{"title":"Proceedings of the 23rd ACM Symposium on Operating Systems Principles 2011, SOSP 2011, Cascais, Portugal, October 23-26, 2011","authors":"","doi":"10.1145/2043556","DOIUrl":"https://doi.org/10.1145/2043556","url":null,"abstract":"","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88861910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reproducing bugs is hard. Deterministic replay systems address this problem by providing a high-fidelity replica of an original program run that can be repeatedly executed to zero-in on bugs. Unfortunately, existing replay systems for multiprocessor programs fall short. These systems either incur high overheads, rely on non-standard multiprocessor hardware, or fail to reliably reproduce executions. Their primary stumbling block is data races -- a source of nondeterminism that must be captured if executions are to be faithfully reproduced. In this paper, we present ODR--a software-only replay system that reproduces bugs and provides low-overhead multiprocessor recording. The key observation behind ODR is that, for debugging purposes, a replay system does not need to generate a high-fidelity replica of the original execution. Instead, it suffices to produce any execution that exhibits the same outputs as the original. Guided by this observation, ODR relaxes its fidelity guarantees to avoid the problem of reproducing data-races altogether. The result is a system that replays real multiprocessor applications, such as Apache, MySQL, and the Java Virtual Machine, and provides low record-mode overhead.
{"title":"ODR: output-deterministic replay for multicore debugging","authors":"Gautam Altekar, I. Stoica","doi":"10.1145/1629575.1629594","DOIUrl":"https://doi.org/10.1145/1629575.1629594","url":null,"abstract":"Reproducing bugs is hard. Deterministic replay systems address this problem by providing a high-fidelity replica of an original program run that can be repeatedly executed to zero-in on bugs. Unfortunately, existing replay systems for multiprocessor programs fall short. These systems either incur high overheads, rely on non-standard multiprocessor hardware, or fail to reliably reproduce executions. Their primary stumbling block is data races -- a source of nondeterminism that must be captured if executions are to be faithfully reproduced.\u0000 In this paper, we present ODR--a software-only replay system that reproduces bugs and provides low-overhead multiprocessor recording. The key observation behind ODR is that, for debugging purposes, a replay system does not need to generate a high-fidelity replica of the original execution. Instead, it suffices to produce any execution that exhibits the same outputs as the original. Guided by this observation, ODR relaxes its fidelity guarantees to avoid the problem of reproducing data-races altogether. The result is a system that replays real multiprocessor applications, such as Apache, MySQL, and the Java Virtual Machine, and provides low record-mode overhead.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83762075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Castro, Manuel Costa, Jean-Phillipe Martin, Marcus Peinado, P. Akritidis, Austin Donnelly, P. Barham, Richard Black
Bugs in kernel extensions remain one of the main causes of poor operating system reliability despite proposed techniques that isolate extensions in separate protection domains to contain faults. We believe that previous fault isolation techniques are not widely used because they cannot isolate existing kernel extensions with low overhead on standard hardware. This is a hard problem because these extensions communicate with the kernel using a complex interface and they communicate frequently. We present BGI (Byte-Granularity Isolation), a new software fault isolation technique that addresses this problem. BGI uses efficient byte-granularity memory protection to isolate kernel extensions in separate protection domains that share the same address space. BGI ensures type safety for kernel objects and it can detect common types of errors inside domains. Our results show that BGI is practical: it can isolate Windows drivers without requiring changes to the source code and it introduces a CPU overhead between 0 and 16%. BGI can also find bugs during driver testing. We found 28 new bugs in widely used Windows drivers.
{"title":"Fast byte-granularity software fault isolation","authors":"M. Castro, Manuel Costa, Jean-Phillipe Martin, Marcus Peinado, P. Akritidis, Austin Donnelly, P. Barham, Richard Black","doi":"10.1145/1629575.1629581","DOIUrl":"https://doi.org/10.1145/1629575.1629581","url":null,"abstract":"Bugs in kernel extensions remain one of the main causes of poor operating system reliability despite proposed techniques that isolate extensions in separate protection domains to contain faults. We believe that previous fault isolation techniques are not widely used because they cannot isolate existing kernel extensions with low overhead on standard hardware. This is a hard problem because these extensions communicate with the kernel using a complex interface and they communicate frequently. We present BGI (Byte-Granularity Isolation), a new software fault isolation technique that addresses this problem. BGI uses efficient byte-granularity memory protection to isolate kernel extensions in separate protection domains that share the same address space. BGI ensures type safety for kernel objects and it can detect common types of errors inside domains. Our results show that BGI is practical: it can isolate Windows drivers without requiring changes to the source code and it introduces a CPU overhead between 0 and 16%. BGI can also find bugs during driver testing. We found 28 new bugs in widely used Windows drivers.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89474537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Xu, Ling Huang, A. Fox, D. Patterson, Michael I. Jordan
Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals.
