Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 3 Articles
For the problem that the energy efficiency of the cloud computing data center is low, from the\r\npoint of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job\r\nscheduling model based on Google�s massive data processing framework. To solve this model,\r\nwe design a practical encoding and decoding method for the individuals and construct an overall\r\nenergy efficiency function of the servers as the fitness value of each individual. Meanwhile, in order\r\nto accelerate the convergent speed of our algorithm and enhance its searching ability, a local search\r\noperator is introduced. Finally, the experiments show that the proposed algorithm is effective and\r\nefficient....
Cloud-based applications require a high degree of automation regarding their IT\r\nresource management, for example, to handle scalability or resource failures. This\r\nautomation is enabled by cloud providers offering management interfaces accessed by\r\napplications without human interaction. The properties of clouds, especially pay-per-use\r\nbilling and low availability of individual resources, demand such a timely system\r\nmanagement. We call the automated steps to perform one of these management tasks a\r\nââ?¬Å?management flowââ?¬Â. Because the emerging behavior of the overall system is comprised of\r\nmany such management flows and is often hard to predict, we propose defining abstract\r\nmanagement flows, describing common steps handling the management tasks. These\r\nabstract management flows may then be refined for each individual use case. We cover\r\nabstract management flows describing how to make an application elastic, resilient\r\nregarding IT resource failure, and how to move application components between different\r\nruntime environments. The requirements of these management flows for handled\r\napplications are expressed using architectural patterns that have to be implemented by the\r\napplications. These dependencies result in abstract management flows being interrelated\r\nwith architectural patterns in a uniform pattern catalog. We propose a method by use of a\r\ncatalog to guide application managers during the refinement of abstract management flows\r\nat the design stage of an application. Following this method, runtime-specific management\r\nfunctionality and management interfaces are used to obtain automated management flows\r\nfor a developed application....
Context-aware technologies can make e-learning services smarter and more\r\nefficient since context-aware services are based on the userââ?¬â?¢s behavior. To add those\r\ntechnologies into existing e-learning services, a service architecture model is needed to\r\ntransform the existing e-learning environment, which is situation-aware, into the\r\nenvironment that understands context as well. The context-awareness in e-learning may\r\ninclude the awareness of user profile and terminal context. In this paper, we propose a new\r\nnotion of service that provides context-awareness to smart learning content in a cloud\r\ncomputing environment. We suggest the elastic four smarts (E4S)ââ?¬â?smart pull, smart\r\nprospect, smart content, and smart pushââ?¬â?concept to the cloud services so smart learning\r\nservices are possible. The E4S focuses on meeting the usersââ?¬â?¢ needs by collecting and\r\nanalyzing usersââ?¬â?¢ behavior, prospecting future services, building corresponding contents,\r\nand delivering the contents through cloud computing environment. Usersââ?¬â?¢ behavior can be\r\ncollected through mobile devices such as smart phones that have built-in sensors. As\r\nresults, the proposed smart e-learning model in cloud computing environment provides\r\npersonalized and customized learning services to its users....
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