Evaluation of different Operating Systems performance in the Private Cloud with ESXi hypervisor using SIGAR framework

Consolidating multiple virtual machines into a single server has been widely adopted in cloud computing to improve system utilization. However, the sharing of physical CPUs among virtual machines in consolidated systems poses a new challenge in providing an illusion of continuous CPU execution to the guest operating systems (OS). Due to the time-sharing of physical CPUs, the execution of a guest OS is periodically interrupted, while the guest OS may not be aware of the discontinuity of virtual time against the real time. The virtual time discontinuity problem causes the delayed processing of critical OS operations, such as interrupt handling and lock processing. Although there have been several prior studies to mitigate the problem, they address only a subset of symptoms, require the modification of guest OSes, or change the processor architecture. This paper proposes a novel way to comprehensively reduce the inefficiency of guest OS execution in consolidated systems. It migrates the short-lived critical OS tasks to dedicated micro-sliced cores, minimizing the delays caused by time sharing. The hypervisor identifies the critical OS tasks without any OS intervention, and schedules the critical code sections onto the dynamically partitioned cores at runtime. The dedicated micro-sliced cores employ a short sub-millisecond quantum to minimize the response latencies for consolidated virtual machines. By readily servicing the critical tasks, the proposed scheme can minimize the adverse artifact of virtual machine consolidation without any modification of guest OSes.

In traditional framework, mandatory access control (MAC) system and malicious software are run in kernel mode. Malicious software can stop MAC systems to be started and make it do invalid. This problem cannot be solved under the traditional framework if the operating system (OS) is comprised since malwares are running in ring0 level. In this paper, we propose a novel way to use hypervisors to protect kernel integrity and the access control system in commodity operating systems. We separate the access control system into three parts: policy management (PM), security server (SS) and policy enforcement (PE). Policy management and the security server reside in the security domain to protect them against malware and the isolation feather of the hypervisor can protect them from attacks. We add an access vector cache (AVC) between SS and PE in the guest OS, in order to speed up communication between the guest OS and the security domain. The policy enforcement module is retained in the guest OS for performance. The security of AVC and PE can be ensured by using a memory protection mechanism. The goal of protecting the OS kernel is to ensure the security of the execution path. We implement the system by a modified Xen hypervisor. The result shows that we can secure the security of the access control system in the guest OS with no overhead compared with modules in the latter. Our system offers a centralized security policy for virtual domains in virtual machine environments.

Virtualization platforms allow multiple operating systems to run on the same hardware. One of their central goal is to provide strong isolation between guest operating systems, unfortunately, they are often vulnerable to practical side-channel attacks. Cache attacks are a common class of side-channel attacks that use the cache as a side channel. We formalize an idealized model of virtualization that features the cache and the Translation Look aside Buffer (TLB), and that provides an abstract treatment of cache-based side-channels. We then use the model for reasoning about cache-based attacks and countermeasures, and for proving that isolation between guest operating systems can be enforced by flushing the cache upon context switch. In addition, we show that virtualized platforms are transparent, i.e. a guest operating system cannot distinguish whether it executes alone or together with other guest operating systems on the platform. The models and proofs have been machine-checked in the Coqproof assistant.

Cloud datacenters are largely adopting virtual machines (VMs) to provide elastic computing services. As many cloud applications are communication-intensive, such as distributed data processing and web services, satisfactory network performance is critical to guarantee the service quality. In virtualized environments, a major problem is that the end hosts are separated by a virtualization layer and subject to the intervention of the hypervisor’s scheduling. Since the scheduling delays due to CPU sharing are commonly from tens of milliseconds to over one hundred milliseconds, when they are added to packet processing, network performance can be seriously degraded. To tackle this problem, prior works focus dominantly on modifying the hypervisor scheduler to hide the virtualization reality by reducing the delays as much as possible. However, this type of approaches brings many other problems, such as increased VM context switches and more complicated CPU resource allocation. This thesis tries to look at the problem from a different but simpler angle: we let the guest operating system (OS) accept the reality that each virtual CPU (vCPU) can be suspended and resumed at any time, and then think about how to refactor the network I/O subsystem to automatically tolerate such scheduling delays.
\nIn general, network I/O processing in the kernel includes two layers: protocol layer and interrupt layer. Our study shows that both layers are very sensitive to VM scheduling delays, and therefore must be redesigned accordingly. First, in the protocol layer, we propose a paravirtualized approach to help TCP counteract the distortion of congestion control caused by the hypervisor scheduler. Second, in the interrupt layer, for SMP-VMs that have multiple vCPUs, we propose a method to dynamically migrate interrupts from a preempted vCPU to a running one whenever it is possible, so that the delays will not be propagated to the protocol layer. Experiments with our prototypes in Xen/Linux show that our approaches can significantly improve the network throughput and responsiveness.

In cloud computing, it is often assumed that cloud vendors are trusted; the guest Operating System (OS) and the Virtual Machine Monitor (VMM, also called Hypervisor) are secure. However, these assumptions are not always true in practice and existing approaches cannot protect the data privacy of applications when none of these parties are trusted. We investigate how to cope with a strong threat model which is that the cloud vendors, the guest OS, or the VMM, or both of them are malicious or untrusted, and can launch attacks against privacy of trusted user applications. This model is relevant because applications may be small enough to be formally verified, while the guest OS and VMM are too complex to be formally verified. Specifically, we present the design and analysis of an architectural solution which integrates a set of components on-chip to protect the memory of trusted applications from potential software and hardware based attacks from untrusted cloud providers, compromised guest OS, or malicious VMM. Full-system performance evaluation results show that the design only incurs 9 percent overhead on average, which is a small performance price that is paid for the substantial security gain.

Storage cache prefetching is an effective technique for reducing the access latency in hierarchical storage systems when the access pattern is predictable based on access locality.In Infrastructure-as-a-Service (IaaS) clouds, however, storage virtualization significantly rearranges data placement, thereby reducing the spatial locality observed in the host operating system (OS). Moreover, IaaS clouds consolidate applications with various workloads that may change over time.Therefore, the access pattern changes both spatially and temporally.This paper proposes an adaptive storage cache prefetching scheme that uses structural and statistical information inside virtual machines (VMs). Observation of the application's file usage and internal file-layout information in the guest OS allows the host OS to capture spatial and temporal locality during storage access.In addition, application-level performance statistics allow the host OS to tune the prefetch speed adaptively to prevent performance degradation due to excessive prefetching.We implemented a prototype cache prefetching system that cooperates with Linux and PostgreSQL in a VM.Experiments using the TPCx-V benchmark showed that VM-awareness improved the performance by 17.1% compared with traditional prefetching.Our system achieved 3.15 times better performance than an existing non-prefetching caching system.

Cloud infrastructure commonly relies on virtualization. Customers provide their own VMs, and the cloud provider runs them often without knowledge of the guest OSes or their configurations. However, cloud customers also want effective and efficient security for their VMs. Cloud providers offering security-as-a-service based on VM introspection promise the best of both worlds: efficient centralization and effective protection. Since customers can move images from one cloud to another, an effective solution requires learning what guest OS runs in each VM and securing the guest OS without relying on the guest OS functionality or an initially secure guest VM state.We present a solution that is highly scalable in that it (i) centralizes guest protection into a security VM, (ii) supports Linux and Windows operating systems and can be easily extended to support new operating systems, (iii) does not assume any a-priori semantic knowledge of the guest, (iv) does not require any a-priori trust assumptions into any state of the guest VM. While other introspection monitoring solutions exist, to our knowledge none of them monitor guests on the semantic level required to effectively support both white- and black-listing of kernel functions, or allows to start monitoring VMs at any state during run-time, resumed from saved state, and cold-boot without the assumptions of a secure start state for monitoring.

On virtualization platforms, peak memory demand caused by hotspot applications often triggers page swapping in guest OS, causing performance degradation inside and outside of this virtual machine (VM). Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between virtual machine monitor (VMM) and guest operating system (OS). Our work aims at utilizing the free memory scattered in multiple hosts in a virtualization environment to improve the performance of guest swapping in a transparent and implicit way. Based on the insightful analysis of behavioral characteristics of guest swapping, we design and implement a distributed and scalable framework HybridSwap. It dynamically constructs virtual swap pools using various policies, and builds up a synthetic swapping mechanism in a peer-to-peer way, which can adaptively choose different virtual swap pools.We implement the prototype of HybridSwap and evaluate it with some benchmarks in different scenarios. The evaluation results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed and shows a double performance promotion in some cases. Even in the worst case, the system overhead brought by HybridSwap is acceptable.

