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Mobile Ad-hoc Networks (MANETs) are used in a wide range of applications because of their unique capabilities. The routing in MANET is regarded as a main challenge on account of its permanent movement and nodes' randomness that can cause a continual change of network topology, thus finding correct paths among the nodes is the fundamental task of routing protocols. There is a rise in protocols that depend on cross-layer communication across several layers to increase the MANET performance. In this manuscript, a Hybrid Clustering Approach (SG-MFOA) using a Multipath Cross-Layer Design in a MANET Network is proposed. Here, multiple routes are selected for data packet transmission with the help of hybrid routing. Furthermore, a cross-layer metric is acquired depending upon Expected Transmission Time (ETT), Residual Energy and Load Balancing Factor. It is necessary to select a Cluster Head (CH) depending on this trade for proficient routing. So, the cluster head is optimally chosen to utilize SG-MFOA. Thus, the multipath route selection is performed by the multi-objective functions containing bandwidth, congestion delay, transmission delay and to ensure the prolonging of network lifetime, queue delay at each connection Access Category from the available routes to a destination. When compared with the existing methods, like a proficient self-attention-based conditional variational auto-encoder generative adversarial networks-based multipath cross-layer design routing paradigm for MANET (SCVAGAN-MCDR-MANET), Energy Aware Cross Layer Routing Protocol to Increase Route Reliability in MANETs (EACLRP-RR-MANET), Cross-layer Adaptive Fuzzy-based Ad hoc On-Demand Distance Vector Routing Protocol for MANET (CLAF-DVRP-MANET), respectively. [ABSTRACT FROM AUTHOR]

Job scheduling plays a prominent part in cloud computing, and the production schedule of jobs can increase the cloud system's effectiveness. When serving millions of users at once, cloud computing must provide all user requests with excellent performance and ensure Quality of Service (QoS). A suitable task scheduling algorithm is needed to appropriately and effectively fulfil these requests. Several methods were proposed for job scheduling in cloud computing, but the existing techniques do not provide better efficiency. The Dual Interactive Wasserstein Generative Adversarial Network Optimized with Arithmetic Optimization Algorithm Based Job Scheduling in Cloud-Based Internet of Things is proposed to overcome this issue. Primarily, the data from the Alibaba dataset is preprocessed using the Kernel Co-Relation (KC) method. The preprocessed data is given to the Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for task forecasting in the dynamic cloud environment, and it generates scheduled tasks as output. Then the Arithmetic Optimization Algorithm (AOA) is utilized to optimize the weight parameters of the Dual Interactive Wasserstein Generative Adversarial Network. The proposed method precisely predicts the future workload and diminishes extravagant power consumption at cloud data centres. The proposed method is implemented in MATLAB. The proposed method attains lower MSE (Mean Square Error), RMSE (Root Mean Square Error), MAPE (Mean Squared Prediction Error), MAE (Mean Absolute Error), and higher results without optimization algorithm before and after normalization compared to the current approaches. [ABSTRACT FROM AUTHOR]

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies.

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. [ABSTRACT FROM AUTHOR]

We propose a dynamic automated infrastructure model for the cloud data centre which is aimed as an efficient service stipulation for the enormous number of users. The data center and cloud computing technologies have been at the moment rendering attention to major research and development efforts by companies, governments, and academic and other research institutions. In that, the difficult task is to facilitate the infrastructure to construct the information available to application-driven services and make business-smart decisions. On the other hand, the challenges that remain are the provision of dynamic infrastructure for applications and information anywhere. Further, developing technologies to handle private cloud computing infrastructure and operations in a completely automated and secure way has been critical. As a result, the focus of this article is on service and infrastructure life cycle management. We also show how cloud users interact with the cloud, how they request services from the cloud, how they select cloud strategies to deliver the desired service, and how they analyze their cloud consumption. [ABSTRACT FROM AUTHOR]

Cloud Storage Providers (CSPs) provide geography features of an area data stores offering numerous classes for storage accompanied by various costs. One significant problem faced by cloud consumers is how to utilize these storage classes to deliver a device with the time-varying workload on its objects at a minimal price. This price includes the price of resident (for example, storage, put and get prices) and the possible migration price (for example, network price). This paper proposes the Replication and Migration Cost Minimization (RMCM) algorithm in Green Cloud Computing to tackle this issue. This algorithm is using VM's direct migration method, which could decrease data centre prices and power usage by combining virtual resources. To decrease the price of data-placement for devices accompanied by varying workloads over time, developers must make optimal use of the price variation between storage and network services across multiple CSPs. This paper proposes an optimal cost-effective technique (OCET) for copying and migrating data into cloud data centres accompanied by numerous storage classes to attain this aim. This work goal to achieve price reduction in the load assign procedures in multiple data centre environments where virtual machines allocated to a provided data center taking into account energy price differences and the availability of local renewable power generation. The simulation outcomes demonstrate that the RMCM and OCET algorithm could reduce a replication and migration cost and decrease the power consumption of data centre in green cloud computing efficiently. [ABSTRACT FROM AUTHOR]

With the rapid development of data acquisition technology, many industries data already have the characteristics of big data and cloud technology has provided strong support for the storage and complex calculations of these massive data. The meteorological department established the cloud data centre based on the existing storage and computing resources and re-arranged the historical data to reduce the historical data access time of applications. However, the placement of each workflow and input data also affects the average data access time, which in turn affects the computing efficiency of the cloud data centre. At the same time, because of the collaborative processing of multiple nodes, the resource utilisation of cloud data centre has also been paid more and more attention. In addition, with the increase of data security requirements, some privacy conflict data should avoid being placed on the same or neighbouring nodes. In response to this challenge, based on the fat-tree network topology, this study proposes a data privacy protection-based collaborative placement strategy of workflow and data to jointly optimise the average data access time, the average resource utilisation, and the data conflict degree. Finally, a large number of experimental evaluations and comparative analyses verify the efficiency of the proposed method.

