Search

A challenge in such scenarios is that cloud vendors may offer varying and possibly incompatible ways to isolate and interconnect virtual machines located in different cloud networks. Our approach is tenant driven in the sense that the tenant provides its connectivity mechanism. We are implementing Blockchain concept in this project. We implement both Public and Private cloud data storage, Private is for sensitive data storage and public cloud normal data storage. We implement this concept for banking system, to identify overall user behaviour with personal identification. Integration of all his / her transactions like Banking, Land Registrations, Gold Purchase or any cash transactions more than Rs. 20k is accounted and monitored. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing. [ABSTRACT FROM AUTHOR]

Retraction statement The publishers of the International Journal of Pervasive Computing and Communications wish to retract the article Alam, M., Mahak, Haidri, R.A. and Yadav, D.K. (2021), “Efficient task scheduling on virtual machine in cloud computing environment”, International Journal of Pervasive Computing and Communications, Vol. 17 No. 3, pp. 271-287. https://doi.org/10.1108/IJPCC-04-2020-0029 An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions. The authors of this article would like to note that they do not agree with the content of this notice. The publishers of the journal sincerely apologize to the readers. The retracted article is available at: https://doi.org/10.1108/IJPCC-04-2020-0029.

Copyright of Journal of Scientific & Technical Applications / Revista de Aplicación Científica & Técnica is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the Internet era, cloud computing is evolved as the efficient distributed platform in the recent years. But the major issue related to the cloud platform is task scheduling. Allocating the suitable VM to the tasks is a challenging task in cloud computing. Many algorithms are proposed to optimize the scheduling process in the cloud environment. The existing algorithms have their own drawbacks. This paper proposed the hybrid model which uses the hierarchical process to prioritize the task before submitting to the scheduler. The Bandwidth-aware divisible task (BAT) scheduling model is modified by adding the Bar system model to develop the hybrid optimization mechanism. The Minimum overload and minimum lease policy is employed for applying the pre-emption in the data center to reduce the overload of the virtual machine. The performance of the proposed hybrid model is evaluated using different parameters. The simulation results prove the efficiency of the hybrid model. [ABSTRACT FROM AUTHOR]

The goal of Internet of Things (IoT) is to make "things" (wearable devices, smart cameras, sensors and smart home appliances) connect to internet. Large storage is required to store huge volume of data that is generated, data processing need to be carried out between IoT devices and the massive number of applications. This process can be made effectively with the help of cloud computing technology. Resources can be effectively utilized with the help of cloud, and IoT plays a significant role in managing the tasks that are to be offloaded to the cloud. The performance of the application is to be enhanced by providing Quality of Service (QoS) and the performance is evaluated in terms of QoS parameters like Power utilization, Makespan and Execution Time. The tasks are allocated based on priority. Fog computing paradigm is used in the proposed model to decrease the makespan of time. The projected mechanism is tested and compared with different present systems and is shown that proposed methodology produced effective results. [ABSTRACT FROM AUTHOR]

Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred to as load balancing (LB). Therefore, in cloud an active load balancing scheme can perhaps enhance the reliability, services and the utilization of resources as well. In this manuscript, the benefits are integrated for Harries Hawks Optimization and Pigeon inspired Optimization Algorithm to create efficient load balancing scheme, which ensures the optimal resources utilizations with tasks response time. The proposed approach is implemented in JAVA Net beans IDE incorporated in the cloudsim framework that is analyzed based on different number of task in order to assess the performance. However, the simulation outcomes demonstrate that the proposed Hawks Optimization and Pigeon inspired Optimization algorithm based load balancing scheme is significantly balance the load optimally amid the Virtual Machines within a shorter period of time than the existing algorithms. The efficiency of the proposed method is 97% compared to the other existing methods. The computational time, cost, throughput analysis, make span, latency, execution time are determined and gets analysed, compared with the Harries Hawks Optimization, Spider Monkey Algorithm, Ant Colony Optimization and Honey Bee Optimization. [ABSTRACT FROM AUTHOR]

Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.

Accurate working set size estimation is important to increase the consolidation ratio of data centers and to improve the efficiency of live migration. Thus, it is important to come up with a technique that provides an accurate estimation of the working set size of virtual machines that can respond to changes in memory usage in real-time. In this paper, we study the problem of working set size estimation in virtual machines and come up with a method that allows us to better estimate the working set size of virtual machines in Linux. Toward that end, we design a finite state machine that can be used to accurately estimate the working set size and that is responsive to changes in workload. We then implement the algorithm on Linux using QEMU-KVM as our hypervisor. The system is tested using the sysbench benchmark for memory, CPU, and database workloads. The results indicate that our algorithm provides better results in terms of average working set size estimations and is competitive with existing techniques in terms of page faults.

For the problem that mobile devices that are far away from the base station obtain limited wireless resources, which causes high transmission delays, the study proposes a multi-hop path to assist users who are far away from the base station to use edge computing resources. And it combines cloud radio access network and wireless self-organising network. The modelling aims to minimise the total energy consumption under the constraints of task delay considering the mobility of mobile devices. Meanwhile, it selects the joint optimisation problem of the path for data transmission and the virtual machine for calculation. This study also introduces the krill herd algorithm and analyses its advantages and disadvantages. The author enhances the global search ability of the algorithm by defining perturbation factors in the random diffusion behaviour and introduces a new strategy of elite selection and retention into the iterative process to improve the convergence accuracy. Finally, the improved krill herd algorithm is used to solve the joint optimisation problem and a better allocation result than the separate resource allocation of calculation and communication is obtained. The experiment proves that the selection algorithm combining virtual machine and routing proposed in this study can achieve the expected results.

Some complex digital circuits must host various operating systems in a single electronic platform to make real-time and not-real-time tasks compatible or assign different priorities to current applications. For this purpose, some hardware–software techniques—called virtualization—must be integrated to run the operating systems independently, as isolated in different processors: virtual machines. These are monitored and managed by a software tool named hypervisor, which is in charge of allowing each operating system to take control of the hardware resources. Therefore, the hypervisor determines the effectiveness of the system when reacting to events. To measure, estimate or compare the performance of different ways to configure the virtualization, our research team has designed and implemented a specific testbench: an electronic system, based on a complex System on Chip with a processing system and programmable logic, to configure the hardware–software partition and show merit figures, to evaluate the performance of the different options, a field that has received insufficient attention so far. In this way, the fabric of the Field Programmable Gate Array (FPGA) can be exploited for measurements and instrumentation. The platform has been validated with two hypervisors, Xen and Jailhouse, in a multiprocessor System-on-Chip, by executing real-time operating systems and application programs in different contexts.

Neural networks become the only way to solve problems in some areas. Such tasks as recognition of images, sounds, classification require serious processor power and memory for training and functioning of the network. Modern mobile devices have quite good characteristics for primary layers of deep neural networks, but there are not enough resources for whole network. Since neural networks for mobile devices are trained separately on external resources, a method of distributed work of a neural network with vertical distribution over sets of layers with synchronization of training data was developed. The model is divided after saving its state, all layers on the mobile device are converted to the format for the mobile framework and synchronized with the device after training on a distributed platform. Variables and coefficients are formed separately, which allows to significantly reduce the size of the neural network data file uploaded to the device. An algorithm for automatic selection of a neural network separation point was proposed. It based on the data amount transferred between the layers and the load on the mobile device resources. The approach allows to use full-size deep neural networks with a mobile device. Performance experiment showed possibility of obtains an acceptable response even with an unstable communication channel without overloading communication channels and device resources.

Abstract Cloud Computing enables providers to rent out space on their virtual and physical infrastructures. Denial of Service (DoS) attacks threaten the ability of the cloud to respond to clients requests, which results in considerable economic losses. The existing detection approaches are still not mature enough to satisfy a cloud-based detection systems requirements since they overlook the changing/dynamic environment, that characterises the cloud as a result of its inherent characteristics. Indeed, the patterns extracted and used by the existing detection models to identify attacks, are limited to the current VMs infrastructure but do not necessarily hold after performing new adjustments according to the pay-as-you-go business model. Therefore, the accuracy of detection will be negatively affected. Motivated by this fact, we present a new approach for detecting DoS attacks in a virtualized cloud under changing environment. The proposed model enables monitoring and quantifying the effect of resources adjustments on the collected data. This helps filter out the effect of adjustments from the collected data and thus enhance the detection accuracy in dynamic environments. Our solution correlates as well VMs application metrics with the actual resources load, which enables the hypervisor to distinguish between benignant high load and DoS attacks. It helps also the hypervisor identify the compromised VMs that try to needlessly consume more resources. Experimental results show that our model is able to enhance the detection accuracy under changing environments.

Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results. [ABSTRACT FROM AUTHOR]

Cloud datacenters on integration with virtualization provide the dynamic and flexible resource provisioning for the computation or processing of the data-intensive applications. To carry out the operations efficiently in virtualized environment, energy consumption has become one of the major challenges. Therefore, optimal virtual machine (VM) mapping to the Physical Machines is required, otherwise power consumption can drastically hike the overall cost. This paper proposes a novel multi-objective approach for allocating the VMs using dynamic data structure R-Tree which is analogous to bin packing problem. R-Tree optimally handles the accommodation of a large number of multidimensional objects without impacting the depth of the tree. The proposed approach tries to pack as many VMs to the host, without breaching their capacities as to increase the profit. The term profit is a multi-valued attribute which includes the count of hosts, service-level agreement (SLA) Violations, Energy Consumption, and cost of the datacenter. CloudSim toolkit is used to conduct the simulation and the results are analyzed, which shows the reduction in energy consumption and SLA Violations. Hence, it provides enough scope for future research. [ABSTRACT FROM AUTHOR]

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers. [ABSTRACT FROM AUTHOR]

The performance levels of a computing machine running a given workload configuration are crucial for both users and providers of computing resources. Knowing how well a computing machine is running with a given workload configuration is critical to making proper computing resource allocation decisions. In this paper, we introduce a novel framework for deriving computing machine and computing resource performance indicators for a given workload configuration. We propose a workload/machine index score (WISE) framework for computing a fitness score for a workload/machine combination. The WISE score indicates how well a computing machine is running with a specific workload configuration by addressing the issue of whether resources are being stressed or sitting idle wasting precious resources. In addition to encompassing any number of computing resources, the WISE score is determined by considering how far from target levels the machine resources are operating at without maxing out. Experimental results demonstrate the efficacy of the proposed WISE framework on two distinct workload configurations. [ABSTRACT FROM AUTHOR]

The common perception in both academic literature and industry today is that virtual machines offer better security, whereas containers offer better performance. However, a detailed review of the history of these technologies and the current threats they face reveals a different story. This survey covers key developments in the evolution of virtual machines and containers from the 1950s to today, with an emphasis on countering modern misperceptions with accurate historical details and providing a solid foundation for ongoing research into the future of secure isolation for multitenant infrastructures, such as cloud and container deployments. [ABSTRACT FROM AUTHOR]

Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and Map Reduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namely logistic regression is applied to calculate attack's conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead. [ABSTRACT FROM AUTHOR]

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random‐access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non‐fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work. [ABSTRACT FROM AUTHOR]

Recently, FUSE based user space file systems have gained importance due to their ease of implementation. Their lower performance versus implementation benefits, compared to traditional in-kernel file systems, has always been a point of debate among researchers. As FUSE requires additional context switching to perform file related operations, there is a noticeable increase in CPU utilization. In this era of cloud computing, where the focus is shifting on running applications in virtual machines, increased CPU utilization during file operations can have considerable impact on performance, as resources of the hypervisor are shared among virtual machines. This degradation in performance can become a major cause of concern especially when resources are over-committed. There has been ongoing research regarding improvement in performance of user space file systems, and this will help in providing a systematic study on evaluating their performance. For an in-depth examination, we analyzed the performance of FUSE based file systems running in a guest virtual machine and highlighted some of the scenarios, where there could be a major damage to the file system's performance. We have also showed here, that a careful selection of parameters like block sizes and type of read/write operations can improve performance of these file systems significantly by 40% to 45% even in heavily loaded environment. This study will inspire users and developers to enhance the performance of FUSE based file systems which are now commonly used in cluster of virtual machines. [ABSTRACT FROM AUTHOR]

As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned. [ABSTRACT FROM AUTHOR]

To meet rising demands for computing resources, information technology service providers need to select cloud‐based services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large‐scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning‐based method in cloud computing for the automated use of virtual machines. Machine learning‐based virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance. [ABSTRACT FROM AUTHOR]

Virtual machines migration is an essential feature of virtualisation technology which brings many advantages in cloud computing management process such as proactive fault tolerance, load balancing, and power management. There have been many virtual migration algorithms that have been developed but it is hard to find one that best suits all applications as well as ensures the desire of all stakeholders. The optimal virtual machines migration of cloud computing is usually NP‐hard or NP‐complete. In addition, determining when virtual machines migrate is a challenge because of the difficulty of fault detection in cloud computing. In this study, the authors propose a virtual machines migration game approach for multi‐tier application in infrastructure‐as‐a‐service cloud computing where they first proposed fault detection model based on Takagi–Sugeno fuzzy system with metric's physical machines and then optimal or near optimal migration solution is approximated based on Nash equilibrium. The results of experiment demonstrate the efficiency of their proposal. The proposed virtual machines migration algorithm has been benchmarked in order to highlight their strength and feasibility. [ABSTRACT FROM AUTHOR]

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]

Web has become an open platform, where everybody can build and run and deploy the applications. If a small company or a user wants to supply web services, it is better for them to rent the computing infrastructure. Infrastructureas-a-Service (IaaS) is the delivery of computer components such as servers, network, hardware, storage, computing power, etc. The customer is charged just for the resources consumed like utility-based computing. Virtualization may be a technique to implement cloud computing resources like platform, application, storage, and network. The beauty of virtualization solutions is that you simply can run multiple virtual machines (VM) simultaneously on one computer. Cloud Native, the emerging computing infrastructure has become a replacement trend for cloud computing, especially after the event of containerization technology like docker, and therefore the orchestration system for them like Kubernetes and Swarm. With the growing popularity of Cloud Native, the subsequent problems are raised: (i) most Cloud Native applications were designed for creating full use of the cloud platform, but their file storage system has not been optimized for adapting it. (ii) the normal filing system is meant as a utility for storing and retrieving files, usually built into the kernel of the operating systems. But when placing it to a largescale condition, sort of a network storage server shared by thousands of computing instances, and storing many files, it gets slow and even unstable. (iii) most storage solutions use metadata for faster tracking of files, but the metadata itself will take up tons of space, and therefore the capacity of it is usually limited. If the filing system store metadata directly into hard disc without caching, the tracking of massive small files are going to be slower. [ABSTRACT FROM AUTHOR]

Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques. [ABSTRACT FROM AUTHOR]

The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of resources need to be addressed towards managing the cloud resources efficiently. The provisioning and scheduling of resources with respect to the tasks depends primarily on the quality of service (QoS) requirements of cloud applications and is a challenging task. For the complete satisfaction of the client, execution of tasks should be as per the QoS parameters; hence a QoS aware cloud framework is required for the purpose mapping of resources efficiently. To handle the complex issue of the resource placement problem, a cloud architectural framework named cloud orchestrated framework for efficient resource placement presents efficient and effective management and placement of resources in the cloud. In this paper, a novel QoS aware resource placement algorithm is proposed based on the social spider mating strategy that manages and places tasks for the computation of resources automatically by optimizing the QoS metrics as a significant feature. The performance of proposed algorithm is evaluated in the cloud and results show that the proposed framework performs better in terms of execution cost, execution time, throughput, and availability, reliability, waiting time, turnaround time, utilization and convergence of cloud resources and utilizes these resources optimally. [ABSTRACT FROM AUTHOR]

For the problem that mobile devices that are far away from the base station obtain limited wireless resources, which causes high transmission delays, the study proposes a multi-hop path to assist users who are far away from the base station to use edge computing resources. And it combines cloud radio access network and wireless self-organising network. The modelling aims to minimise the total energy consumption under the constraints of task delay considering the mobility of mobile devices. Meanwhile, it selects the joint optimisation problem of the path for data transmission and the virtual machine for calculation. This study also introduces the krill herd algorithm and analyses its advantages and disadvantages. The author enhances the global search ability of the algorithm by defining perturbation factors in the random diffusion behaviour and introduces a new strategy of elite selection and retention into the iterative process to improve the convergence accuracy. Finally, the improved krill herd algorithm is used to solve the joint optimisation problem and a better allocation result than the separate resource allocation of calculation and communication is obtained. The experiment proves that the selection algorithm combining virtual machine and routing proposed in this study can achieve the expected results. [ABSTRACT FROM AUTHOR]

Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers interconnected in a cluser, access to remote hardware resources often requires facilitation both in hardware and specialized drivers with virtualization support. This limits the availability of resources to specific devices and drivers that are supported by the virtualization technology being used, as well as what the interconnection technology supports. For PCI Express (PCIe) clusters, we have previously proposed Device Lending as a solution for enabling direct low latency access to remote devices. The method has extremely low computing overhead, and does not require any application- or device-specific distribution mechanisms. Any PCIe device, such as network cards disks, and GPUs, can easily be shared among the connected hosts. In this work, we have extended our solution with support for a virtual machine (VM) hypervisor. Physical remote devices can be "passed through" to VM guests, enabling direct access to physical resources while still retaining the flexibility of virtualization. Additionally, we have also implemented multi-device support, enabling shortest-path peer-to-peer transfers between remote devices residing in different hosts.Our experimental results prove that multiple remote devices can be used, achieving bandwidth and latency close to native PCIe, and without requiring any additional support in device drivers. I/O intensive workloads run seamlessly using both local and remote resources. With our added VM and multi-device support, Device Lending offers highly customizable configurations of remote devices that can be dynamically reassigned and shared to optimize resource utilization, thus enabling a flexible composable I/O infrastructure for VMs as well as bare-metal machines. [ABSTRACT FROM AUTHOR]

