NoSQL stores for coreless mobile networks

The goals of 5G networks — low latency, high bandwidth, and support for fast mobility — are non-trivial and they demand improvements across all involved technology fields. Researchers are therefore exploring approaches that leverage on network function virtualization and software-defined networking for meeting the demands of verticals expected to use 5G networks. One approach which appears promising is the concept of a coreless mobile network where the key network functions are placed at the edge of the network. In this article we focus on management of the user-context state in a coreless mobile network, and posit that these network functions can use a NoSQL data store for maintaining the user-context and other state variables. We first present an overview of promising NoSQL data stores and evaluate their suitability. We then present the results of benchmarking the Apache Geode data store as an example of a state management solution which could serve a coreless mobile network. During our tests we observe that the Apache Geode data store is, subject to its configuration, capable of delivering the data model, consistency, and high availability required by a coreless mobile network.Peer reviewe

Data Integration over NoSQL Stores Using Access Path Based Mappings

International audienceDue to the large amount of data generated by user interactions on the Web, some companies are currently innovating in the domain of data management by designing their own systems. Many of them are referred to as NoSQL databases, standing for 'Not only SQL'. With their wide adoption will emerge new needs and data integration will certainly be one of them. In this paper, we adapt a framework encountered for the integration of relational data to a broader context where both NoSQL and relational databases can be integrated. One important extension consists in the efficient answering of queries expressed over these data sources. The highly denormalized aspect of NoSQL databases results in varying performance costs for several possible query translations. Thus a data integration targeting NoSQL databases needs to generate an optimized translation for a given query. Our contributions are to propose (i) an access path based mapping solution that takes benefit of the design choices of each data source, (ii) integrate preferences to handle conflicts between sources and (iii) a query language that bridges the gap between the SQL query expressed by the user and the query language of the data sources. We also present a prototype implementation, where the target schema is represented as a set of relations and which enables the integration of two of the most popular NoSQL database models, namely document and a column family stores

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

AsterixDB: A Scalable, Open Source BDMS

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements

Idempotent distributed counters using a Forgetful Bloom Filter

Distributed key-value stores power the backend of high-performance web services and cloud computing applications. Key-value stores such as Cassandra rely heavily on counters to keep track of the occurrences of various kinds of events. However, today's implementations of counters do not provide exactly-once semantics. A typical scenario is that a client requests a counter increment, times out waiting for a response, and creates a duplicate request, thus resulting in a double increment on the server side. In this thesis, we address this problem by presenting, analyzing, and evaluating a novel server-side data structure called the Forgetful Bloom Filter (FBF). Like a traditional Bloom filter, an FBF is a compact representation of a set of elements (e.g., client requests). However, an FBF is more powerful than a Bloom filter in two aspects: i) it can forget older elements (e.g., requests that are too old to apply), and ii) it is self-adapting under a varying workload. We also present an adaptive variant of FBF that adapts itself to meet a desired false positive rate -- thus the error achieved in the counter can be bounded even as the workload changes. We present experimental results from a prototype implementation of FBFs and discuss the implications for a key-value store such as Cassandra. Our results show that the FBF is highly accurate in maintaining correct counter values

Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads

Parqua: Online Reconfigurations in Virtual Ring-Based NoSQL Systems

The performance of key-value/NoSQL storage systems is highly tied to the choice of (primary) key for the database table. As application (e.g., business) requirements change over time, and in order to fine-tune the performance of the database to the real query workload, system administrators need to change the primary key of the table. The primary key change is a specific example of a broader class of reconfiguration operations that affect a lot of data all at once. In industry deployments of key-value/NoSQL stores, such reconfigurations are known to be a major pain point. We seek to support reconfiguration operations in keyvalue/ NoSQL storage systems in an automated, online, and efficient manner, i.e., without interrupting the serving of incoming reads and writes, and quickly. Our previous work, titled Morphus, tackled the online reconfiguration problem for sharded NoSQL stores like MongoDB. However, Morphus is inapplicable to ring-based key-value/NoSQL systems (like Cassandra, Riak, and Voldemort) because these rely on a virtual ring (and often consistent hashing). This makes the problem more constrained. In this paper we propose a system called Parqua, which imbues ring-based key-value/NoSQL stores with the ability to perform reconfiguration operations in an online and efficient manner. We present the design and implementation of Parqua. We have integrated Parqua into Apache Cassandra. Experiments based on our cluster deployments show that during reconfiguration Parqua maintains high availability, and with a small impact on read and write latencies.NSF CNS 1319527, NSF CNS 1409416, NSF CCF 0964471, and AFOSR/AFRL FA8750-11-2-0084Ope