Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

SCORE: Simulator for cloud optimization of resources andenergy consumption

Achieving efficiency both in terms of resource utilisation and energy consumption is acomplex challenge, especially in large-scale wide-purpose data centers that serve cloud- computing services. Simulation presents an appropriate solution for the development andtesting of strategies that aim to improve efficiency problems before their applications inproduction environments. Various cloud simulators have been proposed to cover differentaspects of the operation environment of cloud-computing systems. In this paper, we define the SCORE tool, which is dedicated to the simulation of energy-efficient monolithicand parallel-scheduling models and for the execution of heterogeneous, realistic and synthetic workloads. The simulator has been evaluated through empirical tests. The results ofthe experiments confirm that SCORE is a performant and reliable tool for testing energy- efficiency, security, and scheduling strategies in cloud-computing environments.European Cooperation in Science and Technology (COST) COST Action IC140

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Energy and performance-aware scheduling and shut-down models for efficient cloud-computing data centers.

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed.Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energética, así como tres gestores de recursos centralizados: Monolítico, Two-level y Shared-state. Como resultados, esta herramienta de simulación arroja más de 50 Key Performance Indicators (KPI) de rendimiento general, de distribucin de tareas y de energía.Premio Extraordinario de Doctorado U

SGA Model for Prediction in Cloud Environment

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments

Simulating fog and edge computing scenarios: an overview and research challenges

The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous endpoints located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Heterogeneity, High Performance Computing, Self-Organization and the Cloud

application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa