Explore the vast range of titles available.

Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.

Multi-cloud computing is becoming a promising paradigm to provide abundant computation resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the real-time computing requirements, frequently varied wireless channel gains and changeable network scale, make the system more dynamic. It is critical to satisfy the dynamic nature of network with different constraints of IoT devices in multi-cloud environment. In this paper, we establish a continuous-discrete hybrid decision offloading model, each device should learn to make coordinated actions, including cloud server selection, offloading ratio and local computation capacity. Therefore, both continuous-discrete hybrid decision and coordination among IoT devices are challenging. To this end, we first develop a probabilistic method to relax the discrete action (e.g. cloud server selection) to a continuous set. Then, by leveraging a centralized training and distributed execution strategy, we design a cooperative multi-agent deep reinforcement learning (CMADRL) based framework to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of cloud servers. Each IoT device acts as an agent, which not only learns efficient decentralized policies, but also relieves IoT devices’ computing pressure. Experimental results demonstrate that the proposed CMADRL could efficiently learn dynamic offloading polices at each IoT device, and significantly outperform the four state-of-the-art DRL based agents and two heuristic algorithms with lower system cost.

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.

The possibility of connecting billions of smart end devices in the Internet of Things (IoT) provides wide range of services to the user. But, the unlimited connectivity of devices in IoT brings security issues when it is connected to wireless networks. Integrating cloud with IoT networks gains more attention as it reduces the sensor node resource limitations. However, the network complexity, open broadcast characteristics of IoT networks are vulnerable to attacks. To ensure network security and reliable operations, Intrusion Detection Systems (IDS) are widely preferred. IDS identifies the anomalies effectively in complex network environments and ensures the security of the network. Traditional intrusion detection systems based on neural networks consume long training time and low classification accuracy. Recently, deep learning methods are widely used in various image and signal processing, security applications. This research work presents a deep learning-based intrusion detection system for multi-cloud IoT environment to overcome the limitations of neural network-based intrusion detection models. The proposed intrusion detection model improves the detection accuracy by improving the training efficiency. Experimental evaluation of proposed model using NSL-KDD dataset provides improved performance than conventional techniques attaining 97.51% of detection rate, 96.28% of detection accuracy, and 94.41% of precision.

Cloud computing and relevant emerging technologies have presented ordinary methods for processing edge-produced data in a centralized manner. Presently, there is a tendency to offload processing tasks as close to the edge as possible to reduce the costs and network bandwidth used. In this direction, we find efforts that materialize this paradigm by introducing distributed deep learning methods and the so-called Federated Learning (FL). Such distributed architectures are valuable assets in terms of efficiently managing resources and eliciting predictions that can be used for proactive adaptation of distributed applications. In this work, we focus on deep learning local loss functions in multi-cloud environments. We introduce the MulticloudFL system that enhances the forecasting accuracy, in dynamic settings, by applying two new methods that enhance the prediction accuracy in applications and resources monitoring metrics. The proposed algorithm’s performance is evaluated via various experiments that confirm the quality and benefits of the MulticloudFL system, as it improves the prediction accuracy on time-series data while reducing the bandwidth requirements and privacy risks during the training process.

Multi-cloud computing is a promising paradigm to support very large scale world wide distributed applications. Multi-cloud computing is the usage of multiple, independent cloud environments, which assumed no priori agreement between cloud providers or third party. However, multi-cloud computing has to face several key challenges such as portability , provisioning , elasticity , and high availability . Developers will not only have to deploy applications to a specific cloud, but will also have to consider application portability from one cloud to another, and to deploy distributed applications spanning multiple clouds. This article presents soCloud a service-oriented component-based Platform as a Service for managing portability, elasticity, provisioning, and high availability across multiple clouds. soCloud is based on the OASIS Service Component Architecture standard in order to address portability. soCloud provides services for managing provisioning, elasticity, and high availability across multiple clouds. soCloud has been deployed and evaluated on top of ten existing cloud providers: Windows Azure, DELL KACE, Amazon EC2, CloudBees, OpenShift, dotCloud, Jelastic, Heroku, Appfog, and an Eucalyptus private cloud.

In order to meet the needs of multimedia communication in multi cloud environment and improve the experience quality of mobile multimedia users, based on multi cloud computing, based on Cooperative cognitive data behavior measurement, this paper proposes an autonomous multimedia cluster computer system and its architecture. First of all, dispersed edge clouds are distributed and integrated, and cooperate to provide multimedia storage and computing functions. In cloudy environment, a hierarchical access service point is designed between edge cloud and core cloud. On this basis, a multimedia cluster computing system suitable for cloudy environment is built. Secondly, a mulch-dimensional mapping mechanism is built between the link management interface array and the task scheduling array in the edge cloud array. Mulch-dimensional multimedia data and real-time task scheduling cooperation cognition, and core cloud are used to interact with DP vectors with dedicated channels. On this basis, we propose an autonomous computer system based on Cooperative Cognition and multimedia data behavior measure. Finally, it is analyzed by three groups of experiments. The resource utilization of the multimedia cluster computing system, the behavior measurement accuracy based on the cooperative cognitive multimedia data behavior measure and the performance of the proposed autonomous multimedia cluster computing (AMC-CCC) in large-scale real-time multimedia communication applications. The results show that the proposed AMC-CCC mechanism has excellent performance in multimedia QoS, resource management and data behavior measurement.

Scheduling of task is one of the most important aspects in cloud computing, edge computing, and mobile cloud computing. It becomes very prominent issue when a framework and its scheduling algorithm caters to task scheduling at the local cloud and remote cloud with multiple cloud environment. This research paper mainly focuses on scheduling of independent and workflow-based tasks at local cloud and remote cloud. It proposes an advanced scheduling mechanism enabled-scalable key parameter yield of resources framework which is an improved version of SKYR framework which has SKYR framework-based task allocation to resources (STAR) algorithm which manages the various aspects of task scheduling at both local and remote cloud. This framework along with its algorithm, manages heterogenous edge cloud at local level and also caters the multiple cloud environment at remote cloud which is the unique feature of the proposed work. This proposed STAR algorithm uses some prominent and established methods such as laxity-based priority access, constraint optimization algorithm based on ant colony system, execution time computation matrix, genome interpretation and crossover scheduling for efficient and cost-effective scheduling. Moreover, it also facilitates reliability and scalability in computation at both levels and also allows comprehensive interaction among various entities involved to provide effective computation to the multiple types of users.

The evolution of Cloud Computing into a service utility, along with the pervasive adoption of the IoT paradigm, has promoted a significant growth in the need of computational and storage services. The traditional use of cloud services, focused on the consumption of one provider, is not valid anymore due to different shortcomings being the risk of vendor lock-in a critical. We are assisting to a change of paradigm, from the usage of a single cloud provider to the combination of multiple cloud service types, affecting the way in which applications are designed, developed, deployed and operated over such heterogeneous ecosystems. The result is an effective heterogeneity of architectures, methods, tools, and frameworks, copying with the multi-cloud application concept. The goal of this study is manifold. Firstly, it aims to characterize the multi-cloud concept from the application development perspective by reviewing existing definitions of multi-cloud native applications in the literature. Secondly, we set up the basis for the architectural characterization of these kind of applications. Finally, we highlight several open research issues drawn up from the analysis carried out. To achieve that, we have conducted a systematic literature review (SLR), where, a large set of primary studies published between 2011 and 2021 have been studied and classified. The in-depth analysis has revealed five main research trends for the improvement of the development and operation DevOps lifecycle of “multi-cloud native applications”. The paper finishes with directions for future work and research challenges to be addressed by the software community.