Cloud clients face high security risks while sharing physical resources with multiple users. A malicious cloud user exploits co‐residency and hypervisor vulnerabilities to steal and hamper sensitive information of victim virtual machine (VM). To address this alarming issue, this Letter proposes a secure resource allocation model by developing a threat detector based on the analysis of co‐located inter‐VM relations and a workload predictor in the background. The anomalous network traffic, response speed, bandwidth usage and illegal inter‐VM links are considered as security breaches indicator that assists in future threat detection beforehand and consequently guides the secure and resource efficient VM allocation. The comparison of proposed model with state‐of‐the‐art: FCFS, Random‐fit and NSGA‐II based resource allocation approaches indicate that it significantly reduce security threats by 77.38% and number of active servers up to 5.39% with improved resource utilisation up to 26.84% over Random‐fit. [ABSTRACT FROM AUTHOR]

Current geographically distributed cloud data centres (CDCs) require gigantic energy and bandwidth costs to provide multiple cloud applications to users around the world. Previous studies only focus on energy cost minimisation in distributed CDCs. However, a CDC provider needs to deliver gigantic data between users and distributed CDCs through internet service providers (ISPs). Geographical diversity of bandwidth and energy costs brings a highly challenging problem of how to minimise the total cost of a CDC provider. With the recently emerging software-defined networking, we study the total cost minimisation problem for a CDC provider by exploiting geographical diversity of energy and bandwidth costs. We formulate the total cost minimisation problem as a mixed integer non-linear programming (MINLP). Then, we develop heuristic algorithms to solve the problem and to provide a cost-aware request routing for joint optimisation of the selection of ISPs and the number of servers in distributed CDCs. Besides, to tackle the dynamic workload in distributed CDCs, this article proposes a regression-based workload prediction method to obtain future incoming workload. Finally, this work evaluates the cost-aware request routing by trace-driven simulation and compares it with the existing approaches to demonstrate its effectiveness. [ABSTRACT FROM PUBLISHER]

Due to extensive use of Cloud services and newly evolving security threats, most cloud service providers (CSP) deploy varieties of security devices such as, firewalls, IPSec, IDS, etc. for controlling resource accesses based on the data centre security requirements. Today CSPs are looking for systematically hardening the security by incorporating multiple security devices in the network in a cost-effective way. In this paper, we present an automated framework for synthesizing data centre security configurations. We take a dummy data centre topology, CSP security (connectivity and isolation) requirements and business constraints (usability and cost) as input; and then synthesizes the correct and optimal data centre security. It determines the optimal placement of different security devices in the data centre. [ABSTRACT FROM AUTHOR]

Dynamic virtualised resource allocation is the key to quality of service assurance for multi-tier web application services in cloud data centre. In this paper, we develop a self-management architecture of cloud data centres with virtualisation mechanism for multi-tier web application services. Based on this architecture, we establish a flexible hybrid queueing model to determine the amount of virtual machines for each tier of virtualised application service environments. Besides, we propose a non-linear constrained optimisation problem with restrictions defined in service level agreement. Furthermore, we develop a heuristic mixed optimisation algorithm to maximise the profit of cloud infrastructure providers, and to meet performance requirements from different clients as well. Finally, we compare the effectiveness of our dynamic allocation strategy with two other allocation strategies. The simulation results show that the proposed resource allocation method is efficient in improving the overall performance and reducing the resource energy cost. [ABSTRACT FROM AUTHOR]

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective IT solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from the grid and parallel computing models. It uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better IT solutions for large-scale computational data centres. It encompasses different services for supporting data storage, networking, and computing for facilities and amenities for businesses and multinational corporations. The enormous elastic on-demand cloud provisioning resources and services and datasets are processed and stored in tier-level virtualized cloud data centres operated by third-party service providers called cloud owners. The primary issue with these cloud service providers is to provide and maintain data security, privacy, and confidentiality and service availability and data support for end-users. This paper reviews, highlights, and discusses some of the common cloud computing vulnerabilities primarily related to virtualization platforms and their implementations while outsourcing services and resources to different end-users and business enterprises. We then provided blockchain-enabled solutions for virtualized cloud platforms involving both the end-users and cloud service providers (CSP) to address and solve various security and privacy-related vulnerabilities. These solutions will help the data centre industry to improve its virtualized cloud services and resource provisioning facilities. Finally, we discussed different blockchain-related implementation challenges in cloud infrastructures. [ABSTRACT FROM AUTHOR]