While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address "cloud OLTP" applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of apples-to-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!'s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible--it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.

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Benchmarking cloud serving systems with YCSB

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

We present our experiences to date building ONOS (Open Network Operating System), an experimental distributed SDN control platform motivated by the performance, scalability, and availability requirements of large operator networks. We describe and evaluate two ONOS prototypes. The first version implemented core features: a distributed, but logically centralized, global network view; scale-out; and fault tolerance. The second version focused on improving performance. Based on experience with these prototypes, we identify additional steps that will be required for ONOS to support use cases such as core network traffic engineering and scheduling, and to become a usable open source, distributed network OS platform that the SDN community can build upon.

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ONOS: towards an open, distributed SDN OS

Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.

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Windows Azure Storage: a highly available cloud storage service with strong consistency

Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different data centers). At this scale, small and large components fail continuously. The way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. While in many ways Cassandra resembles a database and shares many design and implementation strategies therewith, Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format. Cassandra system was designed to run on cheap commodity hardware and handle high write throughput while not sacrificing read efficiency.

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Cassandra: a decentralized structured storage system

Cassandra is a distributed storage system for managing structured data that is designed to scale to a very large size across many commodity servers, with no single point of failure. Reliability at massive scale is a very big challenge. Outages in the service can have significant negative impact. Hence Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different datacenters). At this scale, small and large components fail continuously; the way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. Cassandra has achieved several goals--scalability, high performance, high availability and applicability. In many ways Cassandra resembles a database and shares many design and implementation strategies with databases. Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format.

Cassandra: structured storage system on a P2P network

Cassandra is a distributed storage system for managing structured data that is designed to scale to a very large size across many commodity servers, with no single point of failure. Reliability at massive scale is a very big challenge. Outages in the service can have significant negative impact. Hence Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different datacenters). At this scale, small and large components fail continuously; the way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. Cassandra has achieved several goals -- scalability, high performance, high availability and applicability. In many ways Cassandra resembles a database and shares many design and implementation strategies with databases. Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format.

Cassandra: a structured storage system on a P2P network

A messaging system that organizes and presents messages to users of the messaging system. The messaging system includes a mailbox module that receives messages and generates conversation threads from the messages. Messages received from different electronic message channels may be aggregated into a common conversation thread. The conversation threads are also organized in a message interface and then presented to a user of the messaging system. The messaging system may include a social networking system that stores social information about users, which can be used to organize the conversation threads. Other features include a multi mode message interface, and a method for adding contextual information to messages.

Organizing Conversation Threads Based on Social Information

A messaging system receives a message that is intended for a recipient, who is a user of a social networking system. An object associated with the message is identified based on the message content and/or metadata associated with the message. The messaging system accesses a social networking system database to obtain social information about a relationship between the recipient and the identified object. The message is then indexed using the social information, wherein an entry for the message is stored in a message index based on the social information. This process may be repeated for each received message, and multiple different message indexes may be generated for received messages to track different types of social information. A user interface for viewing messages uses the message index to organize the messages, such as to filter, categorize, and/or sort the messages in the display.

Prashant Malik

Papers

Cassandra: a decentralized structured storage system

Cassandra: structured storage system on a P2P network

Cassandra: a structured storage system on a P2P network

Organizing Conversation Threads Based on Social Information

Indexing and organizing messages in a messaging system using social network information