Google Earth Engine: Planetary-scale geospatial analysis for everyone

Raft is a consensus algorithm for managing a replicated log. It produces a result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its structure is different from Paxos; this makes Raft more understandable than Paxos and also provides a better foundation for building practical systems. In order to enhance understandability, Raft separates the key elements of consensus, such as leader election, log replication, and safety, and it enforces a stronger degree of coherency to reduce the number of states that must be considered. Results from a user study demonstrate that Raft is easier for students to learn than Paxos. Raft also includes a new mechanism for changing the cluster membership, which uses overlapping majorities to guarantee safety.

/pdf/in-search-of-an-understandable-consensus-algorithm-2tvnovxuiu.pdf

In search of an understandable consensus algorithm

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins. In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

Making Smart Contracts Smarter

/pdf/making-smart-contracts-smarter-5g33ircijb.pdf

Making Smart Contracts Smarter.

Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol --- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3--7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to $1, 600$ nodes confirm ELASTICO's theoretical scaling properties.

A Secure Sharding Protocol For Open Blockchains

Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This paper describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free read-only transactions, and atomic schema changes, across all of Spanner.

/pdf/spanner-google-s-globally-distributed-database-57sdsy7wpa.pdf

Spanner: Google's globally-distributed database

Spanner is Google’s scalable, multiversion, globally distributed, and synchronously replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This article describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free snapshot transactions, and atomic schema changes, across all of Spanner.

https://dl.acm.org/doi/pdf/10.1145/2491245

Spanner: Google’s Globally Distributed Database

A server system receives requests from client systems and sends responses back to the client systems. For a subset of the requests, in addition to responding to a request from a client system, the method includes creating a cryptographic object at the server system. The cryptographic object is used to cryptographically protect information related to the request, and the cryptographically protected information associated with the cryptographic object is stored at the server system. The server system then sends the cryptographic object to the client system, and in conjunction with sending the cryptographic object to the client system, irreversibly modifies the cryptographic object on the server system. For example, in some embodiments the cryptographic object includes an cryptographic key, and the server system deletes or truncates the only instances of the cryptographic object on the server system when the server system finishes responding to the request from the client system.

Privacy enhancements for server-side cookies

A distributed storage system is provided. The distributed storage system includes multiple front-end servers and zones for managing data for clients. Data within the distributed storage system is associated with a plurality of accounts and divided into a plurality of groups, each group including a plurality of splits, each split being associated with a respective account, and each group having multiple tablets and each tablet managed by a respective tablet server of the distributed storage system. Data associated with different accounts may be replicated within the distributed storage system using different data replication policies. There is no limit to the amount of data for an account by adding new splits to the distributed storage system. In response to a client request for a particular account's data, a front-end server communicates such request to a particular zone that has the client-requested data and returns the client-requested data to the requesting client.

Michal Piotr Szymaniak

Papers

Spanner: Google's globally-distributed database

Spanner: Google’s Globally Distributed Database

Privacy enhancements for server-side cookies

Organizing Data in a Distributed Storage System

Storing and Moving Data in a Distributed Storage System