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Cloud deployments now increasingly exploit Field-Programmable Gate Array (FPGA) accelerators as part of virtual instances. While cloud FPGAs are still essentially single-tenant, the growing demand for efficient hardware acceleration paves the way to FPGA multi-tenancy. It then becomes necessary to explore architectures, design flows, and resource management features that aim at exposing multi-tenant FPGAs to the cloud users. In this article, we discuss a hardware/software architecture that supports provisioning space-shared FPGAs in Kernel-based Virtual Machine (KVM) clouds. The proposed hardware/software architecture introduces an FPGA organization that improves hardware consolidation and support hardware elasticity with minimal data movement overhead. It also relies on VirtIO to decrease communication latency between hardware and software domains. Prototyping the proposed architecture with a Virtex UltraScale+ FPGA demonstrated near specification maximum frequency for on-chip data movement and high throughput in virtual instance access to hardware accelerators. We demonstrate similar performance compared to single-tenant deployment while increasing FPGA utilization, which is one of the goals of virtualization. Overall, our FPGA design achieved about 2× higher maximum frequency than the state of the art and a bandwidth reaching up to 28 Gbps on 32-bit data width.

Cloud computing enables on-demand access to a public resource pool. Many businesses are migrating to the cloud due to its popularity and financial benefits. As a result, finding a suitable and best Cloud Service Provider is a difficult task for all cloud users. Many ranking systems, such as ANP, AHP and TOPSIS, have been proposed in the literature .However, many of the studies concentrated on quantitative data. But qualitative attributes are equally significant in many applications where the user is more concerned with the qualitative features.The implementation of MCDM approach for the ranking and the selection of the best player in the market as per the qualitative need of the cloud users like business organization or cloud brokers is the aim of this article. An ISO approved standard SMI framework is available for the evaluation of the CSPs.The authors have considered SMI attributes like accountability and security as the criteria for evaluation of the CSPs. The MCDM approach called IVF-TOPSIS that can handle the inherent vagueness in the cloud dataset is implemented in this work

Resource allocation and scheduling algorithms are the two essential factors that determine the satisfaction of cloud users. The major cloud resources involved here are servers, storage, network, databases, software and so on based on requirements of customers. In the competitive scenario, each service provider tries to use factors like optimal configuration of resources, pricing, Quality of Service (QoS) parameters and Service Level Agreement (SLA) in order to benefit cloud users and service providers. Since, many researchers have proposed different scheduling algorithms and resource allocation strategies, it becomes a cumbersome task to conclude which ones really benefit customers and service providers. Hence, this paper analyses and presents the most relevant considerations that would help the cloud researchers in achieving their goals in terms of mapping of tasks to cloud resources.

Cloud is specifically known to have difficulty in managing resource usage during task scheduling, this is an innate from distributed computing and virtualization. The common issue in cloud is load balancing management. This issue is more prominent in virtualization technology and it affects cloud providers in term of resource utilization and cost and to the users in term of Quality of Service (QoS). Efficient procedures are therefore necessary to achieve maximum resource utilization at a minimized cost. This study implemented a load balancing scheme called Improved Resource Aware Scheduling Algorithm (I-RASA) for resource provisioning to cloud users on a pay-as-you-go basis using CloudSim 3.0.3 package tool. I-RASA was compared with recent load balancing algorithms and the result shown in performance evaluation section of this paper is better than Max-min and RASA load balancing techniques. However, it sometimes outperforms or on equal balance with Improved Max-Min load balancing technique when using makespan, flow time, throughput, and resource utilization as the performance metrics.

Voltage fluctuations in batteries form a major challenge the telecommunication towers face. These fluctuations mostly occur due to poor management and the lack of a battery voltage level monitoring system. The current paper presents a battery voltage-level monitoring system to be used in telecommunication towers. The proposed solution is incorporated with a centralized mobile application dashboard for accessing the live data of the installed battery, integrated with voltage-level, current, temperature, fire, and gas sensors. An Arduino Uno microcontroller board is used to process and analyze the collected data from the sensors. The Global Service Message (GSM) module is used to monitor and store data to the cloud. Users are alerted in the case of low voltage, fire, and increase in harmful gases in the tower through Short Message Service (SMS). The experiment was conducted at Ngorongoro and Manyara telecommunication towers. The developed system can be used in accessing battery information remotely while allowing real-time continuous monitoring of battery usage. The proposed battery voltage-level monitoring system contributes to the elimination of battery hazards in towers. Therefore, the proposed battery voltage level monitoring system can be adopted by telecommunication tower engineers for the reduction of voltage fluctuation risks.

