ISSN: 1690-4524 (Online)
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A Transdisciplinary Approach to Enhancing Online Engineering Education Through Learning Analytics Masikini Lugoma , Lethuxolo Yende , Pule Dikgwatlhe , Akhona Mkonde , Rorisang Thage , Lucky Maseko , Ngonidzashe Chimwani (pages: 1-6) AI Disruptions in Higher Education: Evolutionary Change, Not Revolutionary Overthrow Cristo Leon , James Lipuma , Maximus Rafla