Jacob R. Lorch, Yixuan Chen, Manos Kapritsos, Bryan Parno, S. Qadeer, Upamanyu Sharma, James R. Wilcox, Xueyuan Zhao
Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armada gives developers the flexibility to choose arbitrary memory layout and synchronization primitives so they are never constrained in their pursuit of performance. To reduce developer effort, Armada leverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and alias analysis. All these techniques are proven sound, and Armada can be soundly extended with additional strategies over time. Using Armada, we verify four concurrent case studies and show that we can achieve performance equivalent to that of unverified code.
{"title":"Armada: low-effort verification of high-performance concurrent programs","authors":"Jacob R. Lorch, Yixuan Chen, Manos Kapritsos, Bryan Parno, S. Qadeer, Upamanyu Sharma, James R. Wilcox, Xueyuan Zhao","doi":"10.1145/3385412.3385971","DOIUrl":"https://doi.org/10.1145/3385412.3385971","url":null,"abstract":"Safely writing high-performance concurrent programs is notoriously difficult. To aid developers, we introduce Armada, a language and tool designed to formally verify such programs with relatively little effort. Via a C-like language and a small-step, state-machine-based semantics, Armada gives developers the flexibility to choose arbitrary memory layout and synchronization primitives so they are never constrained in their pursuit of performance. To reduce developer effort, Armada leverages SMT-powered automation and a library of powerful reasoning techniques, including rely-guarantee, TSO elimination, reduction, and alias analysis. All these techniques are proven sound, and Armada can be soundly extended with additional strategies over time. Using Armada, we verify four concurrent case studies and show that we can achieve performance equivalent to that of unverified code.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81087274","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}
Fraser Brown, John Renner, Andres Nötzli, Sorin Lerner, H. Shacham, D. Stefan
We present VeRA, a system for verifying the range analysis pass in browser just-in-time (JIT) compilers. Browser developers write range analysis routines in a subset of C++, and verification developers write infrastructure to verify custom analysis properties. Then, VeRA automatically verifies the range analysis routines, which browser developers can integrate directly into the JIT. We use VeRA to translate and verify Firefox range analysis routines, and it detects a new, confirmed bug that has existed in the browser for six years.
{"title":"Towards a verified range analysis for JavaScript JITs","authors":"Fraser Brown, John Renner, Andres Nötzli, Sorin Lerner, H. Shacham, D. Stefan","doi":"10.1145/3385412.3385968","DOIUrl":"https://doi.org/10.1145/3385412.3385968","url":null,"abstract":"We present VeRA, a system for verifying the range analysis pass in browser just-in-time (JIT) compilers. Browser developers write range analysis routines in a subset of C++, and verification developers write infrastructure to verify custom analysis properties. Then, VeRA automatically verifies the range analysis routines, which browser developers can integrate directly into the JIT. We use VeRA to translate and verify Firefox range analysis routines, and it detects a new, confirmed bug that has existed in the browser for six years.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83487543","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}
Kangjing Huang, Xiaokang Qiu, Peiyuan Shen, Yanjun Wang
Syntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability. In this paper, we propose a cooperative synthesis technique for SyGuS problems with the conditional linear integer arithmetic (CLIA) background theory, as a novel integration of the two approaches, combining the best of the two worlds. The technique exploits several novel divide-and-conquer strategies to split a large synthesis problem to smaller subproblems. The subproblems are solved separately and their solutions are combined to form a final solution. The technique integrates two synthesis engines: a pure deductive component that can efficiently solve some problems, and a height-based enumeration algorithm that can handle arbitrary grammar. We implemented the cooperative synthesis technique, and evaluated it on a wide range of benchmarks. Experiments showed that our technique can solve many challenging synthesis problems not possible before, and tends to be more scalable than state-of-the-art synthesis algorithms.
