A novel penalty model for managing and applying penalties in Cloud Computing

Maarouf, Adil; Qacimy, Bouchra El; Marzouk, Abderrahim; Haqiq, Abdelkrim

doi:10.1109/aiccsa.2015.7507243

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…It is referred to as the waiting time of user requests in the queue, which mainly depends on resource utilisation and network bandwidth. When the underlying resources are highly exploited, it consumes maximal time for performing a new task [34]. e total workload response time is calculated using (4)-( 6):…”

Section: Response Timementioning

confidence: 99%

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Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing

Shruthi

Mundada

Sowmya

et al. 2022

Applied Computational Intelligence and Soft Computing

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Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.

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Section: Response Timementioning

confidence: 99%

“…e existence of agreed resources, when they are required, is specified as resource availability [34]. e resource availability is given in…”

Section: Resource Availabilitymentioning

confidence: 99%

Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing

Shruthi

Mundada

Sowmya

et al. 2022

Applied Computational Intelligence and Soft Computing

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“…To answer these questions, we have modelled with iAgree and tried to validate up to 319 compensable guarantees that are described in natural language, in 24 different scenarios 8 belonging to three different domains, namely: cloud service providers (e.g., Amazon and Rackspace), non-cloud service providers (e.g., DHL and train companies), and B2B service outsourcing. As a complementary material we provide a workspace with the whole list of agreements modelled; in such a context, while in the Designer Studio tool a pre-loaded demonstration workspace is available with a subset of agreements, the whole dataset of modelled agreements 9 can be imported into the tool to be analysed.…”

Section: Evaluation In Real-world Scenariosmentioning

confidence: 99%

“…Chard et al [15] try to avoid malicious bidding strategies. Maarouf et al [8] propose a formal model considering penalties of different metrics. Xiaoyong et al [7] try to maximise the revenue by means of avoiding penalisations.…”

Section: Related Workmentioning

confidence: 99%

“…Such a limit helps to avoid mistakes like an unbounded, automated penalty that was discarded in 2005 by the UK Royal Mail company after causing a loss of £280 million in one year and a half. 1 However, despite the importance of checking the validity of compensations, it has not been dealt with by current research proposals that use some form of compensation in their SLAs [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. Instead, they mostly focus on the optimisations of service costs by finding a trade-off between compensation and operation costs or on the automation of several parts of compensation management.…”

Section: Introductionmentioning

confidence: 99%

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Automated Validation of Compensable SLAs

Müller

Gutierrez

Fernández

et al. 2021

IEEE Trans. Serv. Comput.

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Service Level Agreement (SLA) regulates the provisioning of a service by defining a set of guarantees. Each guarantee sets a Service Level Objective (SLO) on some service metrics, and optionally a compensation that is applied when the SLO is unfulfilled or overfulfilled. Currently, there are software tools and research proposals that use the information about compensations to automate and optimise certain parts of the service management. However, they assume that compensations are well defined, which is too optimistic in some circumstances and can lead to undesirable situations. In this article we discuss about the notion of validity of guarantees with a compensation, which we refer to as compensable guarantees (CG). We describe an abstract model of CGs and we provide a technique that leverages constraint satisfaction problem solvers to automatically validate them. We also present a materialisation of the model of CGs in iAgree, a language to specify SLAs and a tooling support that implements our whole approach. An assessment over 319 CGs taken from 24 real-world SLAs suggests that the expressiveness and effectiveness of our proposal can pave the way for using CGs in a safer and more reliable way.

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