Optimized Inter-domain Communications Among Multiple Virtual Machines Based on Shared Memory

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users’ and cloud providers’ perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.

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.

Cloud service providers offer virtual machines (VMs) as services to users over Internet. As VMs are running on physical machines (PMs), PM power consumption needs to be considered. Meanwhile, VMs running on the same PM share physical resources, and there exists great resource contention, which results in VM performance degradation. Therefore, how to place VMs to reduce PM power consumption and guarantee VM performance is still one major challenge. However, existing VMPs did not study VM performance degradation, so they could not guarantee VM performance. To solve the high power consumption and VMs performance degradation problems, this paper explores the balance between saving PM power and guaranteeing VM performance, and proposes a power-aware and performance-guaranteed VMP (PPVMP). First, we investigate the relationship between power consumption and CPU utilization to build a non-linear power model, which is helpful for the following VMP. Second, we construct VM performance models to present the VM performance degradation trend. Third, based on these models, we formulate VMP as a bi-objective optimization problem, which tries to minimize PM power consumption and guarantee VM performance. We then propose an algorithm based on ant colony optimization to solve it. Finally, the results show the efficiency of our algorithm.

Public Clouds are the backbone of the modern IT Industry. The modern tech start-ups to are heavily dependent on the Public Clouds as save the capital expenditure. Public Clouds offer a wide range of Virtual Infrastructure beginning from single core 0.25 GB RAM virtual machines to 128 cored 3904 GB RAM Virtual Servers aided with GPUs. Apart from costing, The performance of the virtual machines can impact on the services offered. The paper tries to analyse the performance of the Light virtual machines provided by the different cloud vendors and maps their ability to handle certain set of tasks. The prime objective is to compare the entry level virtual machines that are mostly used across the industry for the multiple purposes and find which is the efficient and productive. The systems under Test are the 1 GB RAM machines provided by the most popular Cloud service providers.

In virtualized environments, the customers who purchase virtual machines (VMs) from a third-party cloud would expect that their VMs run in an isolated manner. However, the performance of a VM can be negatively affected by co-resident VMs. In this paper, the authors propose vExplorer, a distributed VM I/O performance measurement and analysis framework, where one can use a set of representative I/O operations to identify the I/O scheduling characteristics within a hypervisor, and potentially leverage this knowledge to carry out I/O based performance attacks to slow down the execution of the target VMs. The authors evaluate their prototype on both Xen and VMware platforms with four server benchmarks and show that vExplorer is practical and effective. The authors also conduct similar tests on Amazon’s EC2 platform and successfully slow down the performance of target VMs.

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.

In a typical virtualized cloud system, network-attached storage (NAS) is the common technology for storing the disk image files of virtual machine (VM). Although the client/server model on NAS is more reliable and manageable, a concurrent accessing behavior from multiple VMs on the shared NAS server may exhaust the underlying network bandwidth, thus hurting the disk I/O performance of VMs. In this paper, we improve this problem by a set of optimizations, which are designed around bridging the semantic gap between the file system of VM and NAS server. The experimental result shows that our work can effectively reduce the network traffic between the NAS server and client. Meanwhile, the disk I/O performance of VMs can be also improved.

As the size of data increases and computation becomes complicated in fog computing environments, the need for highperformance computation is increasing. One of the most popular ways to improve the performance of a virtual machine (VM) is to allocate a graphic processing unit (GPU) to the VM for supporting general purpose computing on graphic processing unit (GPGPU) operations. The direct pass-through, often used for GPUs in VMs, is popular in the cloud because VMs can use the full functionality of the GPU and experience virtually no performance degradation owing to virtualization. Direct pass-through is very useful for improving the performance of VMs. However, since the GPU usage time is not considered in the VM scheduler that operates based on the central processing unit (CPU) usage time of the VM, the VM performing the GPGPU operation degrades the performance of other VMs. In this paper, we analyze the effect of the VM performing the GPGPU operation (GPGPU-intensive VM) on other VMs through experiments. Then, we propose a method to mitigate the performance degradation of other VMs by dynamically allocating the resource usage time of the VM and preventing the priority preemption of the GPGPU-intensive VM.