{"title":"Detecting large-scale system problems by mining console logs","authors":"W. Xu, Ling Huang, A. Fox, D. Patterson, Michael I. Jordan","doi":"10.1145/1629575.1629587","DOIUrl":"https://doi.org/10.1145/1629575.1629587","url":null,"abstract":"Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88669254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeremy Condit, Edmund B. Nightingale, Christopher Frost, Engin Ipek, Benjamin C. Lee, D. Burger, Derrick Coetzee
Modern computer systems have been built around the assumption that persistent storage is accessed via a slow, block-based interface. However, new byte-addressable, persistent memory technologies such as phase change memory (PCM) offer fast, fine-grained access to persistent storage. In this paper, we present a file system and a hardware architecture that are designed around the properties of persistent, byteaddressable memory. Our file system, BPFS, uses a new technique called short-circuit shadow paging to provide atomic, fine-grained updates to persistent storage. As a result, BPFS provides strong reliability guarantees and offers better performance than traditional file systems, even when both are run on top of byte-addressable, persistent memory. Our hardware architecture enforces atomicity and ordering guarantees required by BPFS while still providing the performance benefits of the L1 and L2 caches. Since these memory technologies are not yet widely available, we evaluate BPFS on DRAM against NTFS on both a RAM disk and a traditional disk. Then, we use microarchitectural simulations to estimate the performance of BPFS on PCM. Despite providing strong safety and consistency guarantees, BPFS on DRAM is typically twice as fast as NTFS on a RAM disk and 4-10 times faster than NTFS on disk. We also show that BPFS on PCM should be significantly faster than a traditional disk-based file system.
{"title":"Better I/O through byte-addressable, persistent memory","authors":"Jeremy Condit, Edmund B. Nightingale, Christopher Frost, Engin Ipek, Benjamin C. Lee, D. Burger, Derrick Coetzee","doi":"10.1145/1629575.1629589","DOIUrl":"https://doi.org/10.1145/1629575.1629589","url":null,"abstract":"Modern computer systems have been built around the assumption that persistent storage is accessed via a slow, block-based interface. However, new byte-addressable, persistent memory technologies such as phase change memory (PCM) offer fast, fine-grained access to persistent storage.\u0000 In this paper, we present a file system and a hardware architecture that are designed around the properties of persistent, byteaddressable memory. Our file system, BPFS, uses a new technique called short-circuit shadow paging to provide atomic, fine-grained updates to persistent storage. As a result, BPFS provides strong reliability guarantees and offers better performance than traditional file systems, even when both are run on top of byte-addressable, persistent memory. Our hardware architecture enforces atomicity and ordering guarantees required by BPFS while still providing the performance benefits of the L1 and L2 caches.\u0000 Since these memory technologies are not yet widely available, we evaluate BPFS on DRAM against NTFS on both a RAM disk and a traditional disk. Then, we use microarchitectural simulations to estimate the performance of BPFS on PCM. Despite providing strong safety and consistency guarantees, BPFS on DRAM is typically twice as fast as NTFS on a RAM disk and 4-10 times faster than NTFS on disk. We also show that BPFS on PCM should be significantly faster than a traditional disk-based file system.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77630611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Databases have achieved orders-of-magnitude performance improvements by changing the layout of stored data -- for instance, by arranging data in columns or compressing it before storage. These improvements have been implemented in monolithic new engines, however, making it difficult to experiment with feature combinations or extensions. We present Anvil, a modular and extensible toolkit for building database back ends. Anvil's storage modules, called dTables, have much finer granularity than prior work. For example, some dTables specialize in writing data, while others provide optimized read-only formats. This specialization makes both kinds of dTable simple to write and understand. Unifying dTables implement more comprehensive functionality by layering over other dTables -- for instance, building a read/write store from read-only tables and a writable journal, or building a general-purpose store from optimized special-purpose stores. The dTable design leads to a flexible system powerful enough to implement many database storage layouts. Our prototype implementation of Anvil performs up to 5.5 times faster than an existing B-tree-based database back end on conventional workloads, and can easily be customized for further gains on specific data and workloads.
