Coordination Protocols for Verifiable Consistency in Distributed Storage Systems

Achieving consistency in a highly available distributed storage system has been formally proven to be an impossible task when the system faces network partitions and faulty processes. The complexity is exacerbated when the system allows concurrent processes to send transactions to all the other servers and coordinate the consistent commitment of different transactions. In the event of a partition, each server may allow clients to request updates involving the current state of the data, which makes achieving replicated consistency challenging. To solve the inconsistency problems, several consensus protocols are used, but have strict requirements in order to make progress and are not guaranteed to ever converge to a single value. Additionally, the coordination required to achieve consistency after a partition will be extremely high as each node must compare transaction times and conflicting data with all other servers in the system. To address the inconsistency in distributed systems, this thesis proposes a new coordination protocol that utilizes four ideas in order for clients to verify the consistency of data: (1) a universal timestamp signatory to certify the global order of events, (2) a relative consistency indicator to determine relative consistency during partitions, (3) an operation-based recency-weighted conflict resolution algorithm to simplify coordination for achieving global consistency, and (4) a rejection-oriented distributed transaction commit protocol to eliminate any guarantees required by atomic commit protocols and verify local consistency. This thesis will evaluate and analyze various issues related to coordination and concurrency under network partitions. The experimental results demonstrate that the proposed methods provide a verifiable consistency to the servers of the distributed storage systems

Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability

Blockchain Inefficiency in the Bitcoin Peers Network

We investigate Bitcoin network monitoring the dynamics of blocks and transactions. We unveil that 43\% of the transactions are still not included in the Blockchain after 1h from the first time they were seen in the network and 20\% of the transactions are still not included in the Blockchain after 30 days, revealing therefore great inefficiency in the Bitcoin system. However, we observe that most of these `forgotten' transactions have low values and in terms of transferred value the system is less inefficient with 93\% of the transactions value being included into the Blockchain within 3h. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes and calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording.Comment: 15 pages, 8 figures, 3 table

Distributed Machine Learning via Sufficient Factor Broadcasting

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology. When these models are applied to large-scale ML problems starting at millions of samples and tens of thousands of classes, their parameter matrix can grow at an unexpected rate, resulting in high parameter synchronization costs that greatly slow down distributed learning. To address this issue, we propose a Sufficient Factor Broadcasting (SFB) computation model for efficient distributed learning of a large family of matrix-parameterized models, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i.e., the outer product of two "sufficient factors" (SFs). By broadcasting the SFs among worker machines and reconstructing the update matrices locally at each worker, SFB improves communication efficiency --- communication costs are linear in the parameter matrix's dimensions, rather than quadratic --- without affecting computational correctness. We present a theoretical convergence analysis of SFB, and empirically corroborate its efficiency on four different matrix-parametrized ML models

Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen