SIMMON

Abstract: Automated formula manipulation is shown to be central to object-oriented continuous-system modeling. Such techniques are needed to (i) solve the causality assignment problem in modeling any kind of energy transducer, (ii) generate the equations that result from the couplings between different objects, (iii) automatically reduce structurally singular models, and (iv) take care of algebraic loops that often result from subsystem couplings, and that also occur from the reduction of structurally singular models. A new tool, Dymola 2.0, is presented that implements all of the aforementioned formula manipulation techniques and can be used to generate state-space models in a variety of different simulation languages (ACSL, DESIRE, and Simmon). >

Abstract: Monitoring is a precondition for intelligent management in cloud computing environment, such as dynamic resource allocation. Typically, working as an auxiliary tool, a monitoring system is expected to incur the least additional resource usage, thus the strategies to improve the efficiency of monitoring mechanisms become significant. Yet the scale and monitoring requirement of different data centers vary, we cannot determine whether a monitoring mechanism would work well in a new data center before it serves the data center. To evaluate monitoring mechanisms, we propose SimMon, a toolkit for simulating monitoring mechanisms in cloud computing environments. SimMon is designed to simulate the topologies, actions, and strategies in data collection, dissemination, storage, and management processes. SimMon provides a controllable and repeatable way to evaluate monitoring mechanisms. In this paper, we describe the requirements analysis, design, implementation, and evaluation of SimMon. We simulate several different monitoring systems and compare their cost on time and resource to evaluate the efficiency of SimMon. We reproduce two usage scenarios from former literatures to demonstrate the effectiveness of SimMon on monitoring mechanisms simulation and evaluation. We build a real‐world working environment to validate the capability of SimMon on mimicking the characteristics of cloud monitoring systems. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract: Monitoring is significant to supervise the state of services and guide adaptive management of services in cloud computing environments. Working as auxiliary tools, monitoring systems are expected to incur the least extra cost on physical resources (CPU, memory, network, etc.). Since the scale and requirement of different data centers vary from each other, it is impossible to design a suit-to-all monitoring solution for all the data centers. However, for a certain data center, it is hard to determine whether a predesign monitoring mechanism is well suited before the mechanism is deployed in a real production environment. To address these issues, we propose SimMon, a toolkit for simulating monitoring mechanism in cloud computing environments. SimMon is used to simulate the process on collection, dissemination, storage and requisition of monitoring data. With the help of SimMon, system administrators are able to compare different monitoring mechanisms and select the best one before it is adopted by a monitoring system in a real-world data center.