Virtualization is used for various purposes such as cloud computing and a virtualized environment has been one of the most important platforms. In many virtualized environments, access to its persistent storage device, e.g. hard disk drive (HDD) or solid-state drive (SSD), is performed via two caches, which are page caches of guest and host operating systems. Both caches are usually managed based on the least recently used (LRU) algorithm. Previous work demonstrated that accesses to the host operating system page cache have a negative temporal locality of reference in these cases. Namely, an accessed block will probably not be accessed again in the near future. This locality severely decreases the hit ratio of a host operating system page cache. For addressing this issue, a method for improving the locality of reference in the accesses in the host operating system page cache by fixing data stored in the guest operating system page cache was proposed. However, the method assumed that the hot spot, which is the data area aggressively accessed, was given. Because of this limitation, the method can rarely be used. In this paper, we propose a method for improving the hit ratio of a host operating system page cache by enhancing the locality of reference at the cache. First, we explain the negative temporal locality of reference in virtualized environments. Second, we propose a method for enhancing the locality of reference in the host operating system page cache for improving its hit ratio without an assumption that its hotspot is given. This method observes file accesses in the system using an operating system kernel function and automatically detects the hotspot. This then stores the area in the guest operating system page cache. As a result, the locality of reference in the host operating system cache increases. Lastly, we evaluate this proposed method, then show that this method outperforms the normal method and can provide comparable performance with an existing method even if the hot spot is not given.

Article Free Access Share on The impact of architectural trends on operating system performance Authors: M. Rosenblum Computer Systems Laboratory, Stanford University, Stanford CA Computer Systems Laboratory, Stanford University, Stanford CAView Profile , E. Bugnion Computer Systems Laboratory, Stanford University, Stanford CA Computer Systems Laboratory, Stanford University, Stanford CAView Profile , S. A. Herrod Computer Systems Laboratory, Stanford University, Stanford CA Computer Systems Laboratory, Stanford University, Stanford CAView Profile , E. Witchel Computer Systems Laboratory, Stanford University, Stanford CA Computer Systems Laboratory, Stanford University, Stanford CAView Profile , A. Gupta Computer Systems Laboratory, Stanford University, Stanford CA Computer Systems Laboratory, Stanford University, Stanford CAView Profile Authors Info & Claims SOSP '95: Proceedings of the fifteenth ACM symposium on Operating systems principlesDecember 1995Pages 285–298https://doi.org/10.1145/224056.224078Published:03 December 1995Publication History 188citation2,012DownloadsMetricsTotal Citations188Total Downloads2,012Last 12 Months295Last 6 weeks50 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The paper improves the web service Povitrya aimed at forecasting air pollution in case of man-made disasters by saving the configured CALPUFF atmospheric transport model in a pre-configured image and integrating into the system of software tools for the automatic creation of virtual machines from a pre-saved image after the user request. To do this, an image based on the Ubuntu operating system was configured in the Ukrainian National Grid Infrastructure, to-gether with the necessary CALPUFF atmospheric transport model libraries. Based on the saved image, one can quickly create and run a virtual machine and start the calculations. After the cal-culation, the Povitrya system removes the virtual machine, freeing up cloud resources for other tasks. The example of test calculations shows that due to the transfer of the module of calcula-tion of atmospheric transport to the cloud infrastructure it was possible to reduce the calculation time of the CALPUFF model by almost 2 times, and the download time of weather forecast data was reduced by more than 10 times. In total, the system calculation time after the user's request was reduced by 4 times for the test configuration. In addition, the resilience of the system to cloud infrastructure failures has been increased. The created archive with the operating system and configured for the CALPUFF model runs can also be transferred to other private clouds (for example, Amazon, Google Cloud Platform, Microsoft Azure) and their virtual machines could be used. Thus, the developed web system corresponds to modern trends in the implementation of cloud computing technologies, if necessary, allows scaling and can be adapted to other pri-vate or public cloud computing infrastructures. The system is available for registered users by the link: http://cloud-2.bitp.kiev.ua/ airsystem_english/airsystem.html.

