Risk-based framework for SLA violation abatement from the cloud service provider’s perspective

Hussain, Walayat; Hussain, Farookh Khadeer; Hussain, Omar Khadeer; Bagia, Ravindra; Chang, Elizabeth

doi:10.1093/comjnl/bxx118

Cited by 34 publications

(18 citation statements)

References 43 publications

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“…Cloud computing seems to be a technical phenomenon for contracting as a company that manages the flexible and adaptable IT resources. One of the important aspects of this model is the assured on-demand accessibility over the Internet of distributed resources to address problems with high-performance computing (HPC) (Hussain et al 2018b). The resources are distributed all over the world to provide services rapidly to the consumers.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Gupta¹,

Bhadauria²,

Singh³

2020

J Ambient Intell Human Comput

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In cloud computing environment, load balancing is an important task. So many of the researchers had focused on load balanced scheduling technique. Those provides better load balancing in cloud but there are some issues like resource allocation and cost maintenance. One of the major issue in load balancing techniques is service level agreement (SLA) management because many of them are affected by this SLA-violation. Many researchers have proposed various risk based framework but few of them has guides the service provider to take steps for SLA violation abatement and they also need some improvements. To tackle this problem, a new SLA-aware risk management framework (SA-RMF) is proposed in this work for efficient load balancing in cloud. A new technique is presented here based on CPU parameter for generating efficient dynamic threshold. A better quality of service values prediction is achieved by using hybrid approach in SA-RMF. Through experiments, the appropriateness of proposed system is demonstrated and validated that it helps cloud providers to mitigate future violations of services and consequences.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Gupta¹,

Bhadauria²,

Singh³

2020

J Ambient Intell Human Comput

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“…These parameters can be highly dynamic especially under unexpected variation of the workload. Moreover, the characteristics of task also play an important role on the system performance [24]. In our work, the state space S contains all of environment states, and each state includes three parameters: server states, network states and tasks characteristic.…”

Section: State Spacementioning

confidence: 99%

Reinforcement Learning-Based Workload Scheduling for Edge Computing

Zheng¹,

Wan

Zhang

et al. 2021

Preprint

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Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of the end user, heterogeneous applications and intermittent traffific. In such environment, edge computing always encounters workload scheduling problem of how to effificiently schedule incoming tasks from mobile devices to edge servers or cloud servers, which is a hard and online problem. In this work, we focus on the workload scheduling problem with the goal of balancing the workload, reducing the service time and minimizing the failed task rate. We proposed a reinforcement learning-based approach, which can learn from the previous actions and achieve best scheduling in the absence of a mathematical model of the environment. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine utilization, and failed tasks rate compared with other approaches. Our reinforcement learning-based approach can provide an effificient solution to the workload scheduling problem in edge computing.

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“…When the system finds that the value of QoSPM exceeds the threshold value, then the risk management module (RMM) is activated to determine the risk of possible SLA violation and take the appropriate action to mitigate it. The detail on the risk management module is presented in [49].…”

Section: Viable Sla Management Framework (2phased-vslam)mentioning

confidence: 99%

Cloud Marginal Resource Allocation: A Decision Support Model

et al. 2019

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One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as-marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small-to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.

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Risk-based framework for SLA violation abatement from the cloud service provider’s perspective

Cited by 34 publications

References 43 publications

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud

Reinforcement Learning-Based Workload Scheduling for Edge Computing

Cloud Marginal Resource Allocation: A Decision Support Model

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