Cloud computing environments have introduced a new model of computing by shifting the location of computing infrastructure to the Internet network to reduce the cost associated with the management of hardware and software resources. The Cloud model uses virtualization technology to effectively consolidate virtual machines (VMs) into physical machines (PMs) to improve the utilization of PMs. Stu...

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks appear to be main threats for cloud computing. The protection of cloud services against DoS and DDoS attacks is realized using Intrusion Detection Systems (IDSs).This paper aims to evaluate the experimental results of our proposed quantitative solution. The experiments are performed in a private cloud model deployed using ...

In large-scale computing centres, the advancement of knowledge in regard to the predicted power consumption (PC) and concerns of host servers that run virtual machines (VMs) could improve the capacity planning and networks’ energy efficiency. A parameterised power model is proposed to explore the individual components within the virtualisation-based cloud radio access network. The model evaluat...

With the advent of Docker, it becomes popular to bundle Web applications (apps) and their libraries into lightweight linux containers and offer them to a wide public by deploying them in the cloud. Compared to previous approaches, like deploying apps in cloud-provided virtual machines (VMs), the use of containers allows faster start-up and less overhead. However, having containers inside VMs ma...

When synchronization primitives such as locking and read-copy update (RCU) execute within virtual machines (VMs), preemption can cause multi-second latency spikes, increasing peak memory footprint and fragmentation inside VMs, which in turn may trigger swapping or VM ballooning. The resulting CPU utilization and memory footprint increases can negate the serverconsolidation benefits of virtualiz...

Scalability in monitoring and management of cloud data centres may be improved through the clustering of virtual machines (VMs) exhibiting similar behaviour. However, available solutions for automatic VM clustering present some important drawbacks that hinder their applicability to real cloud scenarios. For example, existing solutions show a clear trade-off between the accuracy of the VMs clust...

Developing and optimizing a virtual machine (VM) is a tedious task that requires many years of development. Although VMs share some common principles, such as a Just In Time Compiler or a Garbage Collector, this opportunity for sharing hash not been yet exploited in implementing VMs. This paper describes and evaluates VMKit, a first attempt to build a common substrate that eases the development...

We consider variational multiscale (VMS) methods with h-adaptive technique for the stationary incompressible Navier–Stokes equations. The natural combination of VMS with adaptive strategy retains the best features of both methods and overcomes many of their deficits. A reliable a posteriori projection error estimator is derived, which can be computed by two local Gauss integrations at the eleme...

We are developing an advanced IaaS (Infrastructure-as-a-Service) datacenter management system that dynamically minimizes running physical servers depending on resource utilization. The management system periodically monitors the loading of a datacenter, and dynamically repacks virtual machines (VMs) into optimal physical servers. Live migration of VMs and the standby mode of physical servers ar...

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. need to be scheduled on Virtual Machines (VMs), which resources a environment. Due immensity search space for possible mappings tasks VMs, meta-heuristics introduced task scheduling. In makespan load balancing, Quality Service (QoS) parameters crucial. This ...