SLA-aware resource scheduling algorithm for cloud storage

Wang, Yong; Tao, Xiaoling; Zhao, Feng; Bo, Tian; Sai, Akshita Maradapu Vera Venkata

doi:10.1186/s13638-019-1604-0

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…SLA‐aware resource algorithm to enable cloud storage more effective is proposed in Reference 25. In this work, the I/O throughput and back‐end node space consumption is achieved by processing simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

Nayagi¹,

Sivasankari

Ravi

et al. 2022

Trans Emerging Tel Tech

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Workload execution is composed by mapping larger tasks onto heterogeneous environments such as cloud platforms for enhancing the efficiency of workload resource management techniques. Execution of scientific workflow on a cloud platform is time-consuming, expensive, and requires a fault-tolerance guarantee. The existing methodology has emphasized on minimizing processing time to reduce costs. However, the processing cost can be reduced by minimizing energy consumption. In providing fault-tolerance while meeting the workload quality of service requirement the task is offloaded to a new physical machine; Hence, this increases energy consumption and thereby increases the cost of workload execution. In addressing the research challenges this paper presents the fault-tolerant aware (FTA) workload resource management (WRM) technique.First, the FTA-WRM optimizes processing and communication costs as an energy constraint leveraging the dynamic voltage frequency scaling technique.Then, a task offloading mechanism is modeled as an energy constraint with an application delay requirement for providing fault tolerance into the FTA-WRM. The experiment outcome shows the FTA-WRM significantly improves processing and energy efficiency in comparison with the existing workload resource management technique.

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Section: Related Workmentioning

confidence: 99%

Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

Nayagi¹,

Sivasankari

Ravi

et al. 2022

Trans Emerging Tel Tech

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“…Wang et al [14] presented a resource scheduling algorithm for efficient exploitation of cloud storage. The described solution utilizes throughput and storage space as constraints in the decision making for the scheduling process.…”

Section: Type Of Nodes Utilitymentioning

confidence: 99%

Pareto-Optimised Fog Storage Services with Novel Service-Level Agreement Specification

et al. 2022

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(1) Background: Cloud storage is often required for successful operation of novel smart applications, relying on data produced by the Internet of Things (IoT) devices. Big Data processing tasks and management operations for such applications require high Quality of Service (QoS) guarantees, requiring an Edge/Fog computing approach. Additionally, users often require specific guarantees in the form of Service Level Agreements (SLAs) for storage services. To address these problems, we propose QoS-enabled Fog Storage Services, implemented as containerised storage services, orchestrated across the Things-to-Cloud computing continuum. (2) Method: The placement of containerised data storage services in the Things-to-Cloud continuum is dynamically decided using a novel Pareto-based decision-making process based on high availability, high throughput, and other QoS demands of the user. The proposed concept is first confirmed via simulation and then tested in a real-world environment. (3) Results: The decision-making mechanism and a novel SLA specification have been successfully implemented and integrated in the DECENTER Fog and Brokerage Platform to complement the orchestration services for storage containers, thus presenting their applicable value. Simulation results as well as practical experimentation in a Europe-wide testbed have shown that the proposed decision-making method can deliver a set of optimal storage nodes, thus meeting the SLA requirements. (4) Conclusion: It is possible to provide new smart applications with the expected SLA guarantees and high QoS for our proposed Fog Storage Services.

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“…Cloud computing services are rapidly provisioned to customers based on their service-level agreement needs which they make through an agreement with a cloud provider. Diversified types of cloud services are available in cloud environments and they need to be provisioned to customers qualitatively with respect to SLA guarantees provided by the cloud provider [2]. To provide these services demanded by users adequately to customers without having any issues, i.e., execution of tasks, operational costs, failure rate of tasks, energy consumption, etc., a task scheduler is desperately needed to assign incoming heterogeneous tasks to virtual resources in a cloud environment while improving the rendering of services to various cloud users around the world.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.

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SLA-aware resource scheduling algorithm for cloud storage

Cited by 9 publications

References 33 publications

Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

Pareto-Optimised Fog Storage Services with Novel Service-Level Agreement Specification

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

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