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Summary: Los Alamos National Laboratory has deployed (over the last year and a half) a pair of Cray Shasta machines—a development testbed named Guaje and and production machine named Chicoma, which will soon comprise the bulk of LANL's open science research computing portfolio. In the process, we have encountered a number of problems and challenges in several realms—authentication and authorization, cluster health management, image management, and configuration management. Both independently and in collaboration with Cray/HPE, we have found solutions and brought the system into stable production. The presentation will discuss the solutions and how they came about, and issues we are working to resolve in the near future. [ABSTRACT FROM AUTHOR]

The recent introduction of full‐mesh virtual private network (VPN) solutions which offer near native performance, coupled with modern encryption algorithms and easy scalability as a result of a central control plane have a strong potential to enable the implementation of a seamless edge‐cloud continuum. To test the performance of existing solutions in this domain, we present a framework consisted of both essential and optional features that full‐mesh VPN solutions need to support before they can be used for interconnecting geographically dispersed compute nodes. We then apply this framework on existing offerings and select three VPN solutions for further tests: Headscale, Netbird, and ZeroTier. We evaluate their features in the context of establishing an underlay network on top of which a Kubernetes overlay network can be created. We test pod‐to‐pod TCP and UDP throughput as well as Kubernetes application programming interface (API) response times, in multiple scenarios, accounting for adverse network conditions such as packet loss or packet delay. Based on the obtained measurement results and through analysis of the underlying strengths and weaknesses of the individual implementations, we draw conclusions on the preferred VPN solution depending on the use‐case at hand, striking a balance between usability and performance. [ABSTRACT FROM AUTHOR]

As the demand for container technology and platforms increases due to the efficiency of IT resources, various workloads are being containerized. Although there are efforts to integrate various workloads into Kubernetes, the most widely used container platform today, the nature of containers makes it challenging to support persistence for memory-centric workloads like in-memory databases. In this paper, we discuss the drawbacks of one of the persistence support methods used for in-memory databases in a Kubernetes environment, namely, the data snapshot. To address these issues, we propose a compromise solution of using container checkpoints. Through this approach, we can perform checkpointing without incurring additional memory usage due to CoW, which is a problem in fork-based data snapshots during snapshot creation. Additionally, container checkpointing induces up to 7.1 times less downtime compared to the main process-based data snapshot. Furthermore, during database recovery, it is possible to achieve up to 11.3 times faster recovery compared to the data snapshot method. [ABSTRACT FROM AUTHOR]

The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP. [ABSTRACT FROM AUTHOR]

Summary: Smart Farming has become a cornerstone of modern agriculture, offering data‐driven insights and automation that optimize resource utilization and increase crop yields. The use of cutting‐edge technologies in agriculture has given rise to Smart Farming, which has transformed traditional farming practices into efficient, data‐driven operations. This paper explores the synergy between high‐performance computing (HPC) systems, Kubernetes orchestration, GoogleNet architecture, and containerization to redefine the future of farming. At the heart of this transformation lies the GoogleNet architecture, a deep learning powerhouse recognized for its efficiency and accuracy in image recognition tasks. The orchestration capabilities of Kubernetes, a versatile tool for managing containerized workloads efficiently on HPC clusters. Hence, in this work, we investigate the intricacies of deploying GoogleNet‐based deep learning models within containerized environments orchestrated by Kubernetes on HPC infrastructure. It explores resource optimization, scalability, security, and adaptability, all tailored to the unique demands of the agricultural domain to evaluate the effectiveness of the given technique it is compared with the existing techniques namely Hermes, Horus, CYBELE, and RZ‐SHAN. The attained ranges of proposed method of various measures of accuracy, precision, recall, and F1‐score are 98.65%, 97.45%, 97.87%, and 98.12% for the Pilot Wheat Ear dataset. Also, the processing time for the proposed approach is 181.50 and 120.2 m for the Pilot Wheat Ear Dataset and the Pilot soya bean farming dataset. The latency of the proposed approach attains a lower value of 1.5 and 1.1 s pilot soya bean farming dataset and Pilot Wheat Ear dataset. The experimental outcome demonstrates the efficiency of the proposed approaches to improve Smart Farming agriculture. [ABSTRACT FROM AUTHOR]

With a shift from the regular in-class education to e-learning-based education, there is a need to have the right platform to conduct assessments and courses in a more effective and user-friendly manner which includes robust monitoring, capability of doubt clearing, easy sharing of reference materials and more. There is a need for a highly customizable lab which supports all languages and is very lightweight. Our solution focuses on the creation of a robust, lightweight lab that provides a plug-and-play model for on-demand compilation capabilities. This virtual lab is hosted on the in-house blade servers and virtualized using metal-as-a-service (maas) along with juju controller. Kubernetes is used for on-demand compilation support as well as serves as the load balancer for the servers. The lab is specific to Computer Science-related courses and aims to impact the students present in the low bandwidth and rural areas. Moreover, the proposed methodology enhances the capabilities of the institutions to utilize their resources to the fullest with the help of virtualization through maas, juju and open-stack and orchestration through Kubernetes. [ABSTRACT FROM AUTHOR]

Edge cloud technologies working in conjunction with AI-powered solutions can help surmount the challenges associated with the distributed execution of immersive services and contribute to delivering a positive end-user experience. Intelligent resource management, orchestration, and predictive systems can enhance service deployment, adapt to changing demands, and ensure seamless service operation. This paper introduces an innovative architectural paradigm that enables multi-domain edge orchestration for highly distributed immersive services by leveraging various AI solutions and technological tools to support multi-domain edge deployments. The proposed architecture is designed to function based on multi-level specification blueprints, separating high-level user-intent infrastructure definition from AI-driven orchestration and the final execution plan. This architectural design enables the incorporation of AI solutions to be conducted in a modular manner. Furthermore, the Application Management Framework provides a visual language and tool as an alternative to formal methods for creating intent blueprints. The proposed architecture is evaluated within the frame of an immersive virtual touring use-case scenario. [ABSTRACT FROM AUTHOR]

Microservices are containerized, loosely coupled, interactive smaller units of the application that can be deployed, reused, and maintained independently. In a microservices-based application, allocating the right computing resources for each containerized microservice is important to meet the specific performance requirements while minimizing the infrastructure cost. Microservices-based applications are easy to scale automatically based on incoming workload and resource demand automatically. However, it is challenging to identify the right amount of resources for containers hosting microservices and then allocate them dynamically during the auto-scaling. Existing auto-scaling solutions for microservices focus on identifying the appropriate time and number of containers to be added/removed dynamically for an application. However, they do not address the issue of selecting the right amount of resources, such as CPU cores, for individual containers during each scaling event. This paper presents a novel approach to dynamically allocate the CPU resources to the containerized microservice during the autoscaling events. Our proposed approach is based on the machine learning method, which can identify the right amount of CPU resources for each container, dynamically spawning for the microservices over time to satisfy the application's response time requirements. The proposed solution is evaluated using a benchmark microservices-based application based on real-world workloads on the Kubernetes cluster. The experimental results show that the proposed solution outperforms by yielding a 40% to 60% reduction in violating the response time requirements with 0.5 × to 1.5 × less cost compared to the state-of-art baseline methods. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing in Internet of Things (IoT) applications has become widespread. However, it presents challenges for latency-sensitive scenarios due to data transmission to the centralized cloud structure, which leads to increased network traffic and service delays. To address this, fog computing has emerged as an intermediary layer between the cloud and IoT, ensuring low-latency interactions. A pivotal challenge within the fog computing paradigm is the service placement problem, involving assigning services to appropriate nodes, which is recognized as NP-hard. Recognizing the intricate nature of service placement, this study introduces a multi-objective optimization approach tailored for dynamic service placement within container-based fog computing environments. Considering multiple objectives is imperative due to the complex interplay of performance metrics in fog computing scenarios. A two-tier resource management framework based on Kubernetes is proposed to manage these diverse yet interrelated objectives effectively. The framework harnesses the power of the multi-objective, non-dominated sorting genetic algorithm II (NSGA-II) to reconcile conflicting objectives and facilitate optimal service placement decisions. Incorporating multi-objective optimization enables a comprehensive evaluation of service placement solutions, ensuring a balanced trade-off between latency, cost-efficiency, and energy consumption. Empirical evaluations demonstrate that the proposed method improves cost, average latency time, and energy consumption by 8% to 36% compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

MotionInsights facilitates object detection and tracking from multiple video streams in real-time. Leveraging the distributed stream processing capabilities of Apache Flink and Apache Kafka (as an intermediate message broker), the system models video processing as a data flow stream processing pipeline. Each video frame is split into smaller blocks, which are dispatched to be processed in parallel by a number of Flink operators. In the first stage, each block undergoes background subtraction and component labeling. The connected components from each frame are grouped, and the eligible components are merged into objects. In the last stage of the pipeline, all objects from each frame are concentrated to produce the trajectory of each object. The Flink application is deployed as a Kubernetes cluster in the Google Cloud Platform. Experimenting in a Flink cluster with 7 machines, revealed that MotionInsights achieves up to 6 times speedup compared to a monolithic (nonparallel) implementation while providing accurate trajectory patterns. The highest (i.e., more than 6 times) speed-up was observed with video streams of the highest resolution. Compared to existing systems that use custom or proprietary architectures, MotionInsights is independent of the underlying hardware platform and can be deployed on common CPU architectures and the cloud. [ABSTRACT FROM AUTHOR]

This work describes an approach to enhance container orchestration platforms with an autonomous and dynamic rescheduling system that aims at improving application service time by co-locating highly interdependent containers for network delay reduction. Unreasonable container consolidation may however lead to host CPU saturation, in turn impairing the service time. The multiobjective approach proposed in this work aims to improve application service-time by minimizing both inter-server network traffic and CPU throttling on overloaded servers. To this extent, the Simulated Annealing combinatorial optimization heuristic is used and compared on its relative performance towards the optimal solution obtained by Mathematical Programming. Additionally, the impact of the proposed system is validated on a Kubernetes cluster hosting three concurrent applications, and this under varying load scenarios. The proposed rescheduling system systematically i) improves the application service-time (up to 27.2% from our experiments) and ii) surpasses the improvement reached by the Kubernetes descheduler. [ABSTRACT FROM AUTHOR]

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

A recent development in cloud computing has introduced serverless technology, enabling the convenient and flexible management of cloud-native applications. Typically, the Function-as-a-Service (FaaS) solutions rely on serverless backend solutions, such as Kubernetes (K8s) and Knative, to leverage the advantages of resource management for underlying containerized contexts, including auto-scaling and pod scheduling. To take the advantages, recent cloud service providers also deploy self-hosted serverless services by facilitating their on-premise hosted FaaS platforms rather than relying on commercial public cloud offerings. However, the lack of standardized guidelines on K8s abstraction to fairly schedule and allocate resources on auto-scaling configuration options for such on-premise hosting environment in serverless computing poses challenges in meeting the service level objectives (SLOs) of diverse workloads. This study fills this gap by exploring the relationship between auto-scaling behavior and the performance of FaaS workloads depending on scaling-related configurations in K8s. Based on comprehensive measurement studies, we derived the logic as to which workload should be applied and with what type of scaling configurations, such as base metric, threshold to maximize the difference in latency SLO, and number of responses. Additionally, we propose a methodology to assess the scaling efficiency of the related K8s configurations regarding the quality of service (QoS) of FaaS workloads. [ABSTRACT FROM AUTHOR]

