Mobile Resource Guarantees for Smart Devices

Aspinall, David; Gilmore, Stephen; Hofmann, Martin; Sannella, Donald; Stark, Ian

doi:10.1007/978-3-540-30569-9_1

Cited by 34 publications

(45 citation statements)

References 30 publications

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“…Related work in the context of Java bytecode includes the work in the MRG project [3], which can be considered complementary to ours. MRG focuses on building a proof-carrying code [15] architecture for ensuring that bytecode programs are free from run-time violations of resource bounds.…”

Section: Discussionmentioning

confidence: 99%

“…(3) Demonstrating that the cost relations describe the cost as defined in step 2. The first two steps are straightforward as the CFG and the recursive representation describe the behavior of the original program, in particular at each branching point we have several possibilities from which only one will be executed.…”

Section: Examplementioning

confidence: 99%

“…For instance, the receiver of the code may want to infer cost information in order to decide whether to reject code which has too large cost requirements in terms of computing resources (in time and/or space), and to accept code which meets the established requirements [8,2,3]. In fact, this is the main motivation for the Mobile Resource Guarantees (MRG) research project [3], which establishes a Proof-Carrying Code [15] framework for guaranteeing resource consumption. Furthermore, the Mobility, Ubiquity and Security (MOBIUS) research project [4], also considers resource consumption as one of the central properties of interest for proof-carrying code.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost Analysis of Java Bytecode

Albert

Arenas

Genaim

et al. 2007

Programming Languages and Systems

118

201

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Abstract. Cost analysis of Java bytecode is complicated by its unstructured control flow, the use of an operand stack and its object-oriented programming features (like dynamic dispatching). This paper addresses these problems and develops a generic framework for the automatic cost analysis of sequential Java bytecode. Our method generates cost relations which define at compile-time the cost of programs as a function of their input data size. To the best of our knowledge, this is the first approach to the automatic cost analysis of Java bytecode.

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Section: Discussionmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cost Analysis of Java Bytecode

Albert

Arenas

Genaim

et al. 2007

Programming Languages and Systems

118

201

View full text Add to dashboard Cite

show abstract

“…Fortunately, this is sufficient for a wide variety of interesting programs. Moreover, the analysis was successfully used to certify such bounds in a Proof Carrying Code system [3].However, it is also important to bound the stack space requirements, especially for functional programs where it is easy to cause excessive stack usage by accident. The Hofmann-Jost analysis has previously been adapted to measure stack space [4,5], but the form of the bounds was again limited to linear functions in terms of the total size of the input.…”

mentioning

confidence: 99%

Amortised Memory Analysis Using the Depth of Data Structures

Campbell

2009

Programming Languages and Systems

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Abstract. Hofmann and Jost have presented a heap space analysis [1] that finds linear space bounds for many functional programs. It uses an amortised analysis: assigning hypothetical amounts of free space (called potential) to data structures in proportion to their sizes using type annotations. Constraints on these annotations in the type system ensure that the total potential assigned to the input is an upper bound on the total memory required to satisfy all allocations. We describe a related system for bounding the stack space requirements which uses the depth of data structures, by expressing potential in terms of maxima as well as sums. This is achieved by adding extra structure to typing contexts (inspired by O'Hearn's bunched typing [2]) to describe the form of the bounds. We will also present the extra steps that must be taken to construct a typing during the analysis.Obtaining bounds on the resource requirements of programs can be crucial for ensuring that they enjoy reliability and security properties, particularly for use in constrained systems such as mobile phones, smartcards and embedded systems. Hofmann and Jost have presented a type-based amortised analysis for finding upper bounds on the heap memory required for programs in a simple functional programming language [1]. The form of these bounds is limited to linear functions with respect to the size of the program's input. Fortunately, this is sufficient for a wide variety of interesting programs. Moreover, the analysis was successfully used to certify such bounds in a Proof Carrying Code system [3].However, it is also important to bound the stack space requirements, especially for functional programs where it is easy to cause excessive stack usage by accident. The Hofmann-Jost analysis has previously been adapted to measure stack space [4,5], but the form of the bounds was again limited to linear functions in terms of the total size of the input.In this work we present a similar analysis where bounds are given as maxplus expressions on the depth of data structures. This is far more precise for programs operating on tree-structured data.Like Hofmann-Jost, our analysis consists of two parts: a type-system which certifies that a given bound really is an upper bound on the stack memory

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“…some cost measure. Cost analysis has a large application field, which includes resource certification [11,4,16,9], whereby code consumers can reject code which is not guaranteed to run within the resources available. The resources considered include processor cycles, memory usage, or billable events, e.g., the number of text messages or bytes sent on a mobile network.…”

Section: Introductionmentioning

confidence: 99%

Automatic Inference of Upper Bounds for Recurrence Relations in Cost Analysis

et al.

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Abstract. The classical approach to automatic cost analysis consists of two phases. Given a program and some measure of cost, we first produce recurrence relations (RRs) which capture the cost of our program in terms of the size of its input data. Second, we convert such RRs into closed form (i.e., without recurrences). Whereas the first phase has received considerable attention, with a number of cost analyses available for a variety of programming languages, the second phase has received comparatively little attention. In this paper we first study the features of RRs generated by automatic cost analysis and discuss why existing computer algebra systems are not appropriate for automatically obtaining closed form solutions nor upper bounds of them. Then we present, to our knowledge, the first practical framework for the fully automatic generation of reasonably accurate upper bounds of RRs originating from cost analysis of a wide range of programs. It is based on the inference of ranking functions and loop invariants and on partial evaluation.

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Mobile Resource Guarantees for Smart Devices

Cited by 34 publications

References 30 publications

Cost Analysis of Java Bytecode

Cost Analysis of Java Bytecode

Amortised Memory Analysis Using the Depth of Data Structures

Automatic Inference of Upper Bounds for Recurrence Relations in Cost Analysis

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