Performance Aspects in Virtualized Software Systems

Virtualization has significantly improved hardware utilization by allowing IT service providers to create and run several independent virtual machine instances on the same physical hardware. One of the features of virtualization is live migration of the virtual machines while they are active, which requires transfer of memory and storage from the source to the destination during the migration process. This problem is gaining importance since one would like to provide dynamic load balancing in cloud systems where a large number of virtual machines share a number of physical servers. In order to reduce the need for copying files from one physical server to another during a live migration of a virtual machine, one would like all physical servers to share the same storage. Providing a physically shared storage to a relatively large number of physical servers can easily become a performance bottleneck and a single point of failure. This has been a difficult challenge for storage solution providers, and the state-of-the-art solution is to build a so called distributed storage system that provides a virtual shared disk to the outside world; internally a distributed storage system consists of a number of interconnected storage servers, thus avoiding the bottleneck and single point of failure problems. In this study, we have done a performance measurement on different distributed storage solutions and compared their performance during read/write/delete processes as well as their recovery time in case of a storage server going down. In addition, we have studied performance behaviors of various hypervisors and compare them with a base system in terms of application performance, resource consumption and latency. We have also measured the performance implications of changing the number of virtual CPUs, as well as the performance of different hypervisors during live migration in terms of downtime and total migration time. Real-time applications are also increasingly deployed in virtualized environments due to scalability and flexibility benefits. However, cloud computing research has not focused on solutions that provide real-time assurance for these applications in a way that also optimizes resource consumption in data centers. Here one of the critical issues is scheduling virtual machines that contain real-time applications in an efficient way without resulting in deadline misses for the applications inside the virtual machines. In this study, we have proposed an approach for scheduling real-time tasks with hard deadlines that are running inside virtual machines. In addition we have proposed an overhead model which considers the effects of overhead due to switching from one virtual machine to another

Performance Implications of Virtualization

Virtualization is a component of cloud computing. Virtualization transforms traditional inflexible, complex infrastructure of individual servers, storage, and network hardware into a flexible virtual resource pool and increases IT agility, flexibility, and scalability while creating significant cost savings. Additional benefits of virtualization include, greater work mobility, increased performance and availability of resources, and automated operations. Many virtualization solutions have been implemented. There are plenty of cloud providers using different virtualization solutions to provide virtual machines (VMs) and containers, respectively. Various virtualization solutions have different performance overheads due to their various implementations of virtualization and supported features. A cloud user should understand performance overheads of different virtualization solutions and the impact on the performance caused by different virtualization features, so that it can choose appropriate virtualization solution, for the services to avoid degrading their quality of services (QoSs). In this research, we investigate the impacts of different virtualization technologies such as, container-based, and hypervisor-based virtualization as well as various virtualization features such as, over-allocation of resources, live migration, scalability, and distributed resource scheduling on the performance of various applications for instance, Cassandra NoSQL database, and a large telecommunication application. According to our results, hypervisor-based virtualization has many advantages and is more mature compare to the recently introduced container-based virtualization. However, impacts of the hypervisorbased virtualization on the performance of the applications is much higher than the container-based virtualization as well as the non-virtualized solution. The findings of this research should be of benefit to the ones who provide planning, designing, and implementing of the IT infrastructure

Performance Aspects in Virtualized Software Systems

Virtualization has significantly improved hardware utilization by allowing IT service providers to create and run several independent virtual machine instances on the same physical hardware. One of the features of virtualization is live migration of the virtual machines while they are active, which requires transfer of memory and storage from the source to the destination during the migration process. This problem is gaining importance since one would like to provide dynamic load balancing in cloud systems where a large number of virtual machines share a number of physical servers. In order to reduce the need for copying files from one physical server to another during a live migration of a virtual machine, one would like all physical servers to share the same storage. Providing a physically shared storage to a relatively large number of physical servers can easily become a performance bottleneck and a single point of failure. This has been a difficult challenge for storage solution providers, and the state-of-the-art solution is to build a so called distributed storage system that provides a virtual shared disk to the outside world; internally a distributed storage system consists of a number of interconnected storage servers, thus avoiding the bottleneck and single point of failure problems. In this study, we have done a performance measurement on different distributed storage solutions and compared their performance during read/write/delete processes as well as their recovery time in case of a storage server going down. In addition, we have studied performance behaviors of various hypervisors and compare them with a base system in terms of application performance, resource consumption and latency. We have also measured the performance implications of changing the number of virtual CPUs, as well as the performance of different hypervisors during live migration in terms of downtime and total migration time. Real-time applications are also increasingly deployed in virtualized environments due to scalability and flexibility benefits. However, cloud computing research has not focused on solutions that provide real-time assurance for these applications in a way that also optimizes resource consumption in data centers. Here one of the critical issues is scheduling virtual machines that contain real-time applications in an efficient way without resulting in deadline misses for the applications inside the virtual machines. In this study, we have proposed an approach for scheduling real-time tasks with hard deadlines that are running inside virtual machines. In addition we have proposed an overhead model which considers the effects of overhead due to switching from one virtual machine to another

Performance Comparison of KVM, VMware and XenServer using a Large Telecommunication Application

One of the most important technologies in cloud computing is virtualization. This paper presents the results from a performance comparison of three well-known virtualization hypervisors: KVM, VMware and XenServer. In this study, we measure performance in terms of CPU utilization, disk utilization and response time of a large industrial real-time application. The application is running inside a virtual machine (VM) controlled by the KVM, VMware and XenServer hypervisors, respectively. Furthermore, we compare the three hypervisors based on downtime and total migration time during live migration. The results show that the Xen hypervisor results in higher CPU utilization and thus also lower maximum performance compared to VMware and KVM. However, VMware causes more write operations to disk than KVM and Xen, and Xen causes less downtime than KVM and VMware during live migration. This means that no single hypervisor has the best performance for all aspects considered here

Energy-aware Auto-scaling Algorithms for Cassandra Virtual Data Centers

Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra Virtual Data Centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: \texttt{Opt}, \texttt{LocalOpt} and \texttt{LocalOpt-H}. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. \texttt{LocalOpt} consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely \texttt{BestFit} and \texttt{BestFit-H}. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures

Energy-aware adaptation in managed Cassandra datacenters

Today, Apache Cassandra, an highly scalable and available NoSql datastore, is largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra datacenters. As for all complex services, human assisted management of a multi-tenant cassandra datacenter is unrealistic. Rather, there is a growing demand for autonomic management solutions. In this paper, we present an optimal energy-aware adaptation model for managed Cassandra datacenters that modify the system configuration orchestrating three different actions: horizontal scaling, vertical scaling and energy aware placement. The model is built from a real case based on real application data from Ericsson AB. We compare the performance of the optimal adaptation with two heuristics that avoid system perturbations due to re-configuration actions triggered by subscription of new tenants and/or changes in the SLA. One of the heuristic is local optimisation and the second is a best fit decreasing algorithm selected as reference point because representative of a wide range of research and practical solutions. The main finding is that heuristic's performance depends on the scenario and workload and no one dominates in all the cases. Besides, in high load scenarios, the suboptimal system configuration obtained with an heuristic adaptation policy introduce a penalty in electric energy consumption in the range [+25%, +50%] if compared with the energy consumed by an optimal system configuration

Performance evaluation of containers and virtual machines when running Cassandra workload concurrently

NoSQL distributed databases are often used as Big Data platforms. To provide efficient resource sharing and cost effectiveness, such distributed databases typically run concurrently on a virtualized infrastructure that could be implemented using hypervisor-based virtualization or container-based virtualization. Hypervisor-based virtualization is a mature technology but imposes overhead on CPU, networking, and disk. Recently, by sharing the operating system resources and simplifying the deployment of applications, container-based virtualization is getting more popular. This article presents a performance comparison between multiple instances of VMware VMs and Docker containers running concurrently. Our workload models a real-world Big Data Apache Cassandra application from Ericsson. As a baseline, we evaluated the performance of Cassandra when running on the nonvirtualized physical infrastructure. Our study shows that Docker has lower overhead compared with VMware; the performance on the container-based infrastructure was as good as on the nonvirtualized. Our performance evaluations also show that running multiple instances of a Cassandra database concurrently affected the performance of read and write operations differently; for both VMware and Docker, the maximum number of read operations was reduced when we ran several instances concurrently, whereas the maximum number of write operations increased when we ran instances concurrently.open access</p