Performance Of Virtual Machines Research Articles

Performance analysis of Proxmox VE firewall for network security in cloud computing server implementation

Virtualization server uses the hypervisor technology in its implementation. Hypervisor is one of the virtualization techniques. Where this technique runs several operating systems together in one host. Cloud computing is a combination of the use of internet-based computer technology. The Open source Proxmox VE (Virtual Environment) is a virtual machine operating system. Where virtual machines are built from the Debian Linux operating system, with the modified RHEL kernel. A firewall is a system designed to prevent attacks on the network. Firewalls can be software or hardware that filters network traffic. Proxmox VE Firewall provides a way to protect network infrastructure. In Proxmox VE firewall allows setting rules for all hosts in a cluster or for virtual machines. In this study, the performance of the firewall from the server virtualization using ProxmoxVE will be examined. This study is intended to determine the performance of the firewall default from Proxmox VE. Firewall testing is done by creating filtering rules in the Proxmox VE cloud server. The results of this study will provide an analysis of the performance of the Proxmox VE firewall that runs on cloud servers. The analysis presents that iptables firewall rules can be applied to cloud servers.

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

Measuring Inter-VM Performance Interference in IaaS Cloud

Virtualization has enabled cloud computing to deliver computing capabilities using limited computer hardware. Server virtualization provides capabilities to run multiple virtual machines (VMs) independently in a shared host leading to efficient utilization of server resources. Unfortunately, VMs experience interference from each other as a result of sharing common hardware. The performance interference arises from VMs having to compete for the hypervisor capacity and as a result of resource contention, which happens when resource demands exceed the allocated resources. From this viewpoint, any VM allocation policy needs to take into account VM performance interference before VM placement. Therefore, understanding how to measure performance interference is crucial. In this paper, we propose a simple experimental approach that can be used to measure performance interference in Infrastructure as a Service (IaaS) cloud during VM consolidation.

Dynamic ranking-based MapReduce job scheduler to exploit heterogeneous performance in a virtualized environment

“More data, more information.” Big data helps businesses and research communities to gain insights and increase productivity. Many public cloud service providers offer Hadoop MapReduce as a service based on pay-per-use via infrastructure as a service on clusters of virtual machines promising on-demand horizontal scaling. These clusters of virtual machines are launched in various physical machines across racks in cloud data centers. Such multi-tenancy negatively introduces performance heterogeneity for Hadoop virtual machines due to hardware heterogeneity and interference from co-located virtual machine. Performance heterogeneity largely affects MapReduce job latency and resource utilization of rented Hadoop virtual clusters. Default MapReduce schedulers assign map/reduce tasks assuming the hardware is homogeneous. Interference-aware schedulers perform by only observing the interference pattern generated by co-located virtual machines. These schedulers do not consider the heterogeneous performance of virtual machines. Therefore, we propose a dynamic ranking-based MapReduce job scheduler that places the map and reduces tasks based on a virtual machine’s performance rank to minimize job latency and improve resource utilization. Our proposed approach calculates the performance score for each virtual machine based on hardware heterogeneity and co-located virtual machine interference. Then, it ranks the virtual machines based on the map and reduce performance separately to place map and reduce tasks. To demonstrate our ideas, we have set a test bed with 29 virtual machines on eight physical machines with different configurations and capacities. We modify a default fair scheduler in Hadoop 2.x to incorporate our ideas and evaluate them with different workloads on the PUMA dataset. The proposed method is then compared against a default fair scheduler (resource-aware) and an interference-aware scheduler based on job latency and resource utilization. Finally, we argue in favor of our approach as it improves resource utilization by 30–65% and overall job latency by up to 30%.

A Predictive and Evolutionary Approach for Cost-Effective and Deadline-Constrained Workflow Scheduling Over Distributed IaaS Clouds

Clouds provide highly elastic resource provisioning styles through which scientific workflows are allowed to acquire desired resources ahead of the execution and build required software environment on virtual machines (VMs). However, various challenges for cloud workflow, especially its optimal scheduling, are yet to be addressed. Traditional approaches mainly consider VMs to be with non-fluctuating, time-invariant, stochastic, or bounded performance. This work describes workflows to be deployed and executed over distributed infrastructure-as-a-service clouds with time-varying performance of VMs and is aimed at reducing the execution cost of workflow while meeting deadline constraints. For this purpose, the authors employ time-series-based prediction approaches to capture dynamic performance fluctuations, feed an evolutionary algorithm with predicted performance information, and generate schedules at real-time. A case study based on multiple randomly-generated workflow templates and third-party commercial clouds shows that their proposed approach outperforms traditional ones.

COWCache: effective flash caching for Copy-on-Write virtual disks

Host-side flash caching emerges as an effective solution for improving the performance of virtual machines (VMs) in cloud computing environment. However, for VMs with the commonly used Copy-on-Write (COW) virtual disks, flash caching in fact has negative impacts since it brings lots of unnecessary cache writes, hurting both the VM performance and the flash endurance. This paper presents COWCache, a COW-aware caching solution that addresses this problem by co-designing flash caching with COW-based virtual disks. First, COWCache designs a new architecture that bridges the semantic gap between flash caching and virtual disk management for realizing the VMs’ real data locality. Second, it separately manages COW metadata with fine-grained caching and journaling to improve the metadata caching efficiency. Third, it provides a novel decoupled Copy-on-Write mechanism, which decouples the amplified data requests from the critical I/O path and only admits the data with real VM locality into flash cache. COWCache also introduces a new data structure, the virtual cache map, to mitigate the memory footprint overhead for indexing the cached data in flash. Evaluations show that COWCache improves the application performance by up to 122.7% and reduces the flash cache writes by up to 78.5% compared to traditional flash caching solutions.

An Access Control Model for Preventing Virtual Machine Hopping Attack

As a new type of service computing model, cloud computing provides various services through the Internet. Virtual machine (VM) hopping is a security issue often encountered in the virtualization layer. Once it occurs, it directly affects the reliability of the entire computing platform. Therefore, we have thoroughly studied the virtual machine hopping attack. In addition, we designed the access control model PVMH (Prevent VM hopping) to prevent VM hopping attacks based on the BLP model and the Biba model. Finally, we implemented the model on the Xen platform. The experiments demonstrate that our PVMH module succeeds in preventing VM hopping attack with acceptable loss to virtual machine performance.

A novel integrity measurement method based on copy-on-write for region in virtual machine

More and more enterprises are migrating services and data into virtual machine (VM) using base and increment, and the files in the VM may contain critical data. Therefore, it is necessary to build a trusted environment to enhance the security of the files in VM by the integrity measurement methods (IMMs). In order to simplify the management of the files in integrity measurement, the security manager needs to put these files into one region and then to measures the integrity of the region. If the region is integrity, the files in that region are integrity too. Currently, the traditional IMMs are all based on the message digest algorithms that use too much amount of data, spend much time, and make the performance of VMs degraded. Aiming to address those problems, we propose a novel IMM based on copy-on-write for the region, called RIMM. The method puts the region into the base image, gets the integrity information of the region from the base image by the structures of image and region, and periodically measures the integrity of the region in the increment image by the structure of image and copy-on-write. It is transparent for VM and can eliminate the semantic gap. The evaluation shows that the RIMM can significantly reduce the amount of data and time in the process of integrity measurement. For example, (1) when the region size is 100MB, the amount of data used by RIMM is about 400 times smaller than that used by IMMs based on MD5; the time spent by RIMM is about 600 times less than that used by IMMs based on MD5. (2) when the region size is 9GB, the amount of data used by RIMM is about 29000 times smaller than that used by IMMs based on MD5; the time spent by RIMM is about 3864 times less than that used by IMMs based on MD5.

