Timing Analysis Sample Clauses
The Timing Analysis clause establishes the requirements and procedures for evaluating the timing performance of a system, component, or process. It typically outlines how timing measurements should be conducted, what metrics or benchmarks must be met, and the documentation or reporting standards to be followed. For example, it may require periodic reviews of system response times or mandate the use of specific tools for timing verification. The core function of this clause is to ensure that all timing-related aspects are systematically assessed and meet predefined standards, thereby reducing the risk of performance issues and ensuring reliable operation.
Timing Analysis. Table 2 outlines the timing analysis measurements for tasks in the Open Vote Network. All measurements were performed on a MacBook Pro running OS X 10.10.5 equipped with 4 cores, 2.3GHz Intel Core i7 and 16 GB DDR3 RAM. All time measurements are rounded up to the next whole millisecond. We use the Web3 framework to facilitate communication between the web browser and the Ethereum daemon. All tasks are executed using .call() that allows us to measure the code’s computation time on the local daemon. The cryptography smart contract is responsible for creating the zero knowl- edge proofs for the voter. The time required to create the proofs is 81 ms for the ▇▇▇▇▇▇▇ proof and 461 ms for the one-out-of-two zero knowledge proof. These actions are always executed using .call() as this contract should never receive transactions. The voting smart contract is responsible for enforcing the election process. Registering a vote involves verifying the ▇▇▇▇▇▇▇ zero knowledge proof and in total requires 142 ms. To begin the election requires computing the reconstructed public keys which takes 277 ms in total for forty voters. Casting a vote involves verifying the one-out-of-two zero knowledge proof which requires 573 ▇▇. ▇▇▇▇▇- ing involves summing all cast votes and brute-forcing the discrete logarithm of the result and on average takes around 132 ms. We decided to distribute the cryptography code using the Ethereum blockchain to allow all voters to use the same code. Running the code on the voter’s local daemon is significantly slower than using a seperate library such as OpenSSL. For example, creating a ▇▇▇▇▇▇▇ signature using OpenSSL on a comparable machine requires 0.69 ms [27]. This slowness is mostly due to the lack of native support for elliptic curve math in Ethereum smart contracts. The Ethereum Foundation Action Avg. Time (ms) Create ZKP(x) 81 Register voting key 142 Begin election 277 Create 1-out-of-2 ZKP 461 Cast vote 573 Tally 132 has plans to include native support and we expect this to significantly improve our reported times.
Timing Analysis. Timing modeling in EAST-ADL results from the work done in the TIMMO project, which produced a dedicated language called TADL (see TIMMO deliverable D6 for instance available from ▇▇▇▇://▇▇▇.▇▇▇▇▇.▇▇▇/). TADL concepts were integrated in the course of the ATESST2 project in the EAST-ADL language. Essentially three packages structure the timing concepts: Timing which defines core elements and their organization, Events which lists various kinds of events that can be associated to structural elements, such arrival of data on ports, and TimingConstraints which lists all possible constraints one can model – delays, synchronization, etc. The modeling principle is the following. TimingConstraints are associated to an EventChain, which defines the scope of the constraint. The EventChain in turn relates to various Events, such as arrival of a data on a Port. Events point to structural elements, such as Ports or Functions Based on this modelling concepts timing analysis can be performed. However TADL does not cover implementation level concepts such as tasks, which are needed to conduct a detailed schedulability analysis for instance. Thus the analysis that one can perform at this level is restricted to feasibility assessment regarding e.g. response times, age, synchronization and resource load balancing and assessing. This provides an interesting insight on how the software implementation could later be defined. To go beyond this, one needs to go to the implementation level (i.e. Autosar architecture) where the allocation of execution to tasks is defined. For this, ▇▇▇▇▇ provides a good basis. In the experiments done on timing analysis in ATESST2, the extra information needed in EAST-ADL models were added using ▇▇▇▇▇ constructs. The following section provides a summary of the main elements in both languages, EAST-ADL (and TIMMO) and ▇▇▇▇▇.
3.4.1 EAST-ADL/TADL
Timing Analysis. At the beginning of the project the purpose of the Timing Analysis plug-in was centred on the idea of providing schedulability analysis of EAST-ADL models. Beyond M12, Timing Analysis for EAST- ADL models has been refined [6]. The following timing analyses have been identified as best- suited to support EAST-ADL Design level models (as documented in D3.1.1):
Timing Analysis. The Timing Analysis plugin builds on results from EDONA and ATESST2 projects to provide schedulability means on EAST-ADL models. The schedulability analysis engine itself (or link to such an engine, provided as a third-party tool) will not be developed in MAENAD, rather the adaptation of a bridge to such an engine will be provided to enable EAST-ADL models analysis. As such, the gap between what EAST-ADL models provide for in terms of timing information and what is needed to perform such an analysis will be assessed and means for manual edition or automated processing will be documented/developed in the course of the project.
2.2.1 Current status