Analysing factors determining the adoption of environmental management measures on the highlands of Ethiopia

A study was conducted on the highlands of Ethiopia to identify and analyse the factors determining the adoption of environmental management measures. In 1985, Ethiopia was classified into low –and high-potential areas based on the suitability of the natural environment for rain-fed agriculture. To address these objectives, case study areas were selected from low-potential and high-potential areas randomly. Data were collected through face-to-face interview and key informants, focus group discussion and field observation. In the low-potential areas, the physical environment ‒ particularly soil and forest environments have shown substantial recovery. Similarly, the water environment has improved. However, in the high-potential areas sampled, these resources are still being degraded. Clear understanding of the benefits of soil conservation structures by farmers, active involvement and technical support from the government and full and genuine participation of farmers in communal environmental resources management activities were found to be main factors in the adoption of environmental management measures.

Improving Management of Distributed Services Using Correlations and Predictions in SLA-Driven Cloud Computing Systems

Recent advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing environmental conditions and number of users, application performance might suffer, leading to Service Level Agreement (SLA) violations and inefficient use of hardware resources. We introduce a system for controlling the complexity of scaling applications composed of multiple services using mechanisms based on fulfillment of SLAs. We present how service monitoring information can be used in conjunction with service level objectives, predictions, and correlations between performance indicators for optimizing the allocation of services belonging to distributed applications. We validate our models using experiments and simulations involving a distributed enterprise information system. We show how discovering correlations between application performance indicators can be used as a basis for creating refined service level objectives, which can then be used for scaling the application and improving the overall application's performance under similar conditions.