In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider's perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties.
Mohs micrographic surgery (MMS) is recognized as the goldstandard treatment for high-risk nonmelanoma skin cancers (NMSC) of the head and neck. Given the rising incidence of skin cancer, the past two decades have seen a rapid increase in the number of centres providing this service in the U.K. However, data on the safety, complication rates and patient acceptance of MMS in the U.K. are lacking. Over a 3-month period (September to November 2012) eight regional MMS centres collected data that included tumour site, number of stages to clearance, method of reconstruction and intra-and postoperative complications. In addition to collecting basic demographic and medical information, patients were also asked to rate, on a 10-point Likert scale, (i) their perceived anxiety levels preoperatively, (ii) how well they tolerated the surgery on the day, and (iii) when followed up, their overall acceptance of having undergone MMS under local anaesthesia (LA). Data on 565 patients were analysed. There were 278 women and 287 men, with a median age of 67 years (range 28-93 years). The majority of lesions treated were NMSC (98%). The average number of stages to tumour clearance was 1Á3 (range 1-5). Overall, 60% of patients were clear of tumour within one stage and 34% in two stages, with 6% requiring three or more stages. On average, patients were able to leave the department a little over 4 h after commencing treatment. In total, 88% of all reconstructions (including large flaps and interpolated flaps) were performed on the day by the Mohs surgeon. No major peri-or postoperative complications occurred. Although trouble-
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