Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Low latency fast data computation scheme for map reduce based clusters

MapReduce based clusters is an emerging paradigm for big data analytics to scale up and speed up the big data classification, investigation, and processing of the huge volumes, massive and complex data sets. One of the fundamental issues of processing the data in MapReduce clusters is to deal with resource heterogeneity, especially when there is data inter-dependency among the tasks. Secondly, MapReduce runs a job in many phases; the intermediate data traffic and its migration time become a major bottleneck for the computation of jobs which produces a huge intermediate data in the shuffle phase. Further, encountering factors to monitor the critical issue of straggling is necessary because it produces unnecessary delays and poses a serious constraint on the overall performance of the system. Thus, this research aims to provide a low latency fast data computation scheme which introduces three algorithms to handle interdependent task computation among heterogeneous resources, reducing intermediate data traffic with its migration time and monitoring and modelling job straggling factors. This research has developed a Low Latency and Computational Cost based Tasks Scheduling (LLCC-TS) algorithm of interdependent tasks on heterogeneous resources by encountering priority to provide cost-effective resource utilization and reduced makespan. Furthermore, an Aggregation and Partition based Accelerated Intermediate Data Migration (APAIDM) algorithm has been presented to reduce the intermediate data traffic and data migration time in the shuffle phase by using aggregators and custom partitioner. Moreover, MapReduce Total Execution Time Prediction (MTETP) scheme for MapReduce job computation with inclusion of the factors which affect the job computation time has been produced using machine learning technique (linear regression) in order to monitor the job straggling and minimize the latency. LLCCTS algorithm has 66.13%, 22.23%, 43.53%, and 44.74% performance improvement rate over FIFO, improved max-min, SJF and MOS algorithms respectively for makespan time of scheduling of interdependent tasks. The AP-AIDM algorithm scored 66.62% and 48.4% performance improvements in reducing the data migration time over hash basic and conventional aggregation algorithms, respectively. Moreover, an MTETP technique shows the performance improvement in predicting the total job execution time with 20.42% accuracy than the improved HP technique. Thus, the combination of the three algorithms mentioned above provides a low latency fast data computation scheme for MapReduce based clusters