Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment

Farid, Mazen; Latip, Rohaya; Hussin, Masnida; Hamid, Nor Asilah Wati Abdul

doi:10.1109/access.2020.2970475

Cited by 57 publications

(48 citation statements)

References 48 publications

(108 reference statements)

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“…Here, the results related to a different number of clouds and hosts is not shown because it does not make any change as the ideal state of VM is not considered. The ETAMCN algorithm is compared with Random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm presented in [3], and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) as in [4]. We have run these algorithms ten times for each set of input tasks representing an ETC matrix for two scenarios.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Energy consumption is another critical performance parameter of the multi-cloud system. An Energy Consumption (EC) matrix is used for the calculation purpose is shown in equation (4). ECi,pq represents the energy consumption of q th VM of p th cloud when the VM will execute the i th task.…”

Section: _______ (2)mentioning

confidence: 99%

“…But, when the same allocation problem is considered for multiple clouds (multi-cloud), the scenario becomes more challenging [30]. Including all these, each CSP tries to minimize the energy usage for profit maximization and experiences penalties for the case of failures during execution [4]. This will lead the problem more attractive for the researchers.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra

Alsayat

et al. 2020

IEEE Access

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In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLAviolation of ETAMCN for different scenarios is performed.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: _______ (2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra

Alsayat

et al. 2020

IEEE Access

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“…Multiple previous works can be found in literature using FL to perform an allocation strategy able to considering inherent imprecisions. For example, the recent works of Farid et al [14], Wang et al [59] and Ragmani et al [60] propose diverse FL-derived strategies for tasks/VMs scheduling in cloud computing. Also, in the field of cloud-fog-IoT, it is to be remarked the work of Mallikarjuna [15], which suggests a scheduling approach for resource management based of FL.…”

Section: Suitable Techniquesmentioning

confidence: 99%

“…To be precise, it can allow millions of users and administrators of these containers' management tools to achieve a more efficient and personalized scheduling of their microservices, based on their specific objectives and applications in fog, cloud or IoT. Although these techniques have been largely proved effective in the scheduling of tasks and VMs in the last years [14][15][16][17][18], their adaptation and adoption in containers' scheduling represent multiple challenges as well as opportunities. These challenges and opportunities have scarcely been explored and analyzed at the time of writing and motivates this work [19,20].…”

Section: Introductionmentioning

confidence: 99%

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

Prado

García-Galán

Expósito

et al. 2020

Sensors

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Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers' scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers' schedulers based on soft-computing in the dominant open-source containers' management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers' schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers' scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line.

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Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

Fan

Zhang

2022

Concurrency and Computation

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Summary The elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.

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Scheduling Scientific Workflow Using Multi-Objective Algorithm With Fuzzy Resource Utilization in Multi-Cloud Environment

Cited by 57 publications

References 48 publications

Energy-Aware Task Allocation for Multi-Cloud Networks

Energy-Aware Task Allocation for Multi-Cloud Networks

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities

Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

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