Defining failure in the nonprofit organization: The case of four human service agencies

Private nonprofit human service organizations provide a spectrum of services that aim to resolve societal problems. Their failure may leave needed and desired services unprovided or not provided sufficiently to meet public demand. However, the concept of organizational failure has not been examined for the nonprofit organization. This research addresses the deficiency in the literatures of organization failure and nonprofit organizations.^ An eight category typology, developed from a review of the current literature and findings from expert interviews, is initially presented to define nonprofit organization failure. A multiple case study design is used to test the typology in four nonprofit human service delivery agencies. The case analysis reduces the typology to five types salient to nonprofit organization failure: input failure, legitimacy failure, adaptive failure, management failure and leadership failure.^ The resulting five category typology is useful to both theory builders and nonprofit practitioners. For theory development, the interaction of the failure types extends the literature and lays a foundation for a theory of nonprofit organization failure that diffuses management and leadership across all of the failure types, highlights management and leadership failure as collective functions shared by paid staff and the volunteer board of directors, and emphasizes the importance of organization legitimacy.^ From a practical perspective, the typology provides a tool for diagnosing failure in the nonprofit organization. Using the management indicators developed for the typology, a checklist of the warning signals of potential failure, emphasizing the key types of management and leadership, offers nonprofit decision makers a priori examination of an organization's propensity for failure. ^

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

A Customer's Expectation and Perception of Hotel Service Quality in Cyprus

In recent years, hotels in Cyprus have encountered difficult economic times due to increasing customer demands and strong internal industry development competition. The hospitality industry’s main concern globally is to serve its customer S needs and desires, most of which are addressed through personal services. Hence, the hotel businesses that are able to provide quality services to its ever-demanding customers in a warm and efficient manner are those businesses which will be more likely to obtain a long term competitive advantage over their rivals. Ironically, the quality of services frequently cannot fully appreciated until something goes wrong, and then, the poor quality of services can have long lasting lingering effects on the customer base and, hence, often is translated into a loss of business. Nevertheless, since the issue of delivery of hospitality services always involves people, this issue must center around the management of the human resource factor, and in particular, on the way which interacts with itself and with guests, as service encounters. In the eyes of guests, hospitality businesses will be viewed successful or failure, depending on [he cumulative impact of the service encounters they have experienced on a personal level. Finally, since hotels are offering intangible and perishable personal service encounters, managing these services must be a paramount concern of any hotel business. As a preliminary exercise, visualize when you have last visited a hotel, or a restaurant, and then, ask yourself these questions: What did you feel about the quality of the experience? Was it a memorable one, which you would recommend it to others, or there were certain things, which could have made the difference? Thus, the way personalized services are provided can make the deference in attracting arid retaining long-term customers

Deployment of a Hybrid Multicast Switch in Energy-Aware Data Center Network: A Case of Fat-Tree Topology

Recently, energy efficiency or green IT has become a hot issue for many IT infrastructures as they attempt to utilize energy-efficient strategies in their enterprise IT systems in order to minimize operational costs. Networking devices are shared resources connecting important IT infrastructures, especially in a data center network they are always operated 24/7 which consume a huge amount of energy, and it has been obviously shown that this energy consumption is largely independent of the traffic through the devices. As a result, power consumption in networking devices is becoming more and more a critical problem, which is of interest for both research community and general public. Multicast benefits group communications in saving link bandwidth and improving application throughput, both of which are important for green data center. In this paper, we study the deployment strategy of multicast switches in hybrid mode in energy-aware data center network: a case of famous fat-tree topology. The objective is to find the best location to deploy multicast switch not only to achieve optimal bandwidth utilization but also to minimize power consumption. We show that it is possible to easily achieve nearly 50% of energy consumption after applying our proposed algorithm.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.