Container-based load balancing for energy efficiency in software-defined edge computing environment

The workload generated by the Internet of Things (IoT)-based infrastructure is often handled by the cloud data centers (DCs). However, in recent time, an exponential increase in the deployment of the IoT-based infrastructure has escalated the workload on the DCs. So, these DCs are not fully capable to meet the strict demand of IoT devices in regard to the lower latency as well as high data rate while provisioning IoT workloads. Therefore, to reinforce the latency-sensitive workloads, an intersection layer known as edge computing has successfully balanced the entire service provisioning landscape. In this IoT-edge-cloud ecosystem, large number of interactions and data transmissions among different layer can increase the load on underlying network infrastructure. So, software-defined edge computing has emerged as a viable solution to resolve these latency-sensitive workload issues. Additionally, energy consumption has been witnessed as a major challenge in resource-constrained edge systems. The existing solutions are not fully compatible in Software-defined Edge ecosystem for handling IoT workloads with an optimal trade-off between energy-efficiency and latency. Hence, this article proposes a lightweight and energy-efficient container-as-a-service (CaaS) approach based on the software-define edge computing to provision the workloads generated from the latency-sensitive IoT applications. A Stackelberg game is formulated for a two-period resource allocation between end-user/IoT devices and Edge devices considering the service level agreement. Furthermore, an energy-efficient ensemble for container allocation, consolidation and migration is also designed for load balancing in software-defined edge computing environment. The proposed approach is validated through a simulated environment with respect to CPU serve time, network serve time, overall delay, lastly energy consumption. The results obtained show the superiority of the proposed in comparison to the existing variants

GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds

Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators. The optimisation of energy consumption in both cloud computational clusters and computing servers is usually related to scheduling problems. The definition of an optimal scheduling policy which does not negatively impact to system performance and task completion time is still challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE, which implements a scheduling model based on the Stackelberg game. This game presents two main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained results show that the Stackelberg cloud scheduler performs better than static energy-optimisation strategies and can achieve a fair balance between low energy consumption and short makespan in a very short tim

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

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

A bibliometric analysis and review of resource management in internet of water things : the use of game theory

DATA AVAILABILITY STATEMENT : The data presented in this study are openly available in Mendeley Data at doi:10.17632/2wxgbcxn3t.1.To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.The University of Pretoria.https://www.mdpi.com/journal/wateram2023Electrical, Electronic and Computer Engineerin

Software-Defined Networks for Resource Allocation in Cloud Computing: A Survey

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user's quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve