Wale: A solution to share libraries in Docker containers

D'Urso, F.; Santoro, Corrado; Santoro, Federico Fausto

doi:10.1016/j.future.2019.03.049

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…We employed random workload generation; incorporated statistical task dispersion using various artificially generated datasets based on different distributions, and utilized real-time work logs from sources [27], [28]. To evaluate the performance of the DDQL-DFTRTSA scheduler, we conducted an extensive set of experiments utilizing the cloudsim [29] simulator. To begin our analysis, we initially employed various statistical data distributions to evaluate all relevant parameters.…”

Section: Resultsmentioning

confidence: 99%

“…Initially, we generated datasets with various statistical distributions, labeled as D01, D02, D03, and D04. Subsequently, we evaluated DDQL-DFTRTSA using real-time work logs, namely HPC2N [29] and NASA, represented as D05 and D06. The suggested DDQL-DFTRTSA approach was compared to existing advanced methods, ICFWS, PCFT, EPRD, and WFMS.…”

Section: Analysis and Results Discussionmentioning

confidence: 99%

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Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Kalaskar,

Somasundaram

2024

IJEECS

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<span>Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.</span>

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Section: Resultsmentioning

confidence: 99%

Section: Analysis and Results Discussionmentioning

confidence: 99%

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Kalaskar,

Somasundaram

2024

IJEECS

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“…This section mainly represents extensive simulations carried out for FTTATS using random generated workload, statistical dispersion of tasks using various fabricated datasets by different distributions and realtime worklogs from [ 41 , 42 ]. This extensive set of experiments conducted using the Cloudsim [ 43 ] simulator to evaluate the performance of the FTTATS scheduler. Initially, to evaluate all parameters, we used various statistical data distributions by fabricating the datasets, i.e., represented as D01, D02, D03, D04, i.e., uniform, normal, left skewed, and right skewed, respectively.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…This subsection clearly presents analysis of results and discusses the ways in which the proposed FTTATS improves scheduling while addressing parameter makespan, rate of failures, success rate of VMs, availability, and turnaround efficiency. We conducted an extensive set of simulations on Cloudsim [ 43 ]. Initially, we fabricated datasets with different statistical distributions, and they are represented as D01, D02, D03, and D04, respectively.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Mangalampalli,

Karri,

Gupta

et al. 2023

Sensors

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Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

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“…Speci cally, the vulnerability found in various components of the Docker environment, whether it be developing or implementing any original use cases using a top-down method. We also identify real-life situations in which speci c vulnerabilities can be abused, recommend potential solutions, and address Docker-provider PaaS implementation issues [17]. However, the study described the security issues of containers and the problems associated with containers being lightweight and using the same kernel as the Host operating system.…”

Section: Security Vulnerabilities In Containerizationmentioning

confidence: 99%

Container Performance and Vulnerability Management for Container Security Using Docker Engine

Alyas

Ali

Khan

et al. 2022

Security and Communication Networks

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Containers have evolved to support microservice architecture as a low-cost alternative to virtual machines. Containers are increasingly prevalent in the virtualization landscape because of better working; containers can bear considerably less overhead than the conventional hypervisor-based component virtual machines. However, containers directly communicate with the host kernel, and attackers can co-locate containers in the host system quicker than virtual machines. This causes significant security issues in container technology. The security hardening system is currently targeted at implementing universal access management regulations that make it difficult to assess the required procedure for accessing containers. Security mechanisms include an explicit awareness of the purpose and actions of the container and entail manual interaction and configuration. A user-friendly container protection scheme implemented an access policy to comply with its anticipated and legitimate application performance. In this study, container technology constraints have been overcome by proposing a unique Docker-sec mechanism. Docker-sec uses four mechanisms; the original collection has been improved during container runtime by additional rules that constrain the capacity of the container, further representing the applications in practice, file system, processes, network isolation, and vulnerability scanning of Docker images over different workload. Different vulnerabilities have been scanned with a CVE severity level. Results showed that inter-container communication with the system is more secure containers from zero vulnerabilities with an overhead of 3.45%.

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Wale: A solution to share libraries in Docker containers

Cited by 9 publications

References 8 publications

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Container Performance and Vulnerability Management for Container Security Using Docker Engine

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