Most existing autoscaling approaches help to avoid violating the performance-related Service Level Objectives (SLO). However, these solutions do not aim to recover the SLO. The proposed novel autoscaling solution covers both SLO violation avoidance and recovery. The SLO failure avoidance part of the solution aims to avoid SLO violations by adjusting autoscaling thresholds based on compliance with SLO. It also dynamically selects the required CPU threshold, cooldown intervals and the number of replicas based on load velocity. The recovery part of the solution aims to recover SLO by additional resource provisioning if SLO is violated due to a delay or resource underestimation. The proposed implementation of the solution targets embarrassingly parallel workloads. It is compared with two autoscaling solutions in five workload scenarios, evaluating the ability of the solution to operate close to the defined SLO values. The results show that the proposed solution provides sufficient resources to support or recover performance-based SLOs. [ABSTRACT FROM AUTHOR]

Traditional container orchestration platforms often suffer from resource wastage in educational settings, and stateless serverless services face challenges in maintaining container state persistence during the teaching process. To address these issues, we propose a stateful serverless mechanism based on Containerd and Kubernetes, focusing on optimizing the startup process for container groups. We first implement a checkpoint/restore framework for container states, providing fundamental support for managing stateful containers. Building on this foundation, we propose the concept of "container groups" to address the challenges in educational practice scenarios characterized by a large number of similar containers on the same node. We then propose the Rofuse optimization mechanism, which employs delayed loading and block-level deduplication techniques. This enables containers within the same group to reuse locally cached file system data at the block level, thus reducing container restart latency. Experimental results demonstrate that our stateful serverless mechanism can run smoothly in typical educational practice scenarios, and Rofuse reduces the container restart time by approximately 50% compared to existing solutions. This research provides valuable exploration for serverless practices in the education domain, contributing new perspectives and methods to improve resource utilization efficiency and flexibility in teaching environments. [ABSTRACT FROM AUTHOR]

Large-scale photovoltaic (PV) electricity production plants rely on reliable operation and maintenance (O&M) systems, often operated by means of supervisory control and data acquisition (SCADA) platforms aimed at limiting, as much as possible, the intrinsic volatility of this energy resource. The current trend is to develop SCADAs that achieve the finest possible control of the system components to efficiently and effectively cope with possible energy delivery problems. In this study, we investigated an innovative design of an IoT-based SCADA specifically tailored for large PV systems in which data transmission overheads are reduced by adopting lightweight protocols, and reliable data storage is achieved by means of hybrid solutions that allow the storage of historical data, enabling accurate performance analysis and predictive maintenance protocols. The proposed solution relies on an architecture where independent functional microservices handle specific tasks, ensuring scalability and fault tolerance. The technical approaches for IoT-SCADA connectivity are herein described in detail, comparing different possible technical choices. The proposed IoT-based SCADA is based on edge computing for latency reduction and to enhance real-time decision making, enabling scalability, and centralized management while leveraging cloud services. The resulting hybrid solutions that combine edge and cloud resources offer a balance between responsiveness and scalability. Finally, in the study, a blockchain solution was taken into account to certify energy data, ensuring traceability, security, and reliability in commercial transactions. [ABSTRACT FROM AUTHOR]

Abstract As the demand for container technology and platforms increases due to the efficiency of IT resources, various workloads are being containerized. Although there are efforts to integrate various workloads into Kubernetes, the most widely used container platform today, the nature of containers makes it challenging to support persistence for memory-centric workloads like in-memory databases. In this paper, we discuss the drawbacks of one of the persistence support methods used for in-memory databases in a Kubernetes environment, namely, the data snapshot. To address these issues, we propose a compromise solution of using container checkpoints. Through this approach, we can perform checkpointing without incurring additional memory usage due to CoW, which is a problem in fork-based data snapshots during snapshot creation. Additionally, container checkpointing induces up to 7.1 times less downtime compared to the main process-based data snapshot. Furthermore, during database recovery, it is possible to achieve up to 11.3 times faster recovery compared to the data snapshot method.

Edge computing is an emerging paradigm that enables low-latency and high-performance computing at the network edge. However, effectively scheduling complex and interdependent tasks on heterogeneous and dynamic edge computing nodes presents a significant challenge in meeting users' real-time response requirements. To solve this problem, a DGCQN scheduling network that leverages reinforcement learning and graph convolutional neural networks to learn an optimal scheduling strategy is proposed in this paper. The proposed method embeds the graph structure of Directed Acyclic Graph (DAG) tasks and node information of Kubernetes (K8s) clusters into a Q value function, guiding the DQN network in selecting the best action at each step. The method is evaluated across various DAG tasks and edge computing scenarios. Compared with HEFT, DQN, and GOSU, the task completion time of the proposed method is reduced by about 20, 10, and 1.5%, respectively. The results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

La creciente relevancia de la contenerización y la computación en la nube destaca la necesidad de investigar áreas emergentes para abordar desafíos cruciales en la gestión de recursos informáticos. El objetivo de este artículo es identificar tendencias investigativas en el uso de Cloud Computing en contenerización entre 2015 y 2023. La metodología planteó un enfoque exploratorio, a partir de un estudio bibliométrico, usando las bases de datos Scopus y Web of Science, por medio de las cuales se obtuvieron 136 documentos académicos, analizados a través de una serie de indicadores bibliométricos de estructura. El estudio reveló resultados significativos para la agenda investigativa, abordando áreas cruciales de contenerización y computación en la nube, como Docker, Kubernetes, Edge Computing y Máquinas Virtuales. Además, destacó líderes en investigación y países influyentes en la producción científica, donde Estados Unidos y Francia se destacan en estos campos. Se observa el surgimiento de temas emergentes, como Evaluación del Rendimiento y Orquestación de Contenedores, señalando áreas de crecimiento en la investigación.

This research paper focuses on developing a complete system for daily automation testing of comprehensive web applications implemented on cloud environments, encompassing the execution of automated API tests, real-time monitoring and results visualization of the testing environments. Despite the tools for developing automated API tests, the study uses containerization tools as Docker and Kubernetes, showcasing their integration into a cohesive testing framework. Furthermore, the implementation leverages the potential of the Google Cloud Platform (GCP) to demonstrate the usage of cloud computing services, emphasizing scalability and efficiency. Additionally, the paper details the integration of monitoring tools, specifically Elasticsearch, to assess and visualize the health and performance of the underlying Kubernetes cluster. Through a comprehensive approach, encompassing a wide variety of tools, the research establishes a continuous and automated testing environment essential for cutting-edge software applications. Results showcase the successful orchestration of all the technologies, highlighting their collective impact on achieving a robust and efficient system for continuous automation testing and monitoring.

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With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research. [ABSTRACT FROM AUTHOR]

This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability. [ABSTRACT FROM AUTHOR]

Forensics investigation of WordPress applications hosted on containerized cloud environment is critically important due to their increased adaptation and an exponential increase in cybercrimes. Investigation of containerized environments is complex due to their volatile nature and dynamic allocation. This paper explores the forensic analysis of WordPress applications deployed inside a containerized environment. In this paper, we have used a log analysis approach to perform the digital forensics investigation of different attacks on WordPress applications running over Kubernetes. To investigate these events, we collected a large number of operational logs generated by these applications. Log filtrations techniques are applied to analyze only the relevant logs for a specific event to perform the forensic investigation efficiently. The experimental results confirm the proposed approach's effectiveness as we could trace all the malicious events to conduct the digital investigation on the WordPress applications running inside Kubernetes. The scheme successfully identified the attacker's information like incident time, incident source, duration of attack etc. Moreover, the proposed solution also provided forensics readiness for the WordPress applications running in a container-based cloud. [ABSTRACT FROM AUTHOR]

Kubernetes is an open-source container orchestration system that provides a built-in module for dynamic resource provisioning named the Horizontal Pod Autoscaler (HPA). The HPA identifies the number of resources to be provisioned by calculating the ratio between the current and target utilisation metrics. The target utilisation metric, or threshold, directly impacts how many and how quickly resources will be provisioned. However, the determination of the threshold that would allow satisfying performance-based Service Level Objectives (SLOs) is a long, error-prone, manual process because it is based on the static threshold principle and requires manual configuration. This might result in underprovisioning or overprovisioning, leading to the inadequate allocation of computing resources or SLO violations. Numerous autoscaling solutions have been introduced as alternatives to the HPA to simplify the process. However, the HPA is still the most widely used solution due to its ease of setup, operation, and seamless integration with other Kubernetes functionalities. The present study proposes a method that utilises exploratory data analysis techniques along with moving average smoothing to identify the target utilisation threshold for the HPA. The objective is to ensure that the system functions without exceeding the maximum number of events that result in a violation of the response time defined in the SLO. A prototype was created to adjust the threshold values dynamically, utilising the proposed method. This prototype enables the evaluation and comparison of the proposed method with the HPA, which has the highest threshold set that meets the performance-based SLOs. The results of the experiments proved that the suggested method adjusts the thresholds to the desired service level with a 1–2% accuracy rate and only 4–10% resource overprovisioning, depending on the type of workload. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand model of computing that utilizes virtualization expertise to offer cloud resources such as CPU, memory, storage, and network in virtual machines. As a result, most big data analytics in many modern enterprise applications are run from the cloud. Since resources in these private clouds are limited, getting the most out of resource applications and providing guaranteed user service by efficiently scheduling tasks and resources is the ultimate goal. However, existing big data processing system schedulers need to consider application performance and resource utilization when performing allocations. Therefore, it is difficult to design workflows for low turnaround time and high resource consumption in extensive data systems. In this paper, we propose a resource management system for efficient job scheduling, called RMS, which dynamically schedules big data jobs in Kubernetes cluster nodes for Spark applications and autonomously adjusts scheduling policies in heterogeneous node clusters to enhance application execution and resource consumption. The RMS mechanism will ensure adequate guidance and resources available in its planning objectives and satisfactory resource utilization. The experimental analysis of different RMS and performance preferences using different methods depends on the predicted completion time and the benchmark statistical result of different significant data performance indicators traces. The results show that RMS decreases the cost and scheduling overhead and improves job execution performance. [ABSTRACT FROM AUTHOR]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Applications that are constrained by low-latency requirements can hardly be executed on cloud infrastructures, given the high network delay required to reach remote servers. Multi-access Edge Computing (MEC) is the reference architecture for executing applications on nodes that are located close to users (i.e., at the edge of the network). This way, the network overhead is reduced but new challenges emerge. The resources available on edge nodes are limited, workloads fluctuate since users can rapidly change location, and complex tasks are becoming widespread (e.g., machine learning inference). To address these issues, this article presents NEPTUNE, a serverless-based framework that automates the management of large-scale MEC infrastructures. In particular, NEPTUNE provides (i) the placement of serverless functions on MEC nodes according to users' location, (ii) the resolution of resource contention scenarios by avoiding that single nodes be saturated, and (iii) the dynamic allocation of CPUs and GPUs to meet foreseen execution times. To assess NEPTUNE, we built a prototype based on K3S, an edge-dedicated version of Kubernetes, and executed a comprehensive set of experiments. Results show that NEPTUNE obtains a significant reduction in terms of response time, network overhead, and resource consumption compared with five state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

This perspective paper introduces a novel framework for container orchestration called CODECO. The CODECO orchestration framework relies on a data-network-computing approach to define the best infrastructure that can support the operation of next-generation Internet applications across a mobile, heterogeneous Edge-Cloud continuum. The selection of such an infrastructure is aligned with target performance profiles defined by the user, such as resilience or greenness. CODECO proposes to rely on decentralized Artificial Intelligence approaches to provide the most suitable infrastructure to an application deployment, considering infrastructural challenges, such as intermittent connectivity and node failure. This paper explains the current CODECO framework and gives insight into operational use-cases where CODECO is being deployed, as relevant examples of application for such a framework. Recent developments in the creation of the open-source CODECO framework are described and explained, allowing the use of the framework by the research community. The paper then provides a thorough analysis of CODECO’s features in comparison with existing orchestration frameworks, explaining the benefits introduced with this dynamic orchestration approach.