49,899 research outputs found
The Homeostasis Protocol: Avoiding Transaction Coordination Through Program Analysis
Datastores today rely on distribution and replication to achieve improved
performance and fault-tolerance. But correctness of many applications depends
on strong consistency properties - something that can impose substantial
overheads, since it requires coordinating the behavior of multiple nodes. This
paper describes a new approach to achieving strong consistency in distributed
systems while minimizing communication between nodes. The key insight is to
allow the state of the system to be inconsistent during execution, as long as
this inconsistency is bounded and does not affect transaction correctness. In
contrast to previous work, our approach uses program analysis to extract
semantic information about permissible levels of inconsistency and is fully
automated. We then employ a novel homeostasis protocol to allow sites to
operate independently, without communicating, as long as any inconsistency is
governed by appropriate treaties between the nodes. We discuss mechanisms for
optimizing treaties based on workload characteristics to minimize
communication, as well as a prototype implementation and experiments that
demonstrate the benefits of our approach on common transactional benchmarks
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Analysis of operating system diversity for intrusion tolerance
One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this paper, we present a study with operating system's (OS's) vulnerability data from the NIST National Vulnerability Database (NVD). We have analyzed the vulnerabilities of 11 different OSs over a period of 18 years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OSs. Hence, although there are a few caveats on the use of NVD data to support definitive conclusions, our analysis shows that by selecting appropriate OSs, one can preclude (or reduce substantially) common vulnerabilities from occurring in the replicas of the intrusion-tolerant system
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
The OTree: multidimensional indexing with efficient data sampling for HPC
Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft
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