A Competitive Penalty Model for Availability Based Cloud Sla

Yuan, Xiaoyong; Tang, Huiping; Liu, Ying; Jia, Tong; Liu, Tiancheng; Wu, Zhonghai

doi:10.29268/stcc.2016.0001

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Popular penalty computation methods adopted by different cloud-service providers are presented in Table 1. There are three popular methods for calculating the penalty: (a) a certain percentage of the total charge paid, (b) a fixed amount paid for different violation levels, and (c) a specific ratio of downtime [36]. In the proposed model, we design the violation cost, which depends on the violation level, i.e., the amount of time the task requires to finish its execution beyond the specified deadline.…”

Section: Sla Violation Costmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs. This approach is particularly relevant in scenarios where latency, resource constraints, and cost considerations are crucial factors for the deployment of applications. In this study, we propose a hybrid approach that combines a genetic algorithm (GA) with the Flamingo Search Algorithm (FSA) to place application modules while minimizing cost. We consider four cost-types for application deployment: Computation, communication, energy consumption, and violations. The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system. An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches. The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio (TGR) and total cost.

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Section: Sla Violation Costmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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“…To simplify the cloud programming, a variety of frameworks [8,[10][11][12][13] are developed for different workload patterns. Secondly, the QoS requirements documented in the form of service level agreements (SLAs) are different [14,15].…”

Section: Introductionmentioning

confidence: 99%

Resource stealing: a resource multiplexing method for mix workloads in cloud system

Tan

et al. 2016

J Supercomput

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The cloud computing paradigm enables providing resources on demand. However, most of them focus on a single type of application requiring separate quality of service. In the context that mix heterogeneous workloads are co-scheduled in the cloud, resource multiplexing is the key to improve resource utilization under premise of performance guaranteing. In this paper, we propose a resource stealing mechanism to improve resource multiplexing of cloud resources. It enables free resource fragments reserved by some workloads being utilized by others. To meet certain service level agreement, resource preemption is adopted as a complement to resource stealing. It ensures each workload with a minimum amount of resources when required. Moreover, we propose an adaptive joint resource provisioning algorithm. It integrates our resource multiplexing method into elastic resource provisioning. Experimental results reveal that the proposed algorithms improve resource utilization and workload performance simultaneously.

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“…In this case, the obligations underwritten in SLAs may not be fulfilled. Consequently, if contractually agreed, e.g., see Yuan et al (2015a), the cloud provider is expected to pay the penalty and/or compensate the customer's losses. If violations take place on a wide scale, penalties and compensations may endanger the economic balance of the cloud provider.…”

Section: Introductionmentioning

confidence: 99%

Pricing Cat Bonds for Cloud Service Failures

Mastroeni

Mazzoccoli

Naldi

2022

JRFM

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The use of the cloud to store personal/company data and to run programs is gaining wide acceptance as it is more efficient and cost-effective. However, cloud services may not always be available, which could lead to losses for customers and the cloud provider (the provider is typically obligated to compensate its customers). It can protect itself from such losses through insurance, which transfers the risk to the insurer. In the case of poor cloud availability, the amount that the insurer has to pay back to the cloud provider may become so high that it endangers the insurer’s financial solvency. We propose the use of cat bonds as reinsurance tools as well as the Nowak–Romaniuk pricing scheme. The outage frequency was described by the Poisson process and the loss severity was described by a Pareto random variable; we derived a closed formula for the price of a cat bond in a stochastic interest rate environment, using both one-factor and two-factor short-rate models. We demonstrated the applicability of our pricing formula in a real context.

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A Competitive Penalty Model for Availability Based Cloud Sla

Cited by 4 publications

References 16 publications

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Resource stealing: a resource multiplexing method for mix workloads in cloud system

Pricing Cat Bonds for Cloud Service Failures

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