Setting up a virtual machine (VM) in the cloud is a time-consuming task. Typically, VMs are created from so called VM images, a kind of blueprints used to configure and create a VM. However, to create a VM image manually might be very time-consuming, especially if the VM has to meet certain security benchmarks. In this paper, we present VM2 (Virtual Machine Vending Machine), a tool for creation of VM images and testing them wrt. security benchmarks as well as easy sharing the secure images. Our analysis demonstrated a significant reduction in security issues in hardened images created by VM2 in comparison with corresponding publicly available images. Moreover, our tool provided better results wrt. CIS benchmarks in comparison with the corresponding images commercially offered by CIS. [ABSTRACT FROM AUTHOR]

Extensive use of cloud services has led to the need for service reliability for both the service provider as well as the users. In the Infrastructure as a Service cloud computing model, it is critical to ensure the reliability of resources such as virtual machines (VMs); storage networks etc. The paper proposes a replication-based fault tolerance method to improve the reliability of VM-based services. The proposed approach utilizes a data centre topology-aware method to select physical machines where replicas of VMs may be placed. The selection criteria for VM replica placement favour the physical machines at lower CPU temperature, more available space and at a lower edge length from the physical machine that primarily hosts the VM. By avoiding deteriorating physical machines, this policy increases the probability of successful recovery if the VM or its host physical machine fails. The proposed approach has been evaluated using two metrics, namely recoverability and the total bandwidth consumed in the replication and recovery process. The performance of the approach has been compared with a random replica placement method as well as a state of art algorithm. The simulation results illustrate that the proposed approach provides higher reliability than the other methods. [ABSTRACT FROM AUTHOR]

This study provides a detailed comparison of the power consumption, usability, and applicability of virtual machines (VMs)—accessed through zero-client devices—and traditional laptop computers in office settings. The study analyzed high-level plug loads across two office spaces, one using traditional laptops, the other using zero clients. In addition, the individual power consumption of four workstations was monitored and compared. Each workstation user switched between using a laptop or zero client for one week and then using the alternate system the second week. Results of the high-level and workstation analysis show that average workstation plug loads are lower for occupants using zero clients. However, this does not include power consumed by the data center managing VMs. This study calculates the affiliated data center power draw of VMs and shows that server-related loads push total zero-client computing energy higher than that of traditional laptops. Finally, a questionnaire was administered to building occupants to determine the appropriateness of zero-client computing for various user groups, with the results indicating that VMs are most appropriate for more basic software functions. The findings of this study suggest that advances in server technology can help improve both the overall efficiency and the usability of zero-client computing. [ABSTRACT FROM AUTHOR]

Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, micro-service-based systems that are intended to provide over-the-top and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high-computational capacity and any disturbance in supplying requested demand might result in quality of experience (QoE) deterioration. For any online delivery deployment, understanding user's QoE plays a crucial role for rebalancing cloud resources. In this paper, a methodology for estimating QoE is provided for a scalable cloud-based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability, and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modeled to cover up availability and reliability of intermediate and edge content delivery network cache nodes.

Consolidation of virtual machines is used to reduce the power consumed in cloud computing systems. In consolidation, some virtual machines are migrated from some source servers to other destination servers and source servers are turned off. Most current consolidation approaches depend on the utilization of servers to determine both source and destination servers. In this paper, a consolidation approach that depends on multiple criteria is proposed and evaluated. The approach has one algorithm for determining source servers and another algorithm for determining destination servers. Simulations experiments show relevant improvements over utilization-based approach in terms of throughput, power consumption, monetary cost, and scalability by 21%, 12%, 24%, and 37%, respectively.

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the model’s QoS characteristics to detect an overloaded server and then move the model’s data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present work’s minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient.

The Infrastructure-as-a-Service model of cloud computing allocates resources in the form of virtual machines that can be resized and live migrated at runtime. This paper presents a novel distributed resource allocation approach for VM consolidation relying on multi-agent systems. Our approach uses a utility function based on host CPU utilization to drive live migration actions. Experimental results show reduced service level agreement violations and a better overall performance compared to a centralized approach and a threshold-based distributed approach.

Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs) and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and VMs as independent problems. This paper proposes an approach which considers VM placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of VMs and data in Physical Machines (PMs) which are physically closer. The VM and data placement problem (referred as MinDistVMDataPlacement problem) is defined in this paper and has been proved to be NP- Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO), which selects a set of adjacent PMs for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of VMs are placed on these PMs to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects PMs in close proximity and the jobs executed in the VMs allocated by the proposed scheme outperforms other allocation schemes.