{"title":"Reconciling enumerative and deductive program synthesis","authors":"Kangjing Huang, Xiaokang Qiu, Peiyuan Shen, Yanjun Wang","doi":"10.1145/3385412.3386027","DOIUrl":"https://doi.org/10.1145/3385412.3386027","url":null,"abstract":"Syntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability. In this paper, we propose a cooperative synthesis technique for SyGuS problems with the conditional linear integer arithmetic (CLIA) background theory, as a novel integration of the two approaches, combining the best of the two worlds. The technique exploits several novel divide-and-conquer strategies to split a large synthesis problem to smaller subproblems. The subproblems are solved separately and their solutions are combined to form a final solution. The technique integrates two synthesis engines: a pure deductive component that can efficiently solve some problems, and a height-based enumeration algorithm that can handle arbitrary grammar. We implemented the cooperative synthesis technique, and evaluated it on a wide range of benchmarks. Experiments showed that our technique can solve many challenging synthesis problems not possible before, and tends to be more scalable than state-of-the-art synthesis algorithms.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75707729","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}
A. Antoniadis, Nikos Filippakis, Paddy Krishnan, R. Ramesh, N. Allen, Y. Smaragdakis
Enterprise applications are a major success domain of Java, and Java is the default setting for much modern static analysis research. It would stand to reason that high-quality static analysis of Java enterprise applications would be commonplace, but this is far from true. Major analysis frameworks feature virtually no support for enterprise applications and offer analyses that are woefully incomplete and vastly imprecise, when at all scalable. In this work, we present two techniques for drastically enhancing the completeness and precision of static analysis for Java enterprise applications. The first technique identifies domain-specific concepts underlying all enterprise application frameworks, captures them in an extensible, declarative form, and achieves modeling of components and entry points in a largely framework-independent way. The second technique offers precision and scalability via a sound-modulo-analysis modeling of standard data structures. In realistic enterprise applications (an order of magnitude larger than prior benchmarks in the literature) our techniques achieve high degrees of completeness (on average more than 4x higher than conventional techniques) and speedups of about 6x compared to the most precise conventional analysis, with higher precision on multiple metrics. The result is JackEE, an enterprise analysis framework that can offer precise, high-completeness static modeling of realistic enterprise applications.
{"title":"Static analysis of Java enterprise applications: frameworks and caches, the elephants in the room","authors":"A. Antoniadis, Nikos Filippakis, Paddy Krishnan, R. Ramesh, N. Allen, Y. Smaragdakis","doi":"10.1145/3385412.3386026","DOIUrl":"https://doi.org/10.1145/3385412.3386026","url":null,"abstract":"Enterprise applications are a major success domain of Java, and Java is the default setting for much modern static analysis research. It would stand to reason that high-quality static analysis of Java enterprise applications would be commonplace, but this is far from true. Major analysis frameworks feature virtually no support for enterprise applications and offer analyses that are woefully incomplete and vastly imprecise, when at all scalable. In this work, we present two techniques for drastically enhancing the completeness and precision of static analysis for Java enterprise applications. The first technique identifies domain-specific concepts underlying all enterprise application frameworks, captures them in an extensible, declarative form, and achieves modeling of components and entry points in a largely framework-independent way. The second technique offers precision and scalability via a sound-modulo-analysis modeling of standard data structures. In realistic enterprise applications (an order of magnitude larger than prior benchmarks in the literature) our techniques achieve high degrees of completeness (on average more than 4x higher than conventional techniques) and speedups of about 6x compared to the most precise conventional analysis, with higher precision on multiple metrics. The result is JackEE, an enterprise analysis framework that can offer precise, high-completeness static modeling of realistic enterprise applications.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72571412","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}
In this paper, we present an efficient, functional, and formally verified parsing algorithm for LL(1) context-free expressions based on the concept of derivatives of formal languages. Parsing with derivatives is an elegant parsing technique, which, in the general case, suffers from cubic worst-case time complexity and slow performance in practice. We specialise the parsing with derivatives algorithm to LL(1) context-free expressions, where alternatives can be chosen given a single token of lookahead. We formalise the notion of LL(1) expressions and show how to efficiently check the LL(1) property. Next, we present a novel linear-time parsing with derivatives algorithm for LL(1) expressions operating on a zipper-inspired data structure. We prove the algorithm correct in Coq and present an implementation as a part of Scallion, a parser combinators framework in Scala with enumeration and pretty printing capabilities.
{"title":"Zippy LL(1) parsing with derivatives","authors":"Romain Edelmann, Jad Hamza, Viktor Kunčak","doi":"10.1145/3385412.3385992","DOIUrl":"https://doi.org/10.1145/3385412.3385992","url":null,"abstract":"In this paper, we present an efficient, functional, and formally verified parsing algorithm for LL(1) context-free expressions based on the concept of derivatives of formal languages. Parsing with derivatives is an elegant parsing technique, which, in the general case, suffers from cubic worst-case time complexity and slow performance in practice. We specialise the parsing with derivatives algorithm to LL(1) context-free expressions, where alternatives can be chosen given a single token of lookahead. We formalise the notion of LL(1) expressions and show how to efficiently check the LL(1) property. Next, we present a novel linear-time parsing with derivatives algorithm for LL(1) expressions operating on a zipper-inspired data structure. We prove the algorithm correct in Coq and present an implementation as a part of Scallion, a parser combinators framework in Scala with enumeration and pretty printing capabilities.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90913194","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}
We present a novel parsing algorithm for all context-free languages. The algorithm features a clean mathematical formulation: parsing is expressed as a series of standard operations on regular languages and relations. Parsing complexity w.r.t. input length matches the state of the art: it is worst-case cubic, quadratic for unambiguous grammars, and linear for LR-regular grammars. What distinguishes our approach is that parsing can be implemented using only immutable, acyclic data structures. We also propose a parsing optimization technique called context-free memoization. It allows handling an overwhelming majority of input symbols using a simple stack and a lookup table, similarly to the operation of a deterministic LR(1) parser. This allows our proof-of-concept implementation to outperform the best current implementations of common generalized parsing algorithms (Earley, GLR, and GLL). Tested on a large Java source corpus, parsing is 3–5 times faster, while recognition—35 times faster.
