Background: Cognitive Muscular TherapyTM (CMT) is an integrated behavioural intervention developed for knee osteoarthritis. CMT teaches patients to reconceptualise the condition, integrates muscle biofeedback and aims to reduce muscle overactivity, both in response to pain and during daily activities. This nested qualitative study explored patient and physiotherapist perspectives and experiences of CMT.Methods: Five physiotherapists were trained to follow a well-defined protocol and then delivered CMT to at least two patients with knee osteoarthritis. Each patient received seven individual clinical sessions and was provided with access to online learning materials incorporating animated videos. Semi-structured interviews took place after delivery/completion of the intervention and data were analysed at the patient and physiotherapist level.Results: Five physiotherapists and five patients were interviewed. All described a process of changing beliefs throughout their engagement with CMT. A framework with three phases was developed to organise the data according to how osteoarthritis was conceptualised and how this changed throughout their interactions with CMT. Firstly, was an identification of pain beliefs to be challenged and recognition of how current beliefs can misalign with daily experiences. Secondly was a process of challenging and changing beliefs, validated through new experiences. Finally, there was an embedding of changed beliefs into self-management to continue with activities. Conclusion:This study identified a range of psychological changes which occur during exposure to CMT. These changes enabled patients to reconceptualise their condition, develop a new understanding of their body, understand psychological processes, and make sense of their knee pain.
Multi-tenancy in cloud computing describes the extent to which resources can be shared while guaranteeing isolation among components (tenants) using these resources. There are three multi-tenancy patterns: shared, tenant-isolated and dedicated component patterns. These patterns have not previously been formally specified. So how do we choose an appropriate multi-tenancy pattern for a multi-tenant application? To address this question, we have created a formalized description of each multi-tenancy pattern in Z language. We formalize the multi-tenancy pattern using fixed semantics, firstly, to verify each of the patterns, secondly, to provide a precise interpretation of the pattern and finally, to choose a suitable multi-tenancy pattern for a multi-tenant application. We then empirically evaluate each pattern using the data-tier of a cloud hosted distributed content management application, WordPress, deployed in a Docker container. Experimental results show that the dedicated pattern performed best with varying tenant needs while shared and tenant-isolated patterns performed variably the same depending on how much data were involved. Based on the empirical evaluation, we provide a selection algorithm to choose suitable multi-tenancy pattern for software deployment.
Web applications commonly suffer from flash crowds and resource failure, resulting in performance degradation. Flash crowds are large, sudden, yet legitimate influxes of user requests that constitute a critical problem because of their potential economic damage. For cloud providers, resource estimation is challenging, while distributing workload and sustaining performance. To alleviate flash crowds and resource failure problems, we propose a novel weight assignment load balancing algorithm that combines five carefully selected server metrics to efficiently distribute the workload of three-tier web applications among virtual machines. We experimentally characterised, using a private cloud running OpenStack, the load distribution ability of our proposed novel algorithm, as well as a baseline algorithm and round-robin algorithm. We compared the performance of the three algorithms by simulating resource failures and flash crowds, while carefully measuring response times. Our experimental results show that our approach improves average response times by 12.5% when compared to the baseline algorithm and 22.3% when compared to the round-robin algorithm in the flash crowds’ situation. In addition, average response time was improved by 20.7% when compared to the baseline algorithm and 21.4% when compared to the round-robin algorithm in resource failure situations. These experiments show that our novel algorithm is more resilient to fluctuating loads and resource failures than baseline algorithms.
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