SummaryMaintenance energy requirements (MERs) were calculated for 17 German shepherd and 20 Labrador retriever adult dogs using an in-home prospective dietary trial. The dogs were fed the same dry pet food and body weight, food intake, body condition score and physical activity were monitored for 10 weeks. Labrador retrievers were significantly heavier and had higher body condition scores than German shepherd dogs, but there was no difference between males and females within each group. Body weights remained stable over the study period, with an average daily gain of . There were no significant differences in MER between the two breeds, or between males and females within and between the two breeds. There was a significant inverse relationship between MER and body condition score, reflecting the lower energy expenditure of adipose tissue. The lower MER of dogs in this study, relative to previous observations, may reflect climatic and environmental differences and highlight the necessity for accurate estimates of MER in relation to the production and feeding of pet foods. K E Y W O R D Smaintenance energy requirement, Labrador retriever, German shepherd

Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method.

<p>Changes in the education policy is normally viewed with apprehension by the teachers, as it brings a change to a higher or lower level, involving novel skills of learning and running through for the improvisation of the tasks done routinely. This paper scouts the new education policy 2020 and its empirical study in which the data is investigated about the earlier policies in depth. It is a framework, helpful for developing expertise in the specific area where the teachers have often felt anxiety.</p>

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

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