{"title":"Faster general parsing through context-free memoization","authors":"G. Herman","doi":"10.1145/3385412.3386032","DOIUrl":"https://doi.org/10.1145/3385412.3386032","url":null,"abstract":"We present a novel parsing algorithm for all context-free languages. The algorithm features a clean mathematical formulation: parsing is expressed as a series of standard operations on regular languages and relations. Parsing complexity w.r.t. input length matches the state of the art: it is worst-case cubic, quadratic for unambiguous grammars, and linear for LR-regular grammars. What distinguishes our approach is that parsing can be implemented using only immutable, acyclic data structures. We also propose a parsing optimization technique called context-free memoization. It allows handling an overwhelming majority of input symbols using a simple stack and a lookup table, similarly to the operation of a deterministic LR(1) parser. This allows our proof-of-concept implementation to outperform the best current implementations of common generalized parsing algorithms (Earley, GLR, and GLL). Tested on a large Java source corpus, parsing is 3–5 times faster, while recognition—35 times faster.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77714692","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}
Hongjune Kim, Jianping Zeng, Qingrui Liu, Mohammad Abdel-Majeed, Jaejin Lee, Changhee Jung
This paper presents Penny, a compiler-directed resilience scheme for protecting GPU register files (RF) against soft errors. Penny replaces the conventional error correction code (ECC) based RF protection by using less expensive error detection code (EDC) along with idempotence based recovery. Compared to the ECC protection, Penny can achieve either the same level of RF resilience yet with significantly lower hardware costs or stronger resilience using the same ECC due to its ability to detect multi-bit errors when it is used solely for detection. In particular, to address the lack of store buffers in GPUs, which causes both checkpoint storage overwriting and the high cost of checkpointing stores, Penny provides several compiler optimizations such as storage coloring and checkpoint pruning. Across 25 benchmarks, Penny causes only ≈3% run-time overhead on average.
{"title":"Compiler-directed soft error resilience for lightweight GPU register file protection","authors":"Hongjune Kim, Jianping Zeng, Qingrui Liu, Mohammad Abdel-Majeed, Jaejin Lee, Changhee Jung","doi":"10.1145/3385412.3386033","DOIUrl":"https://doi.org/10.1145/3385412.3386033","url":null,"abstract":"This paper presents Penny, a compiler-directed resilience scheme for protecting GPU register files (RF) against soft errors. Penny replaces the conventional error correction code (ECC) based RF protection by using less expensive error detection code (EDC) along with idempotence based recovery. Compared to the ECC protection, Penny can achieve either the same level of RF resilience yet with significantly lower hardware costs or stronger resilience using the same ECC due to its ability to detect multi-bit errors when it is used solely for detection. In particular, to address the lack of store buffers in GPUs, which causes both checkpoint storage overwriting and the high cost of checkpointing stores, Penny provides several compiler optimizations such as storage coloring and checkpoint pruning. Across 25 benchmarks, Penny causes only ≈3% run-time overhead on average.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90498558","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}
Sandeep Dasgupta, S. Dinesh, Deepan Venkatesh, Vikram S. Adve, Christopher W. Fletcher
Validating the correctness of binary lifters is pivotal to gain trust in binary analysis, especially when used in scenarios where correctness is important. Existing approaches focus on validating the correctness of lifting instructions or basic blocks in isolation and do not scale to full programs. In this work, we show that formal translation validation of single instructions for a complex ISA like x86-64 is not only practical, but can be used as a building block for scalable full-program validation. Our work is the first to do translation validation of single instructions on an architecture as extensive as x86-64, uses the most precise formal semantics available, and has the widest coverage in terms of the number of instructions tested for correctness. Next, we develop a novel technique that uses validated instructions to enable program-level validation, without resorting to performance-heavy semantic equivalence checking. Specifically, we compose the validated IR sequences using a tool we develop called Compositional Lifter to create a reference standard. The semantic equivalence check between the reference and the lifter output is then reduced to a graph-isomorphism check through the use of semantic preserving transformations. The translation validation of instructions in isolation revealed 29 new bugs in McSema – a mature open-source lifter from x86-64 to LLVM IR. Towards the validation of full programs, our approach was able to prove the translational correctness of 2254/2348 functions taken from LLVM’s single-source benchmark test-suite.
