Archiving the Relaxed Consistency Web

The historical, cultural, and intellectual importance of archiving the web has been widely recognized. Today, all countries with high Internet penetration rate have established high-profile archiving initiatives to crawl and archive the fast-disappearing web content for long-term use. As web technologies evolve, established web archiving techniques face challenges. This paper focuses on the potential impact of the relaxed consistency web design on crawler driven web archiving. Relaxed consistent websites may disseminate, albeit ephemerally, inaccurate and even contradictory information. If captured and preserved in the web archives as historical records, such information will degrade the overall archival quality. To assess the extent of such quality degradation, we build a simplified feed-following application and simulate its operation with synthetic workloads. The results indicate that a non-trivial portion of a relaxed consistency web archive may contain observable inconsistency, and the inconsistency window may extend significantly longer than that observed at the data store. We discuss the nature of such quality degradation and propose a few possible remedies.Comment: 10 pages, 6 figures, CIKM 201

Overview of Auditing Cloud Consistency

Cloud storage services have become very popular due to their infinite advantages. To provide always-on access, a cloud service provider (CSP) maintains multiple copies for each piece of data on geographically distributed servers. A major disadvantage of using this technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this system, a novel consistency as a service (CaaS) model is presented, which involves a large data cloud and many small audit clouds. In the CaaS model we are presented in our system, a data cloud is maintained by a CSP. A group of users that participate an audit cloud can verify whether the data cloud provides the promised level of consistency or not. The system proposes a two level auditing architecture, which need a loosely synchronize clock in the audit cloud. Then design algorithms to measure the severity of violations with two metrics: the commonality of violations, and the oldness value of read. Finally, heuristic auditing strategy (HAS) is devised to find out as many violations as possible. Many experiments were performed using a combination of simulations and a real cloud deployment to validate HAS. DOI: 10.17762/ijritcc2321-8169.15011

DEPENDABILITY AS A FACILITY: APPRAISING CLOUD STABILITY

Within the system of cloud storage, reliability determines precision in addition to real cost for every transaction. Cloud technique is essentially an essential distributed system by which facts are replicated on numerous servers for achieving of high convenience in addition to high finish. Within our work we concentrate on an entirely new constancy like a service representation that comprises cloud of massive data along with other minute audit clouds. During this method controlling of cloud facts are by way of provider of cloud service in addition to clients that form an audit cloud which system verifies whether data cloud is providing the assured volume of constancy. With new constancy like a service illustration clients will measure brilliance of cloud services and choose a precise provider of cloud service between several candidates. We create a two-level auditing structure that needs a loosely synchronized clock intended for pointing of functions within the audit cloud

Quantifying Eventual Consistency with PBS

Data replication results in a fundamental trade-off between operation latency and consistency. At the weak end of the spectrum of possible consistency models is eventual consistency, which provides no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough ” for practitioners given its latency and availability benefits. In this work, we explain this phenomenon and demonstrate that, despite their weak guarantees, eventually consistent systems regularly return consistent data while providing lower latency than their strongly consistent counterparts. To quantify the behavior of eventually consistent stores, we introduce Probabilistically Bounded Staleness (PBS), a consistency model that provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for version-based staleness and model real-time staleness for a large class of quorum replicated, Dynamo-style stores. Using PBS, we measure the trade-off between latency and consistency for partial, non-overlapping quorum systems under Internet production workloads. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering large latency benefits. 1

Eventual Consistent Databases: State of the Art

One of the challenges of cloud programming is to achieve the right balance between the availability and consistency in a distributed database. Cloud computing environments, particularly cloud databases, are rapidly increasing in importance, acceptance and usage in major applications, which need the partition-tolerance and availability for scalability purposes, but sacrifice the consistency side (CAP theorem). In these environments, the data accessed by users is stored in a highly available storage system, thus the use of paradigms such as eventual consistency became more widespread. In this paper, we review the state-of-the-art database systems using eventual consistency from both industry and research. Based on this review, we discuss the advantages and disadvantages of eventual consistency, and identify the future research challenges on the databases using eventual consistency

D.1.3 – Protocols for emergent localities

GDD_HCERES2020This report presents two contributions that illustrate the potential of emerging-locality protocols in large-scale decentralized systems, in two areas of decentralized social computing: recommendation, and eventual consistency of mutable data structures. The first contribution consists of a framework supporting the development of dynamically adaptive decen-tralised recommendation systems. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. Our framework address this through a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission. Our second contribution addresses the growing demand for differentiated consistency requirements in large-scale applications. A large number of today's applications rely on Eventual Consistency, a consistency model that emphasizes liveness over safety. Designers generally adopt this consistency model uniformly throughout a distributed system due to its ability to scale as the number of users or devices grows larger. But this clashes with the need for differentiated consistency requirements. In this contribution, we address this need by introducing UPS, a novel consistency mechanism that offers differentiated eventual consistency and delivery speed by working in pair with a two-phase epidemic broadcast protocol. We propose a closed-form analysis of our approach's delivery speed, and we evaluate our complete protocol experimentally on a simulated network of one million nodes. To measure the consistency trade-off, we formally define a novel and scalable consistency metric operating at runtime

D.1.3 – Protocols for emergent localities

GDD_HCERES2020This report presents two contributions that illustrate the potential of emerging-locality protocols in large-scale decentralized systems, in two areas of decentralized social computing: recommendation, and eventual consistency of mutable data structures. The first contribution consists of a framework supporting the development of dynamically adaptive decen-tralised recommendation systems. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. Our framework address this through a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission. Our second contribution addresses the growing demand for differentiated consistency requirements in large-scale applications. A large number of today's applications rely on Eventual Consistency, a consistency model that emphasizes liveness over safety. Designers generally adopt this consistency model uniformly throughout a distributed system due to its ability to scale as the number of users or devices grows larger. But this clashes with the need for differentiated consistency requirements. In this contribution, we address this need by introducing UPS, a novel consistency mechanism that offers differentiated eventual consistency and delivery speed by working in pair with a two-phase epidemic broadcast protocol. We propose a closed-form analysis of our approach's delivery speed, and we evaluate our complete protocol experimentally on a simulated network of one million nodes. To measure the consistency trade-off, we formally define a novel and scalable consistency metric operating at runtime

A Stochastic Model of Plausibility in Live-Virtual-Constructive Environments

Distributed live-virtual-constructive simulation promises a number of benefits for the test and evaluation community, including reduced costs, access to simulations of limited availability assets, the ability to conduct large-scale multi-service test events, and recapitalization of existing simulation investments. However, geographically distributed systems are subject to fundamental state consistency limitations that make assessing the data quality of live-virtual-constructive experiments difficult. This research presents a data quality model based on the notion of plausible interaction outcomes. This model explicitly accounts for the lack of absolute state consistency in distributed real-time systems and offers system designers a means of estimating data quality and fitness for purpose. Experiments with World of Warcraft player trace data validate the plausibility model and exceedance probability estimates. Additional experiments with synthetic data illustrate the model\u27s use in ensuring fitness for purpose of live-virtual-constructive simulations and estimating the quality of data obtained from live-virtual-constructive experiments