Today’s high-performance RISC microprocessors have been highly tuned for integer and floating point application performance. These architectures have paid less attention to operating system requirements. At the same time, new operating system designs often have overlooked modern architectural trends which may unavoidably change the relative cost of certain primitive operations. The result is that operating system performance is well below application code performance on contemporary RISCS. This paper examines recent directions in computer architecture and operating systems, and the implications of changes in each domain for the other. The requirements of three components of operating system design are discussed in detail: interprocess communication, virt ual memory, and thread management. For each component, we relate operating system functional and performance needs to the mechanisms available on commercial RISC architectures such as the MIPS R2000 and R3000, Sun SPARC, IBM RS6000, Motorola 88000, and Intel i860. Our analysis reveals a number of specific reasons why the performance of operating system primitives on RISCS has not scaled with integer performance. In addition, we identify areas in which architectures could better (and cost-effectively) accommodate operating system needs, and areas in which operating system design could accommodate certain necessary characteristics of cost-effective highperformance microprocessors. This work was supported in part by the National Science Foundation under Grants No. CCR-8703049, CCR-8619663, and CCR-8907666, by the Washington Technology Center, by the Digital Equipment Corporation Systems Research Center and External Research Program, and by IBM and AT&T Fellowships. Bershad is now with the School of Computer Science, Carnegie Mellon University. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 01991 ACM 0-89791 -380 -9/91 /0003 -0108 . ..$1 .50

In this paper, a runtime self-diagnosis and self-recovery infrastructure is presented for embedded systems. Different from existing methods of off-line tracing system logs, our research focuses on analyzing system kernel data structures from runtime memory periodically against predefined constraints. If any violations have been detected, recovery functions are invoked. The prototype system is developed based on a system virtualization layer, above on which the guest operating system, diagnosis and recovery services run simultaneously. The infrastructure requires few modifications to the source code of operating system kernel, thus it can be easily adopted into existing embedded systems for quick implementation. It is also fully software-based without introducing any specific hardware; therefore it is cost-efficient. The experiments indicate that it can correctly detect and recover from several kernel security attacks with acceptable penalty to system performance.

This paper compares the performance of operational short-range ocean forecast systems developed under the Global Ocean Data Assimilation Experiment (GODAE) – an international effort to demonstrate the feasibility of operational ocean forecasting. ‘Best estimates’ from four different operational forecast systems (either analyses, hindcasts, or nowcasts) are inter-compared for the Tasman and Coral Seas, off eastern Australia. Systems considered include those developed in Australia, France, the USA, and the UK. Each system is compared to observations of along-track sea-level anomaly, sea-surface temperature, near-surface velocity, and sub-surface temperature and salinity. All have their strengths and weaknesses, and each system out-performs all others in one aspect or another. With few exceptions, all systems demonstrate signal-to-noise ratios greater than one for all variables. Due to the Australian focus, in addition to the best estimates from the operational systems, operational forecasts and a delayed-mode reanalysis are also inter-compared using the Australian system. The Australian system generally performs the best for sea-level anomaly; the French system is best for near-surface velocities; the USA system generally performs the best for sea-surface temperature; and the UK system is best for sub-surface temperature and salinity. These findings provide useful indicators of deficiencies in each system and clear metrics by which future developments should be assessed. Based on these results and other practical considerations the adoption of multimodel consensus forecasting, using all available forecasts from all systems, is promoted as the most robust approach for the user community. Such developments are being pursued under GODAE OceanView – the successor to GODAE. The results show the success of GODAE in demonstrating the feasibility of operational oceanography.

Since a virtual machine independently uses its own caching policy, redundant disk operations exacerbate the I/O virtualization overhead when virtual machines access large amounts of data on shared storage. This paper presents XHive, an efficient cooperative caching system that is implemented at the virtualization layer, for consolidated environments. Our proposed scheme globally manages buffer caches of consolidated virtual machines in order to accommodate a shared working set in machine memory. A singlet, which is a block cached solely by a virtual machine, is preferentially given more chances to be cached in machine memory by XHive, when it is evicted by a guest operating system. For efficient use of limited memory, singlets are cached in memory that is collaboratively donated from idle memory of virtual machines. Our evaluation shows that XHive significantly reduces disk I/O operations for shared working sets, thereby achieving high read performance and scalability. Improved scalability enables a high degree of workload consolidation with respect to virtual machines that have shared working sets.