{"title":"Scalable validation of binary lifters","authors":"Sandeep Dasgupta, S. Dinesh, Deepan Venkatesh, Vikram S. Adve, Christopher W. Fletcher","doi":"10.1145/3385412.3385964","DOIUrl":"https://doi.org/10.1145/3385412.3385964","url":null,"abstract":"Validating the correctness of binary lifters is pivotal to gain trust in binary analysis, especially when used in scenarios where correctness is important. Existing approaches focus on validating the correctness of lifting instructions or basic blocks in isolation and do not scale to full programs. In this work, we show that formal translation validation of single instructions for a complex ISA like x86-64 is not only practical, but can be used as a building block for scalable full-program validation. Our work is the first to do translation validation of single instructions on an architecture as extensive as x86-64, uses the most precise formal semantics available, and has the widest coverage in terms of the number of instructions tested for correctness. Next, we develop a novel technique that uses validated instructions to enable program-level validation, without resorting to performance-heavy semantic equivalence checking. Specifically, we compose the validated IR sequences using a tool we develop called Compositional Lifter to create a reference standard. The semantic equivalence check between the reference and the lifter output is then reduced to a graph-isomorphism check through the use of semantic preserving transformations. The translation validation of instructions in isolation revealed 29 new bugs in McSema – a mature open-source lifter from x86-64 to LLVM IR. Towards the validation of full programs, our approach was able to prove the translational correctness of 2254/2348 functions taken from LLVM’s single-source benchmark test-suite.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88454020","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}
Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, and, as a result, they do not capture deep, precise program semantics. On the other hand, models learned from runtime information critically depend on the quality of program executions, thus leading to trained models with highly variant quality. This paper tackles these inherent weaknesses of prior approaches by introducing a new deep neural network, Liger, which learns program representations from a mixture of symbolic and concrete execution traces. We have evaluated Liger on two tasks: method name prediction and semantics classification. Results show that Liger is significantly more accurate than the state-of-the-art static model code2seq in predicting method names, and requires on average around 10x fewer executions covering nearly 4x fewer paths than the state-of-the-art dynamic model DYPRO in both tasks. Liger offers a new, interesting design point in the space of neural program embeddings and opens up this new direction for exploration.
{"title":"Blended, precise semantic program embeddings","authors":"Ke Wang, Z. Su","doi":"10.1145/3385412.3385999","DOIUrl":"https://doi.org/10.1145/3385412.3385999","url":null,"abstract":"Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, and, as a result, they do not capture deep, precise program semantics. On the other hand, models learned from runtime information critically depend on the quality of program executions, thus leading to trained models with highly variant quality. This paper tackles these inherent weaknesses of prior approaches by introducing a new deep neural network, Liger, which learns program representations from a mixture of symbolic and concrete execution traces. We have evaluated Liger on two tasks: method name prediction and semantics classification. Results show that Liger is significantly more accurate than the state-of-the-art static model code2seq in predicting method names, and requires on average around 10x fewer executions covering nearly 4x fewer paths than the state-of-the-art dynamic model DYPRO in both tasks. Liger offers a new, interesting design point in the space of neural program embeddings and opens up this new direction for exploration.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74587738","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}
One of the major challenges faced by network operators pertains to whether their network can meet input traffic demand, avoid overload, and satisfy service-level agreements. Automatically verifying if no network links are overloaded is complicated---requires modeling frequent network failures, complex routing and load-balancing technologies, and evolving traffic requirements. We present QARC, a distributed control plane abstraction that can automatically verify whether a control plane may cause link-load violations under failures. QARC is fully automatic and can help operators program networks that are more resilient to failures and upgrade the network to avoid violations. We apply QARC to real datacenter and ISP networks and find interesting cases of load violations. QARC can detect violations in under an hour.
{"title":"Detecting network load violations for distributed control planes","authors":"Kausik Subramanian, Anubhavnidhi Abhashkumar, Loris D'antoni, Aditya Akella","doi":"10.1145/3385412.3385976","DOIUrl":"https://doi.org/10.1145/3385412.3385976","url":null,"abstract":"One of the major challenges faced by network operators pertains to whether their network can meet input traffic demand, avoid overload, and satisfy service-level agreements. Automatically verifying if no network links are overloaded is complicated---requires modeling frequent network failures, complex routing and load-balancing technologies, and evolving traffic requirements. We present QARC, a distributed control plane abstraction that can automatically verify whether a control plane may cause link-load violations under failures. QARC is fully automatic and can help operators program networks that are more resilient to failures and upgrade the network to avoid violations. We apply QARC to real datacenter and ISP networks and find interesting cases of load violations. QARC can detect violations in under an hour.","PeriodicalId":20580,"journal":{"name":"Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77835577","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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