LightChain: A DHT-based Blockchain for Resource Constrained Environments

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions have downsides in communication and storage efficiency, convergence to centralization, and consistency problems. In this paper, we propose LightChain, which is the first blockchain architecture that operates over a Distributed Hash Table (DHT) of participating peers. LightChain is a permissionless blockchain that provides addressable blocks and transactions within the network, which makes them efficiently accessible by all the peers. Each block and transaction is replicated within the DHT of peers and is retrieved in an on-demand manner. Hence, peers in LightChain are not required to retrieve or keep the entire blockchain. LightChain is fair as all of the participating peers have a uniform chance of being involved in the consensus regardless of their influence such as hashing power or stake. LightChain provides a deterministic fork-resolving strategy as well as a blacklisting mechanism, and it is secure against colluding adversarial peers attacking the availability and integrity of the system. We provide mathematical analysis and experimental results on scenarios involving 10K nodes to demonstrate the security and fairness of LightChain. As we experimentally show in this paper, compared to the mainstream blockchains like Bitcoin and Ethereum, LightChain requires around 66 times less per node storage, and is around 380 times faster on bootstrapping a new node to the system, while each LightChain node is rewarded equally likely for participating in the protocol

PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation

Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive $8\text{TB}$ TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.Comment: 36 page

Performance Analysis of Blockchain Platforms

Blockchain technologies have drawn massive attention to the world these past few years mostly because of the burst of cryptocurrencies like Bitcoin, Etherium, Ripple and many others. A Blockchain, also known as distributed ledger technology, has demonstrated huge potential in saving time and costs. This open-source technology which generates a decentralized public ledger of transactions is widely appreciated for ensuring a high level of privacy through encryption and thus sharing the transaction details only amongst the participants involved in the transactions. The Blockchain is used not only for cryptocurrency but also by various companies to meet their business ends, such as efficient management of supply chains and logistics. The rise and fall of numerous crypto-currencies based on blockchain technology have generated debate among tech-giants and regulatory bodies. There are various groups which are working on standardizing the blockchain technology. At the same time, numerous groups are actively working, developing and fine-tuning their own blockchain platforms. Platforms such as etherium, hyperledger, parity, etc. have their own pros and cons. This research is focused on the performance analysis of blockchain platforms which gives a comparative understanding of these platforms