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Cloud computing technology provides flexible, on-demand, and completely controlled computing resources and services are highly desirable. Despite this, with its distributed and dynamic nature and shortcomings in virtualization deployment, the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties. The Intrusion Detection System (IDS) is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources. DDoS attacks are becoming more frequent and powerful, and their attack pathways are continually changing, which requiring the development of new detection methods. Here the purpose of the study is to improve detection accuracy. Feature Selection (FS) is critical. At the same time, the IDS's computational problem is limited by focusing on the most relevant elements, and its performance and accuracy increase. In this research work, the suggested Adaptive butterfly optimization algorithm (ABOA) framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase, that was motivated by this motive Candidates. Accurate classification is not compromised by using an ABOA technique. The design of Deep Neural Networks (DNN) has simplified the categorization of network traffic into normal and DDoS threat traffic. DNN's parameters can be finetuned to detect DDoS attacks better using specially built algorithms. Reduced reconstruction error, no exploding or vanishing gradients, and reduced network are all benefits of the changes outlined in this paper. When it comes to performance criteria like accuracy, precision, recall, and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches. Hence the proposed ABOA + DNN is an excellent method for obtaining accurate predictions, with an improved accuracy rate of 99.05% compared to other existing approaches. [ABSTRACT FROM AUTHOR]

Cloud services gain more attention due to its accessibility, performance, and cost factors. Cloud offers a wide range of services and completes the task without any delay due to its scheduling policies. Task scheduling is an important factor in cloud computing applications. The performance of applications increases due to an effective scheduling strategy. The cloud resources are allocated to the tasks through task scheduling. Factors like customer satisfaction, resource utilization, better performance make task scheduling crucial for service providers. Depending on the scheduling schemes support in clouds, scheduling is categorized into single cloud or multi-cloud scheduling. Multi-cloud environment provides diverse resources and significantly reduces the cost and commercial limitations. However, reducing the cost functions and makespan are the major factors considered to avoid customer dissatisfaction. But it is essential to concentrate on other factors, such as throughput, delay, Makespan, waiting time, response time, utilization, and efficiency to improve the quality of services. This research work presents a Multi-Swarm Optimization model for Multi-Cloud Scheduling for Enhanced Quality of Services for a multi-cloud environment. Experimental results demonstrate that the proposed approach performs better in all aspects compared to existing techniques, such as Adaptive energy-efficient scheduling, single objective particle swarm optimization scheduling, and improves the quality of services. [ABSTRACT FROM AUTHOR]

In cloud computing, scheduling and resource allocation are the major factors that define the overall quality of services. An efficient resource allocation module is required in cloud computing since resource allocation in a single cloud environment is a complex process. Whereas resource allocation in a multi-cloud environment further increases the complexity of allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resource availability status and resource capability before allocating to the requested tasks. So, in this research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resource considering the resource status, distance, bandwidth, and task requirements. Proposed model performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resources in 47 s, with a minimum energy consumption of 200 kWh. Compared to conventional approaches, the performance of the proposed model is much better in terms of deadline missed tasks, resource requirement, energy consumption, and allocation time. [ABSTRACT FROM AUTHOR]

Many organizations are moving their IT infrastructure to cloud and many are getting ready to move their IT infrastructure to cloud. Leading players in cloud market Amazon and Microsoft are reporting huge increase in the sale of their cloud products. Still many organization have questions on the security and being dependent on the vendor to deliver their service. To overcome some of the concerns of the single cloud storage Multicloud storage was introduced. In this paper we have proposed the Secure, Reliable and High performance cloud storage above multicloud environment. . [ABSTRACT FROM AUTHOR]

Cloud infrastructure service provider allows the users to place their business application into availability zone across various regions world-wide by enabling enterprises like amazon with sufficient capability. Amazon EC2 is an instance to be placed in a zone to provision their resources to run their own applications. Multiple availability zones are composed and located in single region each one is different from its character specification because of its hardware and software built version. These zones allows achieving higher availability with lower failure rates but may results in different quality of service against user requirements. This situation may not advertised by cloud infrastructure service provider. In this paper, we proposed the prediction model build by repeated k-fold cross validation, which overcomes the prediction error Type-I exist in k-fold cross validation. This proposed technique helps us to predict optimum availability zone with best user satisfaction level for amazons EC2 placement in heterogeneous environment. [ABSTRACT FROM AUTHOR]

Cloud computing is a concept which is popular among not only software professionals but also common internet users. It allows a program to be executed on multiple connected machines at the same time over a network. Deduplication is one way of ensuring that the network and storage overhead is minimized. Digital data is growing exponentially and by removing redundancy deduplication technique achieves this. There are many deduplication schemes proposed but these schemes only focuses on the files without encryption. Convergent encryption allows the cloud to enable deduplication on encrypted files. However, it is vulnerable to dictionary attacks. In this paper, we propose a new scheme to address this issue. Instead of deriving the encryption key from the entire content, encryption key will be derived from each block of the content. Encrypted password of the user will be appended with the file content to make the file unique among the users. This will allow the files to be protected from the confirmation of the file attack. [ABSTRACT FROM AUTHOR]

The number of cloud users and their respective workload increases everyday with the inherent benefits of cloud computing. On the other hand, it becomes critical for service providers to maintain Quality of Service (QoS) even under heavy workload conditions. In order to provide better computing services, cloud utilizes Virtual Machine (VM) migration techniques, which eases the process of providing the services to the user without any delay and with minimum energy consumption. The existing cloud computing services mainly rely on migration techniques; nevertheless, handling large VM migrations consumes more energy, which directly affects the VM performances. This necessitates the need to develop an effective VM migration strategy to perform the necessary migrations and avoid unnecessary migrations. Conventional migration techniques perform migration based on static parameters which attains less efficient results and lags in performance while handling the resource utilization. In this research work, a hybrid optimization algorithm is presented to handle VM migration in a cloud environment. Cuckoo search optimization algorithm and particle swarm optimization algorithm are combined to obtain the proposed hybrid optimization model. The major objective of this research work is to reduce energy consumption, computation time, and migration cost. Maximizing resource utilization is another objective of this research work. To validate the research objective, the performance of hybrid optimization model is verified through simulation analysis and compared with conventional algorithms like firefly optimization, whale optimization, hybrid whale optimization, and hybrid bee colony optimization in terms of energy consumption, migration cost, resource availability, and computation time. [ABSTRACT FROM AUTHOR]