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Big data analytics is an indispensable tool in transforming science, engineering, medicine, health-care, finance and ultimately business itself. With the explosion of data sizes and need for shorter time-to-solution, in-memory platforms such as Apache Spark gain increasing popularity. In this context, data shuffling, a particularly difficult transformation pattern, introduces important challenges...
The understanding of application characteristics such as hardware resource requirements and communication patterns is key in building highly utilized high performance computing systems for target workloads at a reasonable cost and with available technology. The characterization drives the design decision of both hardware and software. Memory access pattern is a key factor as data movement is a major...
Energy efficiency features are being integrated in network protocols and management systems. Simulations can provide input on how a particular algorithm would perform in different network conditions. However, building an environment that is able to comprehensively account for interactions between different network functions is difficult A combination of emulation and implementation of major energy...
Sustainable computer networking approaches adapt network node power states dynamically in response to traffic demand. This scenario imposes new challenges to the way the network resilience is evaluated based on reliability and availability metrics. The traditional assessments of reliability and availability based on Markov models or the Cut-Set and Tie-Set techniques are based on static values and...
Considering the increasing importance of carbon free networking solutions, several energy-efficient networking systems have been proposed and evaluated in literature. One significant challenge is how to encompass actions targeting energy savings and possible corresponding degradations in network QoS (Quality of Service). This paper presents an energy-efficient policy-based network management architecture...
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