660 research outputs found
A true concurrent model of smart contracts executions
The development of blockchain technologies has enabled the trustless
execution of so-called smart contracts, i.e. programs that regulate the
exchange of assets (e.g., cryptocurrency) between users. In a decentralized
blockchain, the state of smart contracts is collaboratively maintained by a
peer-to-peer network of mutually untrusted nodes, which collect from users a
set of transactions (representing the required actions on contracts), and
execute them in some order. Once this sequence of transactions is appended to
the blockchain, the other nodes validate it, re-executing the transactions in
the same order. The serial execution of transactions does not take advantage of
the multi-core architecture of modern processors, so contributing to limit the
throughput. In this paper we propose a true concurrent model of smart contract
execution. Based on this, we show how static analysis of smart contracts can be
exploited to parallelize the execution of transactions.Comment: Full version of the paper presented at COORDINATION 202
A theory of transaction parallelism in blockchains
Decentralized blockchain platforms have enabled the secure exchange of
crypto-assets without the intermediation of trusted authorities. To this
purpose, these platforms rely on a peer-to-peer network of byzantine nodes,
which collaboratively maintain an append-only ledger of transactions, called
blockchain. Transactions represent the actions required by users, e.g. the
transfer of some units of crypto-currency to another user, or the execution of
a smart contract which distributes crypto-assets according to its internal
logic. Part of the nodes of the peer-to-peer network compete to append
transactions to the blockchain. To do so, they group the transactions sent by
users into blocks, and update their view of the blockchain state by executing
these transactions in the chosen order. Once a block of transactions is
appended to the blockchain, the other nodes validate it, re-executing the
transactions in the same order. The serial execution of transactions does not
take advantage of the multi-core architecture of modern processors, so
contributing to limit the throughput. In this paper we develop a theory of
transaction parallelism for blockchains, which is based on static analysis of
transactions and smart contracts. We illustrate how blockchain nodes can use
our theory to parallelize the execution of transactions. Initial experiments on
Ethereum show that our technique can improve the performance of nodes.Comment: arXiv admin note: text overlap with arXiv:1905.0436
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From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality
The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems
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