مدیریت فعالانه تخلفات SLA با ضبط وقایع خارجی
Abstract
1- Introduction
2- Related work
3- Proposed framework for proactive SLA violation identification by capturing relevant external events
4- SLA modelling and identification of relevant external events module
5- External event processing module
6- Impact assessment module
7- Reasoning and decision-making module
8- Validation of the proposed framework for proactive SLA violation identification by capturing relevant external events
9- Conclusion and future work
References
Abstract
The cloud of things (CoT) is an emerging paradigm that has merged and combined cloud computing and the Internet of Things (IoT). Such a paradigm has enabled service providers to provide on-demand computing resources from devices spread across different locations for service users to be dynamically connected to them. While this benefits the CoT service providers and users in many ways, it also brings a key challenge of ensuring that the service is delivered according to the promised quality. Failure to ensure this will result in the service provider experiencing penalties of different types and the service user experiencing disruptions. The literature addresses this problem by proactively managing for SLA violations. However, given the geographically dispersed region of a formed CoT service, in this paper we argue that for proactive SLA violation identification, we need specialized techniques that also consider events that are outside the usual control of service providers and users, but will impact the CoT environment and the quality of service. We propose a framework that identifies such external events of interest and ascertains their impact on achieving the service according to the promised quality. We explain the working of our proposed framework in detail and demonstrate its superiority in proactively determining SLA violations as compared to existing approaches.
Introduction
The Cloud of Things (CoT) environment which is an amalgamation of the Cloud and the Internet of Things paradigm offers a framework for service users to form on-demand services with different providers that may be located at any location. However, such freedom of choice also brings challenges for service users and service providers. One such challenge common to both is the successful delivery of the service according to the defined QoS parameters, such as response time range, minimum service availability or a certain throughput. These service constraints are usually stated by the service provider in the service offer and are mutually agreed upon by the provider and user in the Service Level Agreement (SLA). However, due to the characteristics of the CoT environment and the services themselves, many services exhibit dynamic QoS variations that result in frequent changes in their behaviours, leading to SLA violations [1–3]. To avoid this, it is necessary to proactively monitor SLAs to prevent violations. As an indication of its importance, the existing literature offers a large amount of work in this area. However, a common shortcoming of the current approaches is that they only focus on events arising from internal sources of the CoT environment which are defined as those events that are under the control of the service provider who forms the SLA with the service user.