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Symmetric Searchable Encryption (SSE) schemes facilitate searching over encrypted data, and have been extensively explored to improve function, efficiency or security. There are, however, additional functions that we need to consider in a real-world setting. For example, forward and backward privacy are required to adequately secure newly added documents and deleted documents in Dynamic SSE (DSSE) schemes, and support boolean search (that allows users to search over encrypted data using basic boolean operations) to achieve improved efficiency and retrieval accuracy. Therefore, in this paper we first construct the Verifiable Boolean Search over encrypted data (VBS), and then improve VBS to achieve Forward and Backward privacy (VBS-FB). Finally, we formally prove the security of our proposed schemes, and evaluate their performance using real-world datasets.

The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts on environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88%. We have released the source codes for public use.

Digital technologies had an effect on people's lives. The majority of these digital devices rely on cloud storage to meet their memory needs. Hundreds of thousands of images, videos, and audio files are being transferred to cloud storage. Thousands of people around the world access these media every second. Unauthorized access to these media must be avoided. One of the weak points for data breaches is the user-end encryption. This paper suggests a strategy for improving cloud data protection by combining the AES and blowfish encryption and decryption algorithms. AES-256 is used as the first layer, followed by blowfish as the second layer, in the hybrid solution. The output of the first layer is input to the second layer and the final result is analyzed. The proposed method also discusses other combined approaches such as AES with other traditional algorithms but the proposed method gives significant results compared to other approaches.

Electricity prices differ during different time periods and change from place to place. Cloud workflow applications often require geo-distributed data which is transmitted among heterogeneous servers in intra- and inter- data centers. Such varying electricity prices and data transmission time bring great challenges when optimizing the energy cost for scheduling tasks in workflow applications to heterogeneous servers in cloud data centers. In this paper, we minimize the total electricity cost in a deadline constrained energy-aware workflow scheduling problem with data being geographically distributed across data centers. A scheduling framework is proposed. Strategies are developed to sequence workflow applications, divide deadlines and sort tasks. An adaptive local search method is presented to improve solutions during the search process which dynamically balances intensification using neighborhood structures of increasing size. Components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. Experimental results demonstrate the effectiveness of the proposal for the considered problem.

Edge cloud computing is a new paradigm in which the computation and storage services of remote cloud data centers are moved to Edge Cloud Nodes (ECNs) in network edges. Compared to traditional cloud data centers, ECNs are geographically close to mobile users so the communication latency is significantly reduced. In this paper, we study the problem of allocating Virtual Machine (VM) resources in geo-distributed ECNs to mobile users by using the auction theory. First, we treat mobile users and ECNs as the buyers and sellers of the VM resource auction, respectively. Then, we model the VM resource allocation problem as an n-to-one weighted bipartite graph matching problem with 0-1 knapsack constraints. Since this problem is NP-hard, we design a greedy approximation algorithm to determine the winners of the auction, based on which we propose a truthful Auction-based VM resource Allocation (AVA) mechanism to solve the problem. Moreover, we prove that the AVA mechanism not only achieves an approximately optimal solution for winner selection, but also has the properties of truthfulness, individual rationality, and computational efficiency. Finally, we conduct extensive simulations on real traces to verify the significant performances of the proposed AVA mechanism.

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.

With the arrival of the era of big data, the audit thinking mode has been promoted to change. Under the influence of big data, audit will become an activity of continuous behavior. Through cloud data, the staff can control the operation status and risk assessment of the whole enterprise, timely analyze, control and respond to risks, and protect the enterprise to reduce risks. With the advent of the era of big data, audit data analysis is becoming more and more important. At the same time, a large amount of data analysis also brings challenges to auditors. Methods to deal and solve the challenges has become an urgent problem to be solved at present. This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data, so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.

To solve the problem of data scarcity in IoT image recognition tasks, an EI-based generative data augmentation system is designed in this paper. The system adopts hybrid architecture, and edge server and cloud data center participate in computing together, which is logically divided into the training phase and running phase. The training phase completes data augmentation of source data and training of Convolutional Neural Networks (CNNs), while the running phase processes information through the pretrained CNNs, and completes iteration of the CNNs through expert review and self-learning mechanism. It is worth mentioning that a generative data augmentation model, an Effective Deep Convolutional Generative Adversarial Network (E-DCGAN), has been proposed in the system. The experiments show that E-DCGAN is superior to the baseline model in image generation and data augmentation in both agricultural and medical fields. Compared with the baseline model, the FID values were reduced by 4.73% and 19.59%. Meanwhile, the use of E-DCGAN for data augmentation can significantly improve the image classification model (VGG19, AlexNet, ResNet50), and the average accuracy of agricultural and medical classification results has increased by 0.96% and 1.27% over the baseline.

