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Nowadays, Big Data becomes a research focus in industrial, banking, social network, and other fields. In addition, the explosive increase of data and information require efficient processing solutions. Therefore, Spark is considered as a promising candidate of Large-Scale Distributed Computing Systems for big data processing. One primary challenge is the straggler problem that occurred due to the presence of heterogeneity where a machine takes an extra-long time to finish execution of a task, which decreases the system throughput. To mitigate straggler tasks, Spark adopts speculative execution mechanism, in which the scheduler launches additional backup to avoid slow task processing and achieve acceleration. In this paper, a new Optimized Straggler Mitigation Framework is proposed. The proposed framework uses a dynamic criterion to determine the closest straggler tasks. This criterion is based on multiple coefficients to achieve a reliable straggler decision. Also, it integrates the historical data analysis and online adaptation for intelligent straggler judgment. This guarantees the effectiveness of speculative tasks by improving cluster performance. Experimental results on various benchmarks and applications show that the proposed framework achieves 23.5% to 30.7% execution time reductions, and 25.4 to 46.3% increase of the cluster throughputs compared with spark engine.

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The advent of mobile devices becomes the way for various technological developments in mobile communication and information technology. However, mobile users expect to access computational intensive applications through resource constrained mobile devices. Consequently the growing demands for boosting the computations, storage and memory resources became essential for mobile devices. A new trend to incorporate mobile devices and cloud resources with the existence of the network connectivity is named as mobile cloud computing (MCC). MCC is the greatest solution to increase the application processing capabilities on mobile devices by migrating the application to the cloud servers with on demand and unlimited resources. This paper proposes a new energy-preserving cloud offloading algorithm. The proposed algorithm estimates the application computational time and uses multiple weighted parameters to give accurate offloading decisions. Simulation results on various applications clarify that the proposed algorithm is capable of estimating an application's computation time with high correlations compared with the real execution time. This improves the offloading decision which actually preserves the energy and reduces the execution time of mobile applications.

The advent of mobile devices becomes the way for various technological developments in mobile communication and information technology. However, mobile users expect to access computational intensive applications through resource constrained mobile devices. Consequently the growing demands for boosting the computations, storage and memory resources became essential for mobile devices. A new trend to incorporate mobile devices and cloud resources with the existence of the network connectivity is named as mobile cloud computing (MCC). MCC is the greatest solution to increase the application processing capabilities on mobile devices by migrating the application to the cloud servers with on demand and unlimited resources. This paper proposes a new energy-preserving cloud offloading algorithm. The proposed algorithm estimates the application computational time and uses multiple weighted parameters to give accurate offloading decisions. Simulation results on various applications clarify that the proposed algorithm is capable of estimating an application's computation time with high correlations compared with the real execution time. This improves the offloading decision which actually preserves the energy and reduces the execution time of mobile applications.