On the Evaluation of the Performance Overhead of a Commercial Embedded Hypervisor

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1 Noname manuscript No. First (will MEDIAN be inserted Workshop by the editor) 2012 On the Evaluation of the Performance Overhead of a Commercial Embedded Hypervisor Salvatore Campagna Massimo Violante Abstract Virtualization has been a major topic for desktop and server applications for decades but today it is moving to embedded systems. The reasons that are pushing virtualization in the embedded market are different from those that pushed virtualization for the enterprise. These reasons lie in the issues that modern embedded systems have to face including the need for realtime and deterministic behavior, security and reliability coupled to high processing performances and highthroughput low-latency communication requirements, despite the resource-constrained target environment. Different commercial products are available today, providing virtualization for embedded systems and different application scenarios are possible. This paper is intended to report experimental results executed on a commercial hypervisor for embedded systems, together with an overview of the current state of the art of virtualization technology for embedded systems. Keywords Embedded virtualization, hypervisor, safety critical embedded systems 1 Introduction Virtualization technology is rapidly moving towards embedded systems. The reasons behind this trend include the possibility of lowering costs through consolidation, the possibility of enabling multicore technology supporting different operating systems and architectures, the possibility of enabling security and reliable services Salvatore Campagna, Politecnico di Torino, DAUIN, Italy, Massimo Violante, Politecnico di Torino, DAUIN, Italy, required by applications running on modern embedded systems. One difference, with respect to enterprise virtualization, is represented by the heterogeneous architectures virtualization has to cope with as long as embedded systems are concerned. For this computing systems the x86 architecture is no longer the platform of choice but rather ARM, Power and SPARC architectures are common. Furthermore, today, we are seeing the rise of multicore yet power efficient platforms. Nevertheless, if we look at the impeding issues hindering multicore technology, we realize that software rework and lack of familiarity with parallel programming represent two major concerns. We are moving from architectures optimized to execute single thread applications (superscalar microprocessors) to architectures with many, but much more simple, interconnected cores. Virtualization comes in handy as an enabling technology suitable to favor the introduction of such parallel architectures since its ability to hide hardware details. There is also a growing need for modern embedded systems to comply with market regulations for safety critical areas, such as the recent IEC and ISO 26262, which impose strict functional safety requirements. A typical example of such situation is related to modern In-Vehicle Infotainment (IVI) systems. Users are experiencing services enabled by modern cloud computing technologies, so they expect systems to cooperate and communicate as happening with smartphones, PDAs, tablets and PCs. Moreover, they expect a certain degree of similarity and integration in terms of applications, functionality and HMI. So the user expects to be able to deploy, even, untrusted third-party applications in IVI systems, as they are already used to with personal appliances. In this context virtualization plays a key role since its ability to provide isolation as a design requirement. It enables untrusted applications to be sandboxed, so that software bugs or faults cannot 59

2 2 Salvatore Campagna, Massimo Violante propagate to the rest of the system. A typical deployment could require a hypervisor running multiple operating systems, one meant for running the automotive grade certified software and the other one for running a Linux OS in charge of managing all the non automotive grade software. This deployment would allow isolation of third-party untrusted software in the Linux environment. As a matter of fact, this isolation enforcement reduces safety risks for the whole system separating untrusted software by safety critical software running inside a separate automotive grade environment. Another important factor to take into account is the possibility enabled by virtualization to preserve the know-how of a company. By this point of view, virtualization allows legacy and established software reuse on top of new architectures. This makes it possible for the company to preserve market shares already gained in the past. Summarizing, virtualization represents an enabling technology for modern embedded systems, since it enables consolidation to reduce costs, separation to split existing functionality for security and reliability, and migration to preserve investment in existing and established products. The paper is organized as follows. Section 2 introduces some background information about virtualization for embedded systems concentrating on the requirements of modern embedded hypervisors and the differences between enterprise and embedded virtualization. Section 3 describes the software and hardware setup used to run the tests. The goal is to measure the overhead introduced by a commercial embedded hypervisor while section 4 reports benchmark results concerning CPU, memory and I/O devices virtualization. The final section, summarizes results drawing conclusions. 2 Background Virtualization identifies the practice of inserting an additional indirection layer between the hardware platform and the software stack in such a way to make the software unaware about the underlying hardware. The piece of software in charge of creating such an abstraction is usually referred to as a hypervisor. The environment that the hypervisor shows to the upper software stack faithfully mimics the underlying hardware and is usually referred to as a Virtual Machine (VM). Virtualization technology has experienced a great evolution in the last decades and today different technologies are available for implementing such a software abstraction layer. The main challenge for the hypervisor is to show a virtual environment in a safe, transparent and efficient way. Safe means that each software running inside a virtual machine should not be able to get out of its isolated environment affecting other virtual machines or the hypervisor. Transparency is required to give the software the illusion of having full control of the underlying platform, while efficiency is mandatory in order to get performances close to that of the real machine. The performance growth experienced by embedded computing systems is one of the major reasons for trading a few percentage points of performance so that to realize the benefits of virtualization. Such benefits include resource sharing and usage optimization, security through spatial and temporal isolation, logical partitioning and fast application development/deployment together with legacy application reuse. Virtualization, anyway, is not a technology coming without drawbacks. It is known to bring some overhead related to the execution of an additional software layer acting between the usual hardware and software layers. When embedded systems are concerned, issues related to performances and efficiency are event more evident given the resource constraint environment. For these reasons understanding the requirements for a hypervisor targeting embedded systems is important. When embedded systems are considered, the hypervisor requirements include: 1. Processor support: embedded systems use different architectures with respect to servers and desktops. Typical processor architectures targeting embedded systems include ARM, PowerPC and SPARC; 2. Real-time capability: the concurrent execution of multiple software stacks mandates for bounded and deterministic interrupt latencies; 3. Isolation: strong temporal and spatial isolation is required for safety reasons. Anyway, inter-partition (VM) low-overhead, low-latency yet high-bandwidth communication channels are required; 4. Low footprint hypervisor: the trusted code base must be reduced in order for the whole hypervisor code to fit the small on-chip memory of a typical embedded system and, in order to have a virtual environment that is close to the real hardware, both in terms of transparency and in terms of performances; This requirements are clearly expressed by Fornaeus [1]. Anyway, a non negligible effort is required to bring virtualization on top of modern computing systems and, in this regard, the paper by Popek [2] examines the architecture of modern computer systems specifying the requirements they have to meet to be fully, or at least partially, virtualizable. A. Masrur at al. [3] demonstrate that, with minor modifications to the Xen hypervisor scheduler, it is possible to guarantee real-time services in the context of automotive control applications while 60

3 On the Evaluation of the Performance Overhead of a Commercial Embedded Hypervisor 3 P. Yu and others [4] demonstrate the possibility of improving the real-time behavior of the Xen hypervisor scheduler, so to support real-time operating systems. Steven H. VanderLeest, in paper [5], describes a prototype implementation of a hypervisor which is ARINC 653 compliant, showing the suitability of this approach to fulfill the requirements imposed to modern embedded applications. In order to achieve full system virtualization, different subsystems require different virtualization strategies. The main components to be virtualized include CPU, memory and I/O devices. The most important resource for a computer system, requiring virtualization, is the CPU. Different CPU virtualization technologies are available. The key to understand CPU virtualization lies in understanding that unprivileged instructions run natively on the CPU while privileged instructions must be caught by the hypervisor, in order to avoid them to affect the physical status of the machine. To fulfill the isolation requirement, indeed, the physical status of the machine must be preserved so to create the illusion of exclusive access to hardware resources for the different guest virtual machines. The most important technologies for providing CPU virtualization are full virtualization, paravirtualization and hardware-assisted virtualization. Full virtualization refers to the practice of employing binary translation together with direct execution. The hypervisor translates all privileged instructions on the fly, possibly caching results to improve performances, while executes natively unprivileged instructions. Para-virtualization is meant to improve the efficiency in the communication layer between the VM and the hypervisor. It requires the software to be modified to replace privileged instructions with, so called, hypercalls into the hypervisor. The main benefit of paravirtualization is represented by the lower runtime overhead due to unnecessary on-the-fly job. The drawback is the requirement for access to the software source code. Hardwareassisted virtualization is a technology emerging as a result of the effort of silicon vendors in providing support for virtualization for performance improvement. This technology has been first introduced by Intel (Intel VT) and AMD (AMD-V) for the desktop and server market, but recently has been introduced by ARM in the Cortex-A9 and Cortex A-15, demonstrating the attention that this technology is gaining in the market of embedded systems. The goal of hardware-assisted virtualization is to improve virtualization performances eliminating the need for CPU paravirtualization and binary translation. In paper [6] the authors show a prototype implementation of an embedded hypervisor combining paravirtualization and full virtualization. It can switch from a technology to the other providing support for real-time applications with minimum overhead. Memory is the other critical component requiring virtualization in order to transparently run software on top of a hypervisor. Memory must be shared and dynamically allocated to different virtual machines. This feature is very similar to the virtual memory layer provided by modern operating systems. Typically it requires special data structures, known as page tables, to map virtual to physical addresses. This mechanism is appropriately supported by a hardware cache usually known as Memory Management Unit (MMU). Memory virtualization, thus, requires the MMU to be virtualized so to add an additional indirection layer in the virtual to physical address mapping. I/O devices virtualization is accomplished by the hypervisor in such a way that each virtual machine is presented with a standard set of virtual devices. These virtual devices emulate well-known hardware and translate virtual requests to physical requests. The hypervisor role is then, that of managing the routing of I/O requests between multiple virtual machines and the shared physical devices. If sharing of a device is not required, it is still possible to statically assign single devices to selected virtual machines in order to provide exclusive access to the peripheral. 3 Experimental setup The objective of the following set of experiments is to measure the overhead introduced by an embedded hypervisor. The strategy we decided to follow, in order to be able to measure the overhead introduced by the embedded hypervisor, provides for the setup of two scenarios. In the first one we run the Linux operating system natively, executing a selected set of benchmarks targeting the measurement of execution times and memory bandwidth. In the second one, we run the Linux kernel on top of the hypervisor against the same set of benchmarks. In this setup the only difference is represented by the device drivers being modified in the Linux kernel. This is necessary in order not to break isolation provided by the hypervisor. As a matter of fact, the Linux kernel running on top of the hypervisor is a paravirtualized kernel provided by the hypervisor vendor. It means that the kernel has been modified, so to avoid any direct access to the hardware which must be in complete control of the hypervisor itself. The hardware platform is a Freescale i.mx53 board by Freescale[7]. It features an ARM Cortex-A8 processor with 1GB of DDR RAM at 400 MHz. It is equipped with a full-size SD/MMC card slot hosting a 8 GB Kingston SD card. The Linux kernel version is while the file system, 61

4 4 Salvatore Campagna, Massimo Violante coming from Freescale itself, is based on Ubuntu Lucid and has been mounted as an EXT3 file system. Unuseful features such as GDM have been disabled in order not to add extra overhead. The benchmark suite of choice is the Phoronix test suite [8]. It features more that 130 test profiles grouped in more that 60 test suites including benchmarks for CPU, memory, caches and I/O devices. Among the available benchmarks a convenient subset has been selected. This set includes the following: Lame, ape and flac encoding: this benchmarks are meant to provide figures concerning the CPU and I/O virtualization overhead. The result of all this benchmarks is the time required to encode the same wave file in the corresponding format. We measured the encoding time required by the native setup against the time required by the paravirtualized kernel running on top of the hypervisor. Since encoding benchmarks do execute lots of block-based read and write operations with non negligible I/O overhead we have also been running these benchmarks by mounting all the directories used by the benchmark suite, as a tmpfs file system. Tmpfs is meant to appear to upper layer applications as a mounted file system on persistent storage, but uses volatile memory instead of mass storage. This practice is required since access to SD card is a source of major overhead. C-Ray and n-queens: these two benchmarks are two CPU-intensive benchmarks meant for benchmarking the overhead in CPU virtualization. The main difference with respect to the previous encoding benchmarks is that they are designed so to require a small amount of I/O data. This way access to RAM and storage is reduced and performances are very close to real CPU overhead. The first one, C-Ray, is a a ray tracing benchmark which computes image light data on a 1600x1200 pixel image, considering 8 rays for each pixel. The second one, n-queens, solves the combinatorially hard chess problem of placing N queens on an chessboard of size N such that no queen can attack any other. It uses recursion to search the solution space for queens meeting the correct conditions and makes use of N threads, each one computing the position of a queen. In our experiments N is 18. Both these tests are multithreaded and, in order to exploit the full capability of the underling hardware, they are compiled with OPENMP support enabled so to run threads in parallel. Also in this case the benchmark result is the ratio between the time required for the native and the paravirtualized kernel to solve the problem. In this scenario there is very little difference in using ext3 or tmpfs file system because of the aforementioned little requirements of I/O data. Cachebench: this benchmark is meant to evaluate the performance of the memory hierarchy. We have been running three cachebench benchmarks, one for evaluating the read bandwidth for varying vector lengths in a compiler optimized loop, one for evaluating write bandwidth for varying vector lengths in a compiler optimized loop and the last one generating read/modify/write bandwidth for varying vector lengths in a compiler optimized loop. All these three test mode produce a bandwidth as result (MB/s). The overhead is measured as the ratio between the average bandwidth on the native and paravirtualized setup. D-bench: this is a benchmak meant to measure the overhead in accessing a file system on permanent storage devices. It generates I/O workloads and mimics parallel access to the file system by a fixed number of clients. Our setup features a 30 MB/s Kingston SD card with a 7 GB ext3 file system. Besides providing figures concerning the bandwidth to mass storage it also provides figures concerning the performances of multiple concurrent clients performing the I/O workload. Benchmarks have been executed with an increasing number of clients, in particular with 1, 6, 12, 48, 128, 256 clients accessing, in parallel, the same file system. The bandwidth with 1 client accessing the file system is 8.7 times higher than the bandwidth experienced with 256 clients accessing the same file system in parallel. 4 Experimental results This section reports results concerning the benchmarks aiming at measuring the overhead introduced by the embedded hypervisor. Table 1 reports results obtained running different benchmarks targeting different subsystems. In this scenario we are interested in the overhead introduced when virtualizing the CPU, the memory/cache subsystem and the storage. I/O bound benchmarks include lame, ape and flac encoding, CPU bound benchmarks include C-Ray and n-queens. Cachebench is used to measure the overhead in accessing main memory while d-bench is meant for measuring the overhead in accessing the SD card. It is evident as the overhead experienced by benchmarks stressing the CPU and memory is almost negligible while I/O devices virtualization shows to have a significant impact. This is because, when I/O devices virtualization is required, optimized device drivers are necessary in order to efficiently route multiple requests coming by concurrently running 62

5 On the Evaluation of the Performance Overhead of a Commercial Embedded Hypervisor 5 Table 1 Embedded hypervisor overhead Target Feature Overhead I/O bound Execution time [s] 35.03% CPU bound Execution-time [s] 9.44% Storage Bandwidth [MB/s] 33.33% Cache/Memory Bandwidth [MB/s] 4.99% VMs. On the contrary, the negative overhead experienced by the benchmark stressing the memory subsystem is due to optimizations happening at the memory interface between the hypervisor and physical memory. Another explanation, for the difference between CPU and I/O devices virtualization performances, lies in the observation that CPU intensive workloads are mainly represented by unprivileged instructions which run natively. Unlike CPU intensive workloads, I/O devices virtualziation requires interrupt virtualization. This is already known to introduce additional latency due to the overhead required for the interrupt to be routed to the right virtual machine. Modern operating systems, moreover, are known to frequently mask and unmask interrupts and this could be the reason for worst performances because of higher latencies experienced by the interrupt handling mechanism. 5 Conclusions Embedded hypervisors are gaining wide acceptance as long as safety and mission critical embedded systems are concerned. For this reason evaluating their performance, in terms of overhead together with their reliability is a key step in the process of evaluating the effectiveness of a hypervisor-based solution for modern embedded systems targeting markets such as the automotive one. Experimental results show that something is still missing as long as performances and overhead in I/O devices virtualization are considered. Currently, paravirtualization is the technology of choice for embedded hypervisors, so such performance penalties for I/O devices virtualization are expected as long as no hardware support is available by the processor. Comparing the overhead experienced by the different subsystems it is clear, indeed, that I/O devices virtualization plays a key role. This means that hardware support and optimized hypervisor level device drivers are necessary, in order to improve performances in scheduling I/O requests and interrupt management. When it comes to CPU and memory virtualization, the overhead is very low with performances very close to that of the native setup. This penalty, anyway, is the price to pay to enable the benefits of virtualization on embedded systems. Embedded virtualization is going to be supported more and more by silicon vendors, which are providing increasingly powerful platforms and adding hardware support for virtualization. The isolation feature provided by modern embedded hypervisors is going to play an important role to enable secure and safe partitioning of resources. This will enable certification of selected pieces of the system so to fulfill the requirements imposed by safety regulation required by modern embedded systems. References 1. J. Fornaeus (2010) Device Hypervisors, Design Automation Conference, pp G. J. Popek, R. P. Goldberg (1974) Formal Requirements for Virtualizable Third Generation Architectures. Communications of the ACM, vol. 17 issue 7, pp A. Masrur, S. Drossler, T. Pfeuffer, S. Chakraborty (2010) VM-Based Real-Time Services for Automotive Control Applications. Embedded and Real-Time Computing Systems and Applications, pp P. Yu, M. Xia, Q. Lin, M. Zhu, S. Gao (2010) Real-time Enhancements for Xen hypervisor. Embedded and Ubiquitous Computing, pp Steven H. VanderLeest (2010) Arinc 653 Hypervisor. Digital Avionic Systems Conference, pp.5.e e Daniel Baldin, Timo Kerstan (2009) Proteus, a Hybrid Virtualization Platform for Embedded Systems. Analysis Architectures and Modeling of Embedded Systems, Volume 310/2009, pp Freescale, 8. Phoronix Test Suite, 63

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