An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

Ibrahim, Muhammad; Nabi, Said; Baz, Abdullah; Alhakami, Hosam; Raza, Muhammad Summair; Hussain, Altaf; Djemame, Karim

doi:10.1109/access.2020.3007201

Cited by 31 publications

(20 citation statements)

References 24 publications

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“…The proposed approach migrates VMs from overloaded PMs to under-loaded PMs according to the location of VMs/users, thus making the proposed approach superior over the compared approaches in terms of metrics (i.e., energy consumption, number of hosts shut down, resources utilization, SLA violation, and number of migrations) compared. The experiments are carried out by utilizing a well-known CloudSim simulator [38]. The simulation experiments are executed ten times with different seed values and results are obtained in terms of (i) energy consumption, (ii) SLA violation, (iii) the number of migrations, and (iv) the number of hosts shutdown.…”

Section: Resultsmentioning

confidence: 99%

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

et al. 2021

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The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

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Section: Resultsmentioning

confidence: 99%

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

et al. 2021

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“…To investigate the performance of the available scheduling approaches, three different benchmark datasets namely the HCSP (Heterogeneous Computing Scheduling Problems) dataset [12], [13], Google like dataset called GoCJ [33], and Synthetic workload [17] have been considered. The HCSP instances-based benchmark dataset is based on the Expected Time to Compute (ETC) model and is generated in such a way that can approximate the real behavior of a heterogeneous computing environment.…”

Section: Methodsmentioning

confidence: 99%

DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

2021

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For the last few years, Cloud computing has been considered an attractive high-performance computing platform for individuals as well as organizations. The Cloud service providers (CSPs) are setting up data centers with high performance computing resources to accommodate the needs of Cloud users. The users are mainly interested in the response time, whereas the Cloud service providers are more concerned about the revenue generation. Concerning these requirements, the task scheduling for the users' applications in Cloud computing attained focus from the research community. Various task scheduling heuristics have been proposed that are available in the literature. However, the task scheduling problem is NP-hard in nature and thus finding optimal scheduling is always challenging. In this research, a resource-aware dynamic task scheduling approach is proposed and implemented. The simulation experiments have been performed on the Cloudsim simulation tool considering three renowned datasets, namely HCSP, GoCJ, and Synthetic workload. The obtained results of the proposed approach are then compared against RALBA, Dynamic MaxMin, DLBA, and PSSELB scheduling approach concerning average resource utilization (ARUR), Makespan, Throughput, and average response time (ART). The DRALBA approach has revealed significant improvements in terms of attained ARUR, throughput, and Makespan.This fact is endorsed by the average resource utilization results (i.e., 98 % for HCSP dataset, 75 % for Synthetic workload (improve ARUR by 72.00 %, 77.33 %, 78.67 %, and 13.33 % as compared to RALBA, Dynamic MaxMin, DLBA and PSSELB respectively), and 77 % for GoCJ (i.e., the second best attained ARUR)). INDEX TERMS Cloud ComputingResource scheduling Dynamic Task scheduling scheduling heuristics tasks to vm mapping load imbalance

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“…Nevertheless, considering the high frequency and variations of ECG signals, the execution of the training module should be faster. This requirement makes the efficient allocation of the Cloud resources [29], [30] a must, which is also subject to extensive research.…”

Section: System Architecturementioning

confidence: 99%

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

et al. 2021

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Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient's heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.

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An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

Cited by 31 publications

References 24 publications

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

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