Static analysis of cloud elasticity

Garcia, Abel; Laneve, Cosimo; Lienhardt, Michael

doi:10.1145/2790449.2790524

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…• a first phase whose goal is the generation of the cost relations from the program to be analyzed (see [4], [18]); • and a second phase that aims at computing a closedform upper-bound from the cost relations [1], [16].…”

Section: B Resource Analysismentioning

confidence: 99%

Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

2021

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Profiling tools have been widely used for studying the behavior of the programs with the objective of reducing the amount of resources consumed by them. Most profilers collect the information with dynamic techniques, i.e., execute an instrumented version of the program with some specific input arguments to profile the measures of interest. This article presents a novel static profiling technique for Ethereum smart contracts that, using static resource analysis, is able to generate upper-bound expressions that can be used to produce profiling information about the measure of interest. Unlike traditional profiling tools, we get upper-bounds on the measures of interest expressed in terms of the input arguments or the state variables of the smart contracts. The information that can be obtained by the upper-bounds allows us to detect gas-expensive fragments of a Solidity program or to spot resource-related vulnerabilities at specific program points of the program. Moreover, in this article we propose an automatic optimization of Solidity programs which reduces their gas consumption replacing the accesses to state variables by gasefficient accesses to local variables. We have experimentally evaluated our technique and we have detected that 6.81% of the public functions analyzed can be optimized and 1.43% are vulnerable to execute arbitrary code.

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Section: B Resource Analysismentioning

confidence: 99%

Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

2021

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“…W.l.o.g., we use the cost model adopted in the Complexity and Termination competition http://termination-portal.org/wiki/ Termination_Competition_2019 (abbreviated as TermComp) which simply estimates the asymptotic complexity order (e.g., by accumulating constant values in cost functions). This classical resource analysis approach has been applied to a wide variety of declarative and imperative programming languages: earlier work applied it to functional (Wegbreit 1975) and logic languages (Debray and Lin 1993;Debray et al 1994), later work to imperative languages such as Java and Java bytecode (Albert et al 2007), concurrent programs (Garcia et al 2015;Albert et al 2018), LLVM (Grech et al 2015;Liqat et al 2015), among others. In most cases, the program written in any imperative/declarative, source/bytecode language is first transformed into a simpler intermediate representation (IR) that works only on Integer data, which is the starting point of our work.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Resource Analysis driven by (Conditional) Termination Proofs

Albert

Bofill

Borralleras

et al. 2019

Theory and Practice of Logic Programming

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When programs feature a complex control flow, existing techniques for resource analysis produce cost relation systems (CRS) whose cost functions retain the complex flow of the program and, consequently, might not be solvable into closed-form upper bounds. This paper presents a novel approach to resource analysis that is driven by the result of a termination analysis. The fundamental idea is that the termination proof encapsulates the flows of the program which are relevant for the cost computation so that, by driving the generation of the CRS using the termination proof, we produce a linearly-bounded CRS (LB-CRS). A LB-CRS is composed of cost functions that are guaranteed to be locally bounded by linear ranking functions and thus greatly simplify the process of CRS solving. We have built a new resource analysis tool, named MaxCore, that is guided by the VeryMax termination analyzer and uses CoFloCo and PUBS as CRS solvers. Our experimental results on the set of benchmarks from the Complexity and Termination Competition 2019 for C Integer programs show that MaxCore outperforms all other resource analysis tools.

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“…This article is a revised and enhanced version of [15] that includes a new semantics of the language, the full proofs of all the results and a discussion about our prototype implementation that includes the type inference system and the experimental assessments we did.…”

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confidence: 99%

Static analysis of cloud elasticity

Garcia¹,

Laneve²,

Lienhardt³

2017

Science of Computer Programming

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Please cite this article in press as: A. Garcia et al., Static analysis of cloud elasticity, Sci. Comput. Program. (2017), http://dx. Highlights• A technique for the static analysis of resource utilization is proposed.• The analysis relies on a static type system that records program's resources behavior.• The proposed approach supports concurrency and explicit release operations.• A proof of soundness of the proposed type system and of the analysis is provided. AbstractWe propose a static analysis technique that computes upper bounds of virtual machine usages in a concurrent language with explicit acquire and release operations of virtual machines. In our language it is possible to delegate other (ad-hoc or third party) concurrent code to release virtual machines (by passing them as arguments of invocations). Our technique is modular and consists of (i) a type system associating programs with behavioural types that record relevant information for resource usage (creations, releases, and concurrent operations), (ii) a translation function that takes behavioural types and returns cost equations, and (iii) an automatic off-the-shelf solver for the cost equations.A soundness proof of the type system establishes the correctness of our technique with respect to the cost equations. We have experimentally evaluated our technique using a cost analysis solver and we report some results.

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Static analysis of cloud elasticity

Cited by 10 publications

References 24 publications

Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

Resource Analysis driven by (Conditional) Termination Proofs

Static analysis of cloud elasticity

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