A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment

Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.

A PDEVS simulator supporting multiple synchronization protocols: implementation and performance analysis

With the ever-increasing complexity of simulation models, parallel simulation becomes necessary to perform simulation within reasonable time bounds. The built-in parallelism of Parallel DEVS is often insufficient to tackle this problem on its own. Several synchronization protocols have been proposed, each with their distinct advantages and disadvantages. Due to the significantly different implementation of these protocols, most Parallel DEVS simulation tools are limited to only one such protocol. In this paper, we present a Parallel DEVS simulator, grafted on C++11 and based on PythonPDEVS, supporting both conservative and optimistic synchronization protocols. The simulator not only supports both protocols but also has the capability to switch between them at runtime. The simulator can combine each synchronization protocols with either a threaded or sequential implementation of the PDEVS protocol. We evaluate the performance gain obtained by choosing the most appropriate synchronization protocol. A comparison is made to adevs in terms of CPU time and memory usage, to show that our modularity does not hinder performance. We compare the speedup obtained by synchronization with that of the inherent parallelism of PDEVS in isolation and combination, and contrast the results with the theoretical limits. We further allow for an external component to gather simulation statistics, on which runtime switching between the different synchronization protocols can be based. The effects of allocation on our synchronization protocols are also studied.

The Cluster Design of MMOG Servers on Mobile Platform

Synchronization and load balance are the primary problems faced by MMOG server cluster. For synchronization issue, system uses an improved algorithm for optimistic synchronization, at the same time for the contradiction in MMOG between the growing requirements of games resource and the limited load capacity of servers, propose an efficient load balancing algorithm. Experimental results show that the cluster system has higher performance and load balancing capabilities.