Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments
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Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architecture features key characteristics (such as robustness, fault tolerance, scalability, generalization, extensibility, ad-hoc queries, minimal maintenance, and low-latency reads and updates) to cope with this complexity. The paper at hand suggest a basic set of strategies to handle the arising challenges regarding the volatility, heterogeneity, and desired low latency execution by reducing the overall system timing (scheduling, execution, monitoring, and faults recovery) as well as possible faults (churn, no answers to executions). The proposed strategies make use of services such as migration, replication, MapReduce simulation, and combined processing methods (batch- and streaming-based). Via these services, a distribution of tasks for the best balance of computational resources is achieved, while monitoring and management can be performed asynchronously in the background. stream-based methods are proposed to reduce the latency.
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