6,928 research outputs found
Scalable Persistent Storage for Erlang
The many core revolution makes scalability a key property. The RELEASE project aims to improve the scalability of Erlang on emergent commodity architectures with 100,000 cores. Such architectures require scalable and available persistent storage on up to 100 hosts. We enumerate the requirements for scalable and available persistent storage, and evaluate four popular Erlang DBMSs against these requirements. This analysis shows that Mnesia and CouchDB are not suitable persistent storage at our target scale, but Dynamo-like NoSQL DataBase Management Systems (DBMSs) such as Cassandra and Riak potentially are. We investigate the current scalability limits of the Riak 1.1.1 NoSQL DBMS in practice on a 100-node cluster. We establish for the first time scientifically the scalability limit of Riak as 60 nodes on the Kalkyl cluster, thereby confirming developer folklore. We show that resources like memory, disk, and network do not limit the scalability of Riak. By instrumenting Erlang/OTP and Riak libraries we identify a specific Riak functionality that limits scalability. We outline how later releases of Riak are refactored to eliminate the scalability bottlenecks. We conclude that Dynamo-style NoSQL DBMSs provide scalable and available persistent storage for Erlang in general, and for our RELEASE target architecture in particular
Grid-Brick Event Processing Framework in GEPS
Experiments like ATLAS at LHC involve a scale of computing and data
management that greatly exceeds the capability of existing systems, making it
necessary to resort to Grid-based Parallel Event Processing Systems (GEPS).
Traditional Grid systems concentrate the data in central data servers which
have to be accessed by many nodes each time an analysis or processing job
starts. These systems require very powerful central data servers and make
little use of the distributed disk space that is available in commodity
computers. The Grid-Brick system, which is described in this paper, follows a
different approach. The data storage is split among all grid nodes having each
one a piece of the whole information. Users submit queries and the system will
distribute the tasks through all the nodes and retrieve the result, merging
them together in the Job Submit Server. The main advantage of using this system
is the huge scalability it provides, while its biggest disadvantage appears in
the case of failure of one of the nodes. A workaround for this problem involves
data replication or backup.Comment: 6 pages; document for CHEP'03 conferenc
Control versus Data Flow in Parallel Database Machines
The execution of a query in a parallel database machine can be controlled in either a control flow way, or in a data flow way. In the former case a single system node controls the entire query execution. In the latter case the processes that execute the query, although possibly running on different nodes of the system, trigger each other. Lately, many database research projects focus on data flow control since it should enhance response times and throughput. The authors study control versus data flow with regard to controlling the execution of database queries. An analytical model is used to compare control and data flow in order to gain insights into the question which mechanism is better under which circumstances. Also, some systems using data flow techniques are described, and the authors investigate to which degree they are really data flow. The results show that for particular types of queries data flow is very attractive, since it reduces the number of control messages and balances these messages over the node
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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