Wale: A Dockerfile-Based Approach to Deduplicate Shared Libraries in Docker Containers

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

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00135

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…This section primarily encompasses extensive simulations conducted for DDQL-DFTRTSA. 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.…”

Section: Resultsmentioning

confidence: 99%

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%

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.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…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. After the calculation of makespan with these, we chose real-time worklogs from [ 41 , 42 ], and they were represented as D05 and D06 throughout the research. We chose these dataset for fabrication in this simulation because generally, many existing authors used random generated workloads, but using a random generated workload for this type of scheduling problem does not provide precise schedules.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…To evaluate the proposed FTTATS, we compared the proposed FTTATS with the existing state-of-the-art approaches, ACO, GA, and PSO. We used configuration settings required for simulation captured from [ 42 ]. Table 3 below represents configuration settings used in the simulation for the proposed FTTATS.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Initially, we fabricated datasets with different statistical distributions, and they are represented as D01, D02, D03, and D04, respectively. After evaluating FTTATS with statistical distributions, we used real-time worklogs, i.e., HPC2N [ 41 ] and NASA [ 42 ]. They are represented as D05, D06.…”

Section: Simulation and Resultsmentioning

confidence: 99%

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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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