Efficient transfers to many recipients present a host of issues on Ethereum. First, accounts are identified by long and incompressible constants. Second, these constants have to be stored and communicated for each payment. Third, the standard interface for token transfers does not support lists of recipients, adding repeated communication to the overhead. Since Ethereum charges resource usage, even small optimizations translate to cost savings. Airdrops, a popular marketing tool used to boost coin uptake, present a relevant example for the value of optimizing bulk transfers. Therefore, we review technical solutions for airdrops of Ethereum-based tokens, discuss features and prerequisites, and compare the operational costs by simulating 35 scenarios. We find that cost savings of factor two are possible, but require specific provisions in the smart contract implementing the token system. Pull-based approaches, which use on-chain interaction with the recipients, promise moderate savings for the distributor while imposing a disproportional cost on each recipient. Total costs are broadly linear in the number of recipients independent of the technical approach. We publish the code of the simulation framework for reproducibility, to support future airdrop decisions, and to benchmark innovative bulk payment solutions.
Chen @cite_12 identify underpriced instructions (even after the 2016 gas price adjustment) and propose an adaptive pricing scheme. Their main interest is to raise economic barriers against congestion, which in the worst case enables denial of service attacks on the systemic level.
@cite_12
2015555034 2133068139 2963220038 2061926759
While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach. The adaptive cubic overestimation algorithm described in Cartis, Gould and Toint (2007) is adapted to the problem of minimizing a nonlinear, possibly nonconvex, smooth objective function over a convex domain. Convergence to first-order critical points is shown under standard assumptions, but without any Lipschitz continuity requirement on the objective’s Hessian. A worst-case complexity analysis in terms of evaluations of the problem’s function and derivatives is also presented for the Lipschitz continuous case and for a variant of the resulting algorithm. This analysis extends the best known bound for general unconstrained problems to nonlinear problems with convex constraints. The gas mechanism in Ethereum charges the execution of every operation to ensure that smart contracts running in EVM (Ethereum Virtual Machine) will be eventually terminated. Failing to properly set the gas costs of EVM operations allows attackers to launch DoS attacks on Ethereum. Although Ethereum recently adjusted the gas costs of EVM operations to defend against known DoS attacks, it remains unknown whether the new setting is proper and how to configure it to defend against unknown DoS attacks. In this paper, we make the first step to address this challenging issue by first proposing an emulation-based framework to automatically measure the resource consumptions of EVM operations. The results reveal that Ethereum’s new setting is still not proper. Moreover, we obtain an insight that there may always exist exploitable under-priced operations if the cost is fixed. Hence, we propose a novel gas cost mechanism, which dynamically adjusts the costs of EVM operations according to the number of executions, to thwart DoS attacks. This method punishes the operations that are executed much more frequently than before and lead to high gas costs. To make our solution flexible and secure and avoid frequent update of Ethereum client, we design a special smart contract that collaborates with the updated EVM for dynamic parameter adjustment. Experimental results demonstrate that our method can effectively thwart both known and unknown DoS attacks with flexible parameter settings. Moreover, our method only introduces negligible additional gas consumption for benign users. A large penetration of electric and plug-in hybrid electric vehicles would likely result in increased system peaks and overloading of power system assets if the charging of vehicles is left uncontrolled. In this paper we propose both a centralized and a decentralized smart-charging scheme which seek to minimize system-wide generation costs while respecting grid constraints. Under the centralized scheme, vehicles' batteries are aggregated to virtual storage resources at each network node, which are optimally dispatched with a multiperiod Optimal Power Flow. On the other hand, under the decentralized scheme, price profiles broadcasted to vehicles day-ahead are determined so that the optimal response of individual vehicles to this tariff achieves the goal of cost minimization. Two alternative tariffs are explored, one where the same price profile applies system-wide, and another where different prices can be defined at different nodes. Results show that compared with uncontrolled charging, these smart-charging schemes successfully avoid asset overloading, displace most charging to valley hours and reduce generation costs. Moreover they are robust in the face of forecast errors in vehicle behavior.