud and flowing through the network in the plain text format is a security threat. This paper proposes a method that allows users to store and access the data securely from cloud storage. This method ensures the security and privacy of data stored on the cloud. A further advantage of this method is we will be using encryption techniques to encrypt.

The recent advances in virtualization technology have enabled the sharing of computing and networking resources of cloud data centers among multiple users. Virtual Network Embedding (VNE) is highly important and is an integral part of the cloud resource management. The lack of historical knowledge on cloud functioning and inability to foresee the future resource demand are two fundamental shortcomings of the traditional VNE approaches. The consequence of those shortcomings is the inefficient embedding of virtual resources on Substrate Nodes (SNs). On the contrary, application of Artificial Intelligence (AI) in VNE is still in the premature stage and needs further investigation. Considering the underlying complexity of VNE that includes numerous parameters, intelligent solutions are required to utilize the cloud resources efficiently via careful selection of appropriate SNs for the VNE. In this paper, Reinforcement Learning based prediction model is designed for the efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers. The proposed MUVINE scheme is extensively simulated and evaluated against the recent state-of-the-art schemes. The simulation outcomes show that the proposed MUVINE scheme consistently outperforms over the existing schemes and provides the promising results., The final version of this paper is published in IEEE Journal on Selected Areas in Communications