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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
PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication
Geo-replicated data platforms are at the backbone of several large-scale
online services. Transactional Causal Consistency (TCC) is an attractive
consistency level for building such platforms. TCC avoids many anomalies of
eventual consistency, eschews the synchronization costs of strong consistency,
and supports interactive read-write transactions. Partial replication is
another attractive design choice for building geo-replicated platforms, as it
increases the storage capacity and reduces update propagation costs. This paper
presents PaRiS, the first TCC system that supports partial replication and
implements non-blocking parallel read operations, whose latency is paramount
for the performance of read-intensive applications. PaRiS relies on a novel
protocol to track dependencies, called Universal Stable Time (UST). By means of
a lightweight background gossip process, UST identifies a snapshot of the data
that has been installed by every DC in the system. Hence, transactions can
consistently read from such a snapshot on any server in any replication site
without having to block. Moreover, PaRiS requires only one timestamp to track
dependencies and define transactional snapshots, thereby achieving resource
efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment
composed of up to 10 replication sites. We show that PaRiS scales well with the
number of DCs and partitions, while being able to handle larger data-sets than
existing solutions that assume full replication. We also demonstrate a
performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x
higher throughput with 5.91x lower latency for read-dominated workloads and up
to 1.46x higher throughput with 20.56x lower latency for write-heavy
workloads)
Extending Eventually Consistent Cloud Databases for Enforcing Numeric Invariants
Geo-replicated databases often operate under the principle of eventual
consistency to offer high-availability with low latency on a simple key/value
store abstraction. Recently, some have adopted commutative data types to
provide seamless reconciliation for special purpose data types, such as
counters. Despite this, the inability to enforce numeric invariants across all
replicas still remains a key shortcoming of relying on the limited guarantees
of eventual consistency storage. We present a new replicated data type, called
bounded counter, which adds support for numeric invariants to eventually
consistent geo-replicated databases. We describe how this can be implemented on
top of existing cloud stores without modifying them, using Riak as an example.
Our approach adapts ideas from escrow transactions to devise a solution that is
decentralized, fault-tolerant and fast. Our evaluation shows much lower latency
and better scalability than the traditional approach of using strong
consistency to enforce numeric invariants, thus alleviating the tension between
consistency and availability
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