{"title":"Modular data storage with Anvil","authors":"Mike Mammarella, Shant Hovsepian, E. Kohler","doi":"10.1145/1629575.1629590","DOIUrl":"https://doi.org/10.1145/1629575.1629590","url":null,"abstract":"Databases have achieved orders-of-magnitude performance improvements by changing the layout of stored data -- for instance, by arranging data in columns or compressing it before storage. These improvements have been implemented in monolithic new engines, however, making it difficult to experiment with feature combinations or extensions. We present Anvil, a modular and extensible toolkit for building database back ends. Anvil's storage modules, called dTables, have much finer granularity than prior work. For example, some dTables specialize in writing data, while others provide optimized read-only formats. This specialization makes both kinds of dTable simple to write and understand. Unifying dTables implement more comprehensive functionality by layering over other dTables -- for instance, building a read/write store from read-only tables and a writable journal, or building a general-purpose store from optimized special-purpose stores. The dTable design leads to a flexible system powerful enough to implement many database storage layouts. Our prototype implementation of Anvil performs up to 5.5 times faster than an existing B-tree-based database back end on conventional workloads, and can easily be customized for further gains on specific data and workloads.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89434027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Resin is a new language runtime that helps prevent security vulnerabilities, by allowing programmers to specify application-level data flow assertions. Resin provides policy objects, which programmers use to specify assertion code and metadata; data tracking, which allows programmers to associate assertions with application data, and to keep track of assertions as the data flow through the application; and filter objects, which programmers use to define data flow boundaries at which assertions are checked. Resin's runtime checks data flow assertions by propagating policy objects along with data, as that data moves through the application, and then invoking filter objects when data crosses a data flow boundary, such as when writing data to the network or a file. Using Resin, Web application programmers can prevent a range of problems, from SQL injection and cross-site scripting, to inadvertent password disclosure and missing access control checks. Adding a Resin assertion to an application requires few changes to the existing application code, and an assertion can reuse existing code and data structures. For instance, 23 lines of code detect and prevent three previously-unknown missing access control vulnerabilities in phpBB, a popular Web forum application. Other assertions comprising tens of lines of code prevent a range of vulnerabilities in Python and PHP applications. A prototype of Resin incurs a 33% CPU overhead running the HotCRP conference management application.
{"title":"Improving application security with data flow assertions","authors":"A. Yip, Xi Wang, N. Zeldovich, M. Kaashoek","doi":"10.1145/1629575.1629604","DOIUrl":"https://doi.org/10.1145/1629575.1629604","url":null,"abstract":"Resin is a new language runtime that helps prevent security vulnerabilities, by allowing programmers to specify application-level data flow assertions. Resin provides policy objects, which programmers use to specify assertion code and metadata; data tracking, which allows programmers to associate assertions with application data, and to keep track of assertions as the data flow through the application; and filter objects, which programmers use to define data flow boundaries at which assertions are checked. Resin's runtime checks data flow assertions by propagating policy objects along with data, as that data moves through the application, and then invoking filter objects when data crosses a data flow boundary, such as when writing data to the network or a file.\u0000 Using Resin, Web application programmers can prevent a range of problems, from SQL injection and cross-site scripting, to inadvertent password disclosure and missing access control checks. Adding a Resin assertion to an application requires few changes to the existing application code, and an assertion can reuse existing code and data structures. For instance, 23 lines of code detect and prevent three previously-unknown missing access control vulnerabilities in phpBB, a popular Web forum application. Other assertions comprising tens of lines of code prevent a range of vulnerabilities in Python and PHP applications. A prototype of Resin incurs a 33% CPU overhead running the HotCRP conference management application.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80730510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Perkins, Sunghun Kim, S. Larsen, Saman P. Amarasinghe, J. Bachrach, Michael Carbin, Carlos Pacheco, F. Sherwood, Stelios Sidiroglou, Greg Sullivan, W. Wong, Yoav Zibin, Michael D. Ernst, M. Rinard
We present ClearView, a system for automatically patching errors in deployed software. ClearView works on stripped Windows x86 binaries without any need for source code, debugging information, or other external information, and without human intervention. ClearView (1) observes normal executions to learn invariants thatcharacterize the application's normal behavior, (2) uses error detectors to distinguish normal executions from erroneous executions, (3) identifies violations of learned invariants that occur during erroneous executions, (4) generates candidate repair patches that enforce selected invariants by changing the state or flow of control to make the invariant true, and (5) observes the continued execution of patched applications to select the most successful patch. ClearView is designed to correct errors in software with high availability requirements. Aspects of ClearView that make it particularly appropriate for this context include its ability to generate patches without human intervention, apply and remove patchesto and from running applications without requiring restarts or otherwise perturbing the execution, and identify and discard ineffective or damaging patches by evaluating the continued behavior of patched applications. ClearView was evaluated in a Red Team exercise designed to test its ability to successfully survive attacks that exploit security vulnerabilities. A hostile external Red Team developed ten code injection exploits and used these exploits to repeatedly attack an application protected by ClearView. ClearView detected and blocked all of the attacks. For seven of the ten exploits, ClearView automatically generated patches that corrected the error, enabling the application to survive the attacks and continue on to successfully process subsequent inputs. Finally, the Red Team attempted to make Clear-View apply an undesirable patch, but ClearView's patch evaluation mechanism enabled ClearView to identify and discard both ineffective patches and damaging patches.