A Virtual Network Resource Allocation Framework Based on SR-IOV

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

A cloud computing price model based on virtual machine performance degradation

This paper reports the interference effects in virtual machines performance running higher workloads to improve the resources payment in cloud computing. The objective is to produce an acceptable pay-as-you-go model to be used by cloud computing providers. Presently, a price of pay-as-you-go model is based on the virtual machine utilised per time. However, this scheme does not consider the interference caused by virtual machines running concurrently, which may cause performance degradation. In order to obtain a fair charging model, this paper proposes an approach considering a recovery over the initial price considering the virtual machine performance interference. Results showed benefits of a fair pay-as-you-go model, ensuring the effective user requirement. This novel model contributes to cloud computing in a fair and transparent price composition.

KBR: Knowledge Based Reduction Method for Virtual Machine Migration in Cloud Computing

Through large scale deployment of cloud data centers, burning up of energy by data center is very high and violations of service level agreements (SLA) are increasing. Implementation of Virtual machine consolidation techniques direct to physical host over exploitation which degrades the performance of physical host and Virtual machine. It has been a momentous confronts to get better exploitation of resources and energy effectiveness in cloud data center. This paper proposed knowledge based reduction method for Virtual machine assignment and consolidation. This method chooses the Virtual machines from the overloaded host and migrate the Virtual machines to other physical host to diminish energy burning up and get better quality of service. To provide evidence and effectiveness of proposed method, the experiment is conducted with diverse workload circumstances of real system. The experimental consequences exhibits that the proposed method diminishes energy burning up and optimizes infringement of service level agreements in achieving better performance.

Interdomain I/O Optimization in Virtualized Sensor Networks.

In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface.

Managing energy, performance and cost in large scale heterogeneous datacenters using migrations

Improving datacenter energy efficiency becomes increasingly important due to energy supply problems, fuel costs and global warming. Virtualisation can help to improve datacenter energy efficiency through server consolidation which involves migrations that can be expensive in terms of extra energy consumption and performance loss. This is because, in clouds, Virtual Machines (VMs) of the same instance class running on different hosts may perform quite differently due to resource heterogeneity. As a result of variations in performance, different runtimes will exist for a given workload, with longer runtimes potentially leading to higher energy consumption. For a large datacenter, this would both reduce the overall throughput, and increase overall energy consumption and costs. In this paper, we demonstrate how the performance of workloads across different CPU models leads to variability in energy efficiencies, and therefore costs. We investigate through a number of experiments, using the Google workload traces for 12,583 hosts and 492,309 tasks, the impact of migration decisions on energy efficiency when performance variations of workloads are taken into account. We discuss several findings, including (i) the existence of a trade-off between overall energy consumption and performance (hence cost), (ii) that higher utilization decreases the energy efficiency as it offers fewer chances to CPU management tools for energy savings, and (iii) how our migration approach could save up to 3.66% energy, and could improve VMs performance up to 1.87% compared with no migration. Similarly, compared with migrate all, the proposed migration approach could save up to 2.69% energy, and improve VMs performance up to 1.01%. We discuss these results for different combinations of VM allocation, migration policies and different benchmark workloads.11A part of this work is accepted for publication at 13th International Conference on Economics of Grids, Cloud, Systems and Services (GECON-2016), and should appear in Lecture Notes in Computer Science (LNCS), Springer [this paper used that part as one approach to demonstrate the findings].

