Providing an accurate analysis of dengue epidemic seasons will allow sufficient time in taking necessary decisions and actions to safeguard the situation for local authorities. This research aims to develop a web-based dengue tracking system (DTS) that makes use of environmental input factors in predicting the future behavior of dengue cases using Artificial Neural Network. The system can serve a valuable purpose for the health sectors as it informs them to take action on recorded cases in areas which are prone to dengue. Through mapping, the system can also generate an analysis of the area where outbreaks of dengue commonly occurs using a graphical representation of case patterns.

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A cloud system has to deal with highly variable workloads resulting from dynamic usage patterns in order to keep the QoS within the predefined SLA. Aside from the aspects regarding services, another emerging concern is to keep the energy consumption at a minimum. This requires the cloud providers to consider energy and performance trade-off when allocating virtualized resources in cloud data centers. In this paper, we propose a resource provisioning approach based on dynamic thresholds to detect the workload level of the host machines. The VM selection policy uses utilization data to choose a VM for migration, while the VM allocation policy designates VMs to a host based on its service reputation. We evaluated our work through simulations and results show that our work outperforms non-power aware methods that don't support migration as well as those based on static thresholds and random selection policy.

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

It is common among users to have multiple computing devices as well as to access their files or do work at different locations. To achieve file consistency as well as mobility in this scenario, an efficient approach for workspace synchronization should be used. However, file synchronization alone cannot guarantee the mobility of work environment which allows activities to be resumed at any place and time. This paper proposes a ubiquitous synchronization approach which provides cloud-based access to a user's workspace. Efficient synchronization is achieved by combining session monitoring with file system management. Experimental results show that the proposed mechanism outperforms Cloud Master-replica Synchronization in terms of number of I/O operations, CPU utilization, as well as the average and maximum latencies in responding to client requests.