Virtualization technology promotes the development of cloud computing technology, and cloud service providers provide many virtual machines with different configurations. It is becoming more and more important to analyze the performance of virtual machines based on the characteristics of the workload. This paper analyzes the performance of memory-intensive, compute-intensive and I/O intensive workloads running in virtual machines with different sizes. In order to avoid excessive overhead when comparing the performance of virtual machines, this paper proposes a performance prediction method for virtual machines based on the results of micro-benchmarks. Finally, this paper proposes the concept of virtual machine consistency. If a workload has the same performance in two virtual machines, we call the performance of the two virtual machines consistent with respect to this workload.

We experimentally evaluate the performance overhead of the virtual environments VMware Player, QEMU, VirtualPC and VirtualBox on a dual-core machine. Firstly, we assess the performance of a Linux guest OS running on a virtual machine by separately benchmarking the CPU, file I/O and the network bandwidth. These values are compared to the performance achieved when applications are run on a Linux OS directly over the physical machine. Secondly, we measure the impact that a virtual machine running a volunteer @home project worker causes on a host OS. Results show that performance attainable on virtual machines depends simultaneously on the virtual machine software and on the application type, with CPU-bound applications much less impacted than IO-bound ones. Additionally, the performance impact on the host OS caused by a virtual machine using all the virtual CPU, ranges from 10% to 35%, depending on the virtual environment.

Virtual machine (VM) consolidation is necessary for increasing the server utilization; however, it also leads to VM performance degradation. This work presents a method to predict the consolidated VMs performance from the critical system events data. Experiments are designed to demonstrate the effect of system events like interrupts, page faults, mutex operations, and context switching on the consolidated VMs. Results show that the host server counters are not reliable for such predictions. On the other hand, the coupling of the task execution time with the VM system events is an effective way to predict the consolidation performance. Results further show that the VM memory allocation plays an important part in the consolidated tasks performance. The system event data is also used to train an Artificial Neural Network (ANN) for performance prediction on three hypervisors; ESXi, Xen, and XenServer and similar results are observed in all three.

Virtualization, one of the most actively adopted technologies today in computer systems, is increasingly widening its range of applications. As multiple virtual machines are concurrently executed on a physical machine, they compete for physical resources such as CPU, DRAM, HDD and NIC. In large scale data centers or cluster systems, therefore, it is required to balance each physical machine's load by migrating a virtual machine to another physical machine. Since system performance is significantly hindered during migration, a technique that can reduce the migration overheads is required. Interestingly, we notice that virtual machines' memory contain numerous dispensable data. Therefore, in this paper, we propose an efficient virtual machine migration technique called D3 (Discarding Dispensable Data). The basic idea is simple but effective: finding indispensable page frames, and then, transferring only those page frames. In D3, simple metadata is handed over for the dispensable page frames, instead of actual data transmission, and the discarded page frames are reconstructed appropriately after the migration process. One issue is that checking whether a page frame is dispensable or not based on comparing the entire content yields excessive overhead. To overcome this problem, we devise a novel sampling based dispensable page frame detection technique. D3 have implemented in Xen virtualization software and evaluated on a realistic environment with relevant workloads. We show that D3 can efficiently reduce migration time up to 64.2% and size of memory required for completing a migration up to 94.8%. We also present that D3 is very useful to provide a certain level of QoS and better performance.

This paper explores the relationship between I/O scheduling in a virtual machine monitor (VMM) and performance of Map Reduce applications running on the virtual machines (VMs). Traditionally, Map Reduce applications running on the virtual machines perform worse than on the physical machines due to competing for I/O. In this paper, we discuss the effect of I/O scheduling on performance of Map Reduce running on VMs. We present two strategies to improve the I/O scheduling and evaluate the performance through experiments. The results show that the strategies could reduce the percentage of I/O waiting and time consuming of Map Reduce applications effectively.

As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.

Stochastic scheduling for variation-aware virtual machine placement in a cloud computing CPS