Virtual machine design for parallel dynamic programming languages

To leverage the benefits of modern hardware, dynamic languages must support parallelism, and parallelism requires a virtual machine (VM) capable of parallel execution — a parallel VM. However, unrestricted concurrency and the dynamism of dynamic languages pose great challenges to the implementation of parallel VMs. In a dynamic language, a program changing itself is part of the language model. To help the VM, languages often choose memory models (MM) that weaken consistency guarantees. With lesser guarantees, local program state cannot be affected by every concurrent state change. And less interference allows a VM to make local assumptions about the program state which are not immediately violated. These local assumptions are essential for a VM’s just-in-time compiler for delivering state-of-the-art VM performance. Unfortunately, some dynamic languages employ MMs that give exceedingly strong consistency guarantees and thereby hinder the development of parallel VMs. Such is the case in particular for languages that depend on a global interpreter lock, which mandates a MM with sequential consistency and instruction atomicity. In this paper, we reflect on a first implementation of the Parallel RPython execution model, which facilitates the development of parallel VMs by decoupling language semantics from the synchronization mechanism used within the VM. The implementation addresses the challenges imposed by strong MMs through strict isolation of concurrent computations. This isolation builds on transactional parallel worlds, which are implemented with a novel combination of software techniques and the capabilities of modern hardware. We evaluate a set of parallel Python programs on a parallel VM that relies on Parallel RPython’s implementation. Compared with a serial baseline VM that relies on a global interpreter lock, the parallel VM achieves speedups of up to 7.5× on 8 CPU cores. The evaluation shows that our realization of Parallel RPython meets the challenges of dynamic languages, and that it can serve as a solid foundation for the construction of parallel dynamic language VMs.

Performance Comparison Between Virtual Machines and Docker Containers

Containers emerged as a new alternative to the virtual machines in large enterprise systems due to their virtualization mechanism that demands a low utilization level of the computational resources. Performance evaluation of Containers and virtual machines provides to practitioners and system administrators information about the quality of the services provisioned for virtualization. The main goal of this paper is to conduct a comparative study of the performance evaluation of virtual machines and containers. In order to accomplish this, a methodology is proposed to evaluate the performance of Docker containers and virtual machines. Additionally, a real-world case study is presented to illustrate the applicability of the proposed approach. For all the experiments, the Docker container shown lower execution times for the requests against virtual machine.

Power-Aware and Performance-Guaranteed Virtual Machine Placement in the Cloud

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.

SnapMig: Accelerating VM Live Storage Migration by Leveraging the Existing VM Snapshots in the Cloud

Virtual Machine (VM) live storage migration is becoming increasingly important and indispensable in the current cloud data centers, for the purposes of load balance, hardware maintenance and system upgrade. Nevertheless, conventional VM migration approaches induce significant extra storage and network traffic to the source server that is already heavily loaded or scheduled for upgrade or repair. As a result, both the VM performance perceived by the application/user and the migration performance are degraded significantly. In this paper, we aim to address this problem by proposing a novel scheme, called SnapMig , to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in backup servers. By outsourcing the task of transferring VM base image and snapshots to the destination server to backup servers, the source server only needs to migrate the latest state changes to the destination server, leading to simultaneous improvement on VM performance, migration time and multiple-VM migration efficiency. Our lightweight prototype implementation of the SnapMig scheme demonstrates that, compared with the state-of-the-art approaches, SnagMig can significantly reduce the migration time and improve the source-server VM performance at the same time. Moreover, the performance improvement provided by SnapMig becomes much more pronounced with multiple concurrent VM migrations.

Mitigation technique for performance degradation of virtual machine owing to GPU pass-through in fog computing

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.

Analysis to Impact of Monitoring interval on Performance of Virtual Machine in Cloud Environments

Analysis to Impact of Monitoring interval on Performance of Virtual Machine in Cloud Environments

Analysis of performance measures to improve the quality of service in cloud based e-government web portal

Online registration system (ORS) is an Indian government digital initiative under the policy of e-Kranti. This sector needs high quality of service (QoS) as it is crowded with numerous patients for various requisitions. In this paper, a stochastic queueing model is proposed to study a single virtual machine queue with batch arrivals and two types of different services (registration and appointment) one after the other in series. After getting this service, a certain batch of patient's may need an additional service from the ORS portal or they may quit. We also considered patient's impatient behaviour wherein batch of patients decide not to take any service in the ORS portal. We attain steady-state results and probability generating functions and the properties of several parameters on the virtual machine performance are analysed numerically. These results can help the management to calculate the service level of the web portal and to improve the performance.