IoT-Fog-Edge-Cloud Computing Simulation Tools, A Systematic Review

The Internet of Things (IoT) perspective promises substantial advancements in sectors such as smart homes and infrastructure, smart health, smart environmental conditions, smart cities, energy, transportation and mobility, manufacturing and retail, farming, and so on. Cloud computing (CC) offers appealing computational and storage options; nevertheless, cloud-based explanations are frequently conveyed by downsides and constraints, such as energy consumption, latency, privacy, and bandwidth. To address the shortcomings related to CC, the advancements like Fog Computing (FC) and Edge Computing (EC) are introduced later on. FC is a novel and developing technology that connects the cloud to the network edges, allowing for decentrali zed computation. EC, in which processing and storage are performed nearer to where data is created, may be able to assist address these issues by satisfying particular needs such as low latency or lower energy use. This study provides a comprehensive overview and analysis of IoT-Fog-Edge-Cloud Computing simulation tools to assist researchers and developers in selecting the appropriate device for research studies while working through various scenarios and addressing current reality challenges. This study also takes a close look at various modeling tools, which are examined and contrasted to improve the future

A Novel Framework for Simulating Computing Infrastructure and Network Data Flows Targeted on Cloud Computing

Understanding how computing infrastructure decisions influence overall performance and cost can be very difficult. Simulation techniques are an important tool to help analyse and evaluate different infrastructure configurations and deployment scenarios. As diversity of cloud computing resources is growing, the space of possible infrastructure configurations becomes larger and finding the best solution becomes harder. In our previous paper, we presented a framework to benchmark cloud resources to obtain objective and comparable results. Based on those results, our simulation framework allows to model applications and to estimate their performance. Compared to other infrastructure or cloud simulation tools our framework excels when it comes to the simulation of network data flows

Holistic energy and failure aware workload scheduling in Cloud datacenters

The global uptake of Cloud computing has attracted increased interest within both academia and industry resulting in the formation of large-scale and complex distributed systems. This has led to increased failure occurrence within computing systems that induce substantial negative impact upon system performance and task reliability perceived by users. Such systems also consume vast quantities of power, resulting in significant operational costs perceived by providers. Virtualization – a commonly deployed technology within Cloud datacenters – can enable flexible scheduling of virtual machines to maximize system reliability and energy-efficiency. However, existing work address these two objectives separately, providing limited understanding towards studying the explicit trade-offs towards dependable and energy-efficient compute infrastructure. In this paper, we propose two failure-aware energy-efficient scheduling algorithms that exploit the holistic operational characteristics of the Cloud datacenter comprising the cooling unit, computing infrastructure and server failures. By comprehensively modeling the power and failure profiles of a Cloud datacenter, we propose workload scheduling algorithms Ella-W and Ella-B, capable of reducing cooling and compute energy while minimizing the impact of system failures. A novel and overall metric is proposed that combines energy efficiency and reliability to specify the performance of various algorithms. We evaluate our algorithms against Random, MaxUtil, TASA, MTTE and OBFIT under various system conditions of failure prediction accuracy and workload intensity. Evaluation results demonstrate that Ella-W can reduce energy usage by 29.5% and improve task completion rate by 3.6%, while Ella-B reduces energy usage by 32.7% with no degradation to task completion rate