Spark has garnered significant attention owing to its superior capability in processing vast amounts of data. Its executor-based model necessitates that sufficient resources are allocated to executors, thus facilitating the executions of diverse tasks. These tasks often exhibit different resource consumption patterns, resulting in time-varying resource consumption concerning CPU, network resources, etc. Consequently, this often leads to resource wastage. Some research endeavors have aimed to optimize the performance of Spark applications with time-varying CPU consumption. Their strategies primarily involve adjusting the number of executors dynamically. However, these efforts often overlook another crucial time-varying resource, namely, network bandwidth usage.To address this issue, this paper proposes a novel pre-transmission approach to optimize inter-stage communication in Spark. In essence, this approach leverages idle network bandwidth during the runtime of an upstream stage to initiate the transmission of outputs of its tasks in advance, which subsequently serve as the exactly input data of the tasks of corresponding downstream stages. Consequently, these tasks’ execution times are considerably reduced as most of their input data have already been in local nodes due to the pre-transmission approach. This leads to an overall enhancement in the performance of Spark applications. Our approach makes significant modifications to Spark’s internal working mechanism to achieve the pre-transmission, including extensions of the Spark Scheduler and Executor. Our experiments conducted utilizing the HiBench benchmark demonstrate that our approach can boost Spark applications’ performance by up to 31.3%, in comparison to the native Spark.

The Internet of medical things (IoMT) has become a promising paradigm, where the invaluable additional data can be collected by the ordinary medical devices when connecting to the Internet. The deep understanding of symptoms and trends can be provided to patients to manage their lives and treatments. However, due to the diversity of medical devices in IoMT, the codes of healthcare applications may be manipulated and tangled by malicious devices. In addition, the linguistic structures for layer activation in languages cause controls of layer activation to be part of program’s business logic, which hinders the dynamic replacement of layers. Therefore, to solve the above critical problems in IoMT, in this paper, a new approach is firstly proposed to support the dynamic replacement of layer in IoMT applications by incorporating object proxy into virtual machine (VM). Secondly, the heap and address are used to model the object and object evolution to guarantee the feasibility of the approach. After that, we analyze the influences of field access and method invocation and evaluate the risk and safety of the application when these constraints are satisfied. Finally, we conduct the evaluations by extending Java VM to validate the effectiveness of the proposal.

Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, other recommender system utilizes different particular type information by combining different techniques to improve the quality of the recommendation. This paper proposed a novel personalized recommendation method that utilizes semi-supervised clustering based Gaussian mixture model, which provides a hybrid recommender method by combining demographic method and user-based collaborative filtering method. The result from various simulations using MovieLens data set shows that the proposed recommender method performs better and helps to improve the quality of recommendation rating.