{"title":"Automatically patching errors in deployed software","authors":"J. Perkins, Sunghun Kim, S. Larsen, Saman P. Amarasinghe, J. Bachrach, Michael Carbin, Carlos Pacheco, F. Sherwood, Stelios Sidiroglou, Greg Sullivan, W. Wong, Yoav Zibin, Michael D. Ernst, M. Rinard","doi":"10.1145/1629575.1629585","DOIUrl":"https://doi.org/10.1145/1629575.1629585","url":null,"abstract":"We present ClearView, a system for automatically patching errors in deployed software. ClearView works on stripped Windows x86 binaries without any need for source code, debugging information, or other external information, and without human intervention.\u0000 ClearView (1) observes normal executions to learn invariants thatcharacterize the application's normal behavior, (2) uses error detectors to distinguish normal executions from erroneous executions, (3) identifies violations of learned invariants that occur during erroneous executions, (4) generates candidate repair patches that enforce selected invariants by changing the state or flow of control to make the invariant true, and (5) observes the continued execution of patched applications to select the most successful patch.\u0000 ClearView is designed to correct errors in software with high availability requirements. Aspects of ClearView that make it particularly appropriate for this context include its ability to generate patches without human intervention, apply and remove patchesto and from running applications without requiring restarts or otherwise perturbing the execution, and identify and discard ineffective or damaging patches by evaluating the continued behavior of patched applications.\u0000 ClearView was evaluated in a Red Team exercise designed to test its ability to successfully survive attacks that exploit security vulnerabilities. A hostile external Red Team developed ten code injection exploits and used these exploits to repeatedly attack an application protected by ClearView. ClearView detected and blocked all of the attacks. For seven of the ten exploits, ClearView automatically generated patches that corrected the error, enabling the application to survive the attacks and continue on to successfully process subsequent inputs. Finally, the Red Team attempted to make Clear-View apply an undesirable patch, but ClearView's patch evaluation mechanism enabled ClearView to identify and discard both ineffective patches and damaging patches.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84303586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Klein, Kevin Elphinstone, G. Heiser, June Andronick, David A. Cock, Philip Derrin, D. Elkaduwe, Kai Engelhardt, Rafal Kolanski, Michael Norrish, Thomas Sewell, Harvey Tuch, Simon Winwood
Complete formal verification is the only known way to guarantee that a system is free of programming errors. We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation. seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.
{"title":"seL4: formal verification of an OS kernel","authors":"G. Klein, Kevin Elphinstone, G. Heiser, June Andronick, David A. Cock, Philip Derrin, D. Elkaduwe, Kai Engelhardt, Rafal Kolanski, Michael Norrish, Thomas Sewell, Harvey Tuch, Simon Winwood","doi":"10.1145/1629575.1629596","DOIUrl":"https://doi.org/10.1145/1629575.1629596","url":null,"abstract":"Complete formal verification is the only known way to guarantee that a system is free of programming errors.\u0000 We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation.\u0000 seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82439317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kirk Glerum, Kinshuman Kinshumann, Steve Greenberg, Gabriel Aul, Vince Orgovan, Greg Nichols, David Grant, Gretchen Loihle, G. Hunt
Windows Error Reporting (WER) is a distributed system that automates the processing of error reports coming from an installed base of a billion machines. WER has collected billions of error reports in ten years of operation. It collects error data automatically and classifies errors into buckets, which are used to prioritize developer effort and report fixes to users. WER uses a progressive approach to data collection, which minimizes overhead for most reports yet allows developers to collect detailed information when needed. WER takes advantage of its scale to use error statistics as a tool in debugging; this allows developers to isolate bugs that could not be found at smaller scale. WER has been designed for large scale: one pair of database servers can record all the errors that occur on all Windows computers worldwide.
{"title":"Debugging in the (very) large: ten years of implementation and experience","authors":"Kirk Glerum, Kinshuman Kinshumann, Steve Greenberg, Gabriel Aul, Vince Orgovan, Greg Nichols, David Grant, Gretchen Loihle, G. Hunt","doi":"10.1145/1629575.1629586","DOIUrl":"https://doi.org/10.1145/1629575.1629586","url":null,"abstract":"Windows Error Reporting (WER) is a distributed system that automates the processing of error reports coming from an installed base of a billion machines. WER has collected billions of error reports in ten years of operation. It collects error data automatically and classifies errors into buckets, which are used to prioritize developer effort and report fixes to users. WER uses a progressive approach to data collection, which minimizes overhead for most reports yet allows developers to collect detailed information when needed. WER takes advantage of its scale to use error statistics as a tool in debugging; this allows developers to isolate bugs that could not be found at smaller scale. WER has been designed for large scale: one pair of database servers can record all the errors that occur on all Windows computers worldwide.","PeriodicalId":20672,"journal":{"name":"Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89512954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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