Distributed scheduling method for multiple workflows with parallelism prediction and DAG prioritizing for time constrained cloud applications

Davami, Fatemeh; Adabi, Sahar; Rezaee, Ali; Rahmani, Amir Masoud

doi:10.1016/j.comnet.2021.108560

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…The integrity of the divided data is very important and should be able to detect whether the data has been hacked or tampered by any other user [12]. Thus, it is important to provide an effective workflow execution that meets application security requirement such as availability, confidentiality, and integrity and also application quality of service (QoS) requirement such as energy efficiency, processing time [13]. here a deep-rooted survey of various workload scheduling is conducted for identifying research challenges for heterogeneous cloud computing framework.…”

Section: Figure 1 Workflow In the Cloud Servicesmentioning

confidence: 99%

Research on secure workload execution scheme in heterogeneous cloud environment

Pasha

Jayapandian²

2023

IJEECS

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<span lang="EN-US">The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customer’s specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment.</span>

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Section: Figure 1 Workflow In the Cloud Servicesmentioning

confidence: 99%

Research on secure workload execution scheme in heterogeneous cloud environment

Pasha

Jayapandian²

2023

IJEECS

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“…A comparison and analysis of the system overhead and cost for the proposed schemes, as well as alternative schemes, follow the presentation of a priority-scheduling model based on the fuzzy AHP approach. A module for long-term memory parallel neural network prediction was used [112] to analyse the workflow graphs using the multicriteria Mamdani fuzzy method. The fuzzy inference system group-based priority assignment schema gives workflows a priority value to denote the relative precedence of the requests.…”

Section: Figure 7 Fuzzy Scheduling Classificationmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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“…Davami et al 71 developed multicriteria Mamdani fuzzy algorithm to maximize the utilization of resources and application scheduling success rate within a time constraint.…”

Section: Related Workmentioning

confidence: 99%

A new meta‐heuristic approach for load aware‐cost effective workflow scheduling

Ram

Srivastava

Mishra

2022

Concurrency and Computation

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Workflow scheduling is an important way to manage the execution of a workflow. It introduces the concept of providing suitable resources to workflow tasks in order to finish workflow execution and meet the user's objectives. However, the problem becomes more complex when scheduling must balance two conflicting objectives, such as minimizing execution cost and maximizing load across all computing resources. A workflow has many interdependent tasks, and the cloud datacenter has many computing resources to execute the workflow. There can be an asymptotically infinite number of mappings of tasks-to-computing resources. Every mapping produces different execution costs with different workloads on computing resources. The main challenge for the researcher is to develop an intelligent scheduling algorithm to identify an optimal mapping that produces minimal execution cost with fair workload distribution on resources. We developed a novel meta-heuristic algorithm named Investment-Based Optimization (IBO) to identify an optimal mapping. The IBO algorithm was first tested on optimization benchmark functions and then simulated in CloudSim to see its performance for scheduling workflows. Finally, IBO was tested over Montage, Epigenomics, Sipht, and a sample workflow, and it was found that IBO reduces execution costs by 33%, 16%, 16.36%, and 20% with a fair workload distribution.

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Distributed scheduling method for multiple workflows with parallelism prediction and DAG prioritizing for time constrained cloud applications

Cited by 10 publications

References 39 publications

Research on secure workload execution scheme in heterogeneous cloud environment

Research on secure workload execution scheme in heterogeneous cloud environment

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

A new meta‐heuristic approach for load aware‐cost effective workflow scheduling

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