AU
Alexandru Uta
Authored
9 records found
Exploring HPC and Big Data Convergence
A Graph Processing Study on Intel Knights Landing
The question 'Can big data and HPC infrastructure converge?' has important implications for many operators and clients of modern computing. However, answering it is challenging. The hardware is currently different, and fast evolving: big data uses machines with modest numbers of
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Serverless is More
From PaaS to Present Cloud Computing
In the late-1950s, leasing time on an IBM 704 cost hundreds of dollars per minute. Today, cloud computing, that is, using IT as a service, on-demand and pay-per-use, is a widely used computing paradigm that offers large economies of scale. Born from a need to make platform as ...
Elasticity in Graph Analytics?
A Benchmarking Framework for Elastic Graph Processing
Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the diversity of graph data and algorithms, many parallel and distributed graph-processing systems exist. However, until now these plat
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Massivizing computer systems
A vision to understand, design, and engineer computer ecosystems through and beyond modern distributed systems
Our society is digital: industry, science, governance, and individuals depend, often transparently, on the inter-operation of large numbers of distributed computer systems. Although the society takes them almost for granted, these computer ecosystems are not available for all, ma
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Massivizing computer systems
A vision to understand, design, and engineer computer ecosystems through and beyond modern distributed systems
Our society is digital: industry, science, governance, and individuals depend, often transparently, on the inter-operation of large numbers of distributed computer systems. Although the society takes them almost for granted, these computer ecosystems are not available for all, ma
...
Massivizing computer systems
A vision to understand, design, and engineer computer ecosystems through and beyond modern distributed systems
Our society is digital: industry, science, governance, and individuals depend, often transparently, on the inter-operation of large numbers of distributed computer systems. Although the society takes them almost for granted, these computer ecosystems are not available for all, ma
...
Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability when running experiments in the cloud. Fo
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Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the variety of graph data and algorithms, many parallel and distributed graph processing systems exist. However, until now these platfo
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Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets
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Contributed
1 records found
POSUM
A Generic Portfolio Scheduler for MapReduce Workloads
MapReduce ecosystems are (still) widely popular for big data processing in data centers. To address the diverse non-functional requirements arising from many and increasingly more sophisticated users, the community has developed many scheduling policies for MapReduce workloads. A
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