Many organizations require handling a massive quantity of data. The rapid growth of data in size leads to the demand for a new large space for storage. It is impossible to store bulk data individually. The data growth issues compel organizations to search novel cost-efficient ways of storage. In cloud computing, reducing an execution cost and reducing a storage price are two of several problems. This work proposed an optimal cost-effective data storage (OCEDS) algorithm in cloud data centres to deal with this problem. Storing the entire database in the cloud on the cloud client is not the best approach. It raises processing costs on both the customer and the cloud service provider. Execution and storage cost optimization is achieved through the proposed OCEDS algorithm. Cloud CSPs present their clients profit-maximizing services while clients want to reduce their expenses. The previous works concentrated on only one side of cost optimization (CSP point of view or consumer point of view), but this OCEDS reduces execution and storage costs on both sides.

In this paper we present a survey on keyword based searching algorithms. Various searching techniques are used for retrieving the encrypted data from cloud servers. This survey work involves a comparative study of these keyword based searching algorithms. It concludes that till now multi-keyword ranked search MRSE scheme is the best methodology for searching the encrypted data.

With the rapid development of virtualization techniques, cloud data centers allow for cost effective, flexible, and customizable deployments of applications on virtualized infrastructure. Virtual machine (VM) placement aims to assign each virtual machine to a server in the cloud environment. VM Placement is of paramount importance to the design of cloud data centers. Typically, VM placement involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different constituents including cloud administrators and customers that might have disparate preferences while opting for a placement solution. Thus, it is often valuable to not only return an optimized solution to the VM placement problem but also a solution that reflects the given preferences of the constituents. In this paper, we provide a detailed review on the role of preferences in the recent literature on VM placement. We further discuss key challenges and identify possible research opportunities to better incorporate preferences within the context of VM placement., Comment: 40 pages, 5 figures, 6 tables

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

Involvement of Machine Learning, Real-time Data Analysis and IOT are critically contributing factors in contemporary technical scenarios. Utilization of these three technologies can play a major role in the success of farming thereby modernizing the irrigation system. This paper is focused on the Smart Irrigation System which draws a lot from real-time data analysis, IOT and Machine Leaning. It also presents a study of a system that processes real time data and takes decision about to what extent the field needs to be irrigated. In this way water is saved, its misuse regulated and can be restored for future use if required. Here we rely on cloud data and some other agri-factors which help in decision making. The Smart Irrigation System discussed here shall also regulate the use of underground water my incorporating IOT and weather forecast. The system will also contribute to effective irrigation taking in view the contemporary weather conditions and the requirement of water in the crop.

Cloud service providers emphasize the concept of virtual machines, wherein resources and services are accessed over a distributed network system. The loss of user privacy is indeed contempt in concern to any individual or organization, particularly concerning the Healthcare infrastructure. This research paper introduces a Euclidean L3P-based Multi-Objective Successive Approximation (EMSA) algorithm, an efficient measure of privacy in the Healthcare Cloud. The role-based encryption keys are the critical foundation for the storage of sensitive data in cloud environments is presented here. The proposed EMSA algorithm is the hybridization of the existing Euclidean L3P Distance algorithm, Multi-objective Optimization algorithm, and Successive Approximation Iterative Proximate algorithm. In addition, the performance of the proposed EMSA compared with Bat, PUBAT, TPNGS, WOA, and CIC-WOA algorithms based on performance metrics, such as fitness, privacy, and utility. The simulation shows that, comparison to the existing state-of-the-art algorithms, the proposed EMSA model achieves higher privacy values of 0.34, 0.42, 0.42, 0.35, and 0.30.

Cloud data auditing with file and authenticator deduplication can not only check the integrity of cloud data, but also reduce the cloud’s storage overhead significantly. In this paper, we propose a cloud data auditing scheme supporting file and authenticator deduplication. To the best of our knowledge, the proposed scheme is the first practical one that truly achieves low-entropy security. For the file with low-entropy, the malicious cloud cannot forge any authenticator to pass the auditing verification. The proposed scheme is user-friendly. Users do not need to keep interacting with the Third Party Auditor (TPA) in the auditing phase. We give the detailed security proof to show that the proposed scheme is secure. Comprehensive experiments show the efficiency of our scheme.

Purpose Climate change strains scarce water resources and food production infrastructure, necessitating establishment of sound scientific basis for operation of sustainable alternative food production methodologies - such as aquaponics, which promises high yield versus small footprint. In a climate (such as in Bloemfontein, South Africa) including temperatures below freezing, real-time aquaponics monitoring and control is necessary to mitigate thermal losses and to ensure sustainability of fish stock and bio-filter micro-organisms. The system thermal energy also needs sustainable supplementation during wintertime. This study/paper aims to address the problem of monitoring and controlling thermal energy in a medium sized aquaponics system, to ensure biological sustainability, especially during extreme cold weather events. Design/methodology/approach Required supplementation was determined and the aquaponics system described and quantified in terms of mass-flow power transfer by measuring loop differential temperature and flow rate. Cold front temperature data evaluation determined implementation suitability and -sustainability. Subsystem temperature contributions to the main reservoir, and their cumulative influence on the biological component, were considered. Findings Results indicate thermal supplementation enabled 1.3 °C temperature loss mitigation for a 42-kl water reservoir over a period of three days, offsetting severe system temperature decrease during a cold front event, and preventing organism mortality Originality/value Quantification of flow loop power transfer, and successful supplementation monitoring and control, demonstrates the approach and implementation merit in the Bloemfontein area. Wi-Fi-enabled online real-time data potentially facilitates incorporation into the concept of “Sustainable Smart Cities”.

Purpose Data centres evolve constantly in size, complexity and power consumption. Energy-efficient scheduling in a cloud data centre is a critical and challenging research problem. It becomes essential to minimize the overall operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the cloud data centres. Resource scheduling in cloud data centres is NP-hard and often requires substantial computational resources. Design/methodology/approach To overcome these problems, the authors propose a novel model that leads to nominal operational cost and energy consumption in cloud data centres. The authors propose an effective approach, parallel hybrid Jaya algorithm, that performs parallel processing of Jaya algorithm and genetic algorithm using multi-threading and shared memory for interchanging the information to enhance convergence premature rate and global exploration. Findings Experimental results reveal that the proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms. Originality/value Experimental results reveals that our proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.

Privacy preserved outsourced data access control is a hard task under the control of third–party storage server. To overcome obstacles in the third party based scenario, Attribute-based signcryption system with bilinear pairing tool is one of the most suitable methods in cloud. It maintains the basic features of security like, authenticity, confidentiality, public verifiability, owner privacy, etc. Although, this method has some challenges like a centralized authority used for user secret key generation for de-signcryption operation, and lack in competent attribute revocation. To overcome the issues, we have proposed a scheme of attribute revocable privacy preserved outsourced based data access control mechanism using Attribute-based signcryption. The proposed method allows multi-authorities for assigning both attribute and secret keys for users along with trusted certified authority, which provides security parameters. The analysis of the proposed method shows less computation cost in decryption and authentication verification. The almost same performance and efficiency is found while comparing with the existing schemes after adding new features.

Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.

Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.

In this paper, the author introduces the theory of fuzzy mathematics into the evaluation of higher education. By determining the set of evaluation factors and comments, the author constructs the relevant mathematical model and processes the data, thus turning the evaluation problem into the multiplication problem of the fuzzy matrix. Deep learning is a very active branch of machine learning research in recent years. By increasing the depth and breadth of the model, i.e. increasing the number of operations from the input end to the output end and the number of channels of the model, the scale of parameters of the model is increased, so that the model has the ability to express complex functions. It is appropriate to use deep learning in teaching quality evaluation. The simulation results show that the deep learning model is very effective in dealing with data diversity and extracting complex implicit rules. It can effectively model experts’ professional knowledge and experience. Deep neural network has powerful expressive ability, and can effectively extract the deep-seated laws affecting the teaching quality. It can be used as an assistant technology for the evaluation of teaching quality in Colleges.

Cloud has become one of the most demanding services for data storage. On another hand, the security of data is one of the challenging tasks for Cloud Service Provider (CSP). Cryptography is one of the ways for securing the storage data. Cryptography is not a new approach instead of the efficient utilization of cryptographical algorithms is greatly needed. In this work, we proposed a Secure Hidden Layer (SHL) and Application Programming Interface (API) for data encryption. The SHL is consisting of two major modules (i) Key Management Server (KMS) and (ii) Share Holder Server (SHS) which is used for storing and sharing of cryptographic key. For this purpose, we proposed a server-side encryption algorithm, which is based on the asymmetric algorithm (RSA and CRT) for providing end-to-end security of multimedia data. The experimental results of text and video are evidence that the size of file is not much affected after the encryption and effectively stored at Cloud Storage Server (CSS). The parameters like ciphertext size, encryption time and throughput are considered for performance evaluation of the proposed encryption technique.

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...