Paradigm shift towards cloud computing offers plethora of advantages both for cloud users and Cloud Service Provider (CSP). For cloud users, it offers saving of cost, scaling of resources, pay per use, elastic and on-demand services. On the other hand, it offers centralized resource management and provisioning of operations, safety and security for CSP. By holding multiple virtual IT resources (CPUs, storage servers, network components and software) over the internet, Infrastructure-as-a-Service (IaaS) serves as fundamental layer for all other delivery models. Along with benefits of IaaS, there exists several security and privacy issues and threats to confidentiality, integrity, authentication, access control and availability. In this paper, detailed study of IaaS components, associated security and privacy issues are explored and counter measures for the same are determined. Furthermore, as a result of the study, Model for IaaS Security and Privacy (MISP) is proposed. The model presents a cubical structure and adds more features than the existing models to enhance the security and privacy of data and operations and guide security assessment for safer adoption by enterprises.

Paradigm shift towards cloud computing offers plethora of advantages both for cloud users and Cloud Service Provider (CSP). For cloud users, it offers saving of cost, scaling of resources, pay per use, elastic and on-demand services. On the other hand, it offers centralized resource management and provisioning of operations, safety and security for CSP. By holding multiple virtual IT resources (CPUs, storage servers, network components and software) over the internet, Infrastructure-as-a-Service (IaaS) serves as fundamental layer for all other delivery models. Along with benefits of IaaS, there exists several security and privacy issues and threats to confidentiality, integrity, authentication, access control and availability. In this paper, detailed study of IaaS components, associated security and privacy issues are explored and counter measures for the same are determined. Furthermore, as a result of the study, Model for IaaS Security and Privacy (MISP) is proposed. The model presents a cubical structure and adds more features than the existing models to enhance the security and privacy of data and operations and guide security assessment for safer adoption by enterprises

Nowadays, after the technological development in societies, cloud computing has become one of the most important technologies. It provides users with software, hardware, and platform as remote services over the Internet. The increasing number of cloud users has caused a critical problem in how the clients receive cloud services when the cloud is in a state of instability, as it cannot provide required services and, thus, a delay occurs. Therefore, an algorithm was proposed to provide high efficiency and stability to work, because all existing tasks must operate without delay. The proposed system is an enhancement shortest job first algorithm (ESJF) using a time slice, which works by taking a task in the shortest time first and then the longest first from the queue. Through the experimental results in decreasing the waiting and completion time of the task, as well as taking into account reducing tasks starvation, the result of the proposed ESJF algorithm was compared with the traditional shortest job first (SJF) algorithm. These algorithms were applied when all tasks arrived at the same time, and it proved that the ESJF algorithm works more efficiently compared to SJF.

In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach - deep sparse auto-encoder (DSAE) is employed. In this way, this paper proposes a NIDS model for VCN named - GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model's performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets - NSL-KDD, UNSW-NB15, and CICIDS 2017 and found better than other methods.

Abstract Internet of Things (IoT) is a leading concept that envisions everyday objects around us as a part of internet. In order to accomplish this attribution, cloud computing provides a pathway to deliver all the promises with IoT enabled devices. The outbreak of COVID-19 coronavirus, namely SARS-CoV-2, acts as feather to the cap for the growth of Cloud users. With the increasing traffic of applications on cloud computing infrastructure and the explosion in data center sizes, QoS along with energy efficiency to protect environment, reducing CO2 emissions is need of the hour. This strategy is typically achieved using Three Layer upper Threshold (TLTHR) policy to analyze and perform VM consolidation. The proposed model controls number of migrations by placement of virtual machines, based on VMs and their utilization capacity on host. The efficacy of the proposed technique is exhibited by comparing it with other baseline algorithms using computer based simulation. Hence better QoS and energy efficiency has been obtained than other classical models.