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Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments

机译:降低违反SLA的影响:一种针对云计算环境的主动资源分配方法

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User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users’ satisfaction level. The amount of this decrease depends on user's characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users’ satisfaction level that cause to gain in profitability.
机译:在基于市场的文献中,许多研究人员强调了用户满意度是用户忠诚度的重要先决条件。违反SLA的重要因素可能会降低用户的满意度。减少量取决于用户的特征。其中一些特性与QoS要求有关,并通过SLA宣布给服务提供商。但是其中一些对于服务提供商而言并不为人所知,自私的用户也没有兴趣真正透露它们。文献中的大多数作品都忽略了考虑这些特性,仅根据SLA参数对待用户。因此,具有不同特征但具有相似SLA的两个用户对于服务提供商具有同等的重要性。在本文中,我们使用了两个用户的隐藏特征,即为服务付费和对确定性付费的意愿,提出了一种新的主​​动资源分配方法,旨在减少违反SLA的影响。还提供了基于学习自动机来估计这些特征的新方法。为了验证我们的方法,我们在紧急情况下进行了一些数值模拟。结果表明,我们的方法能够提高用户的满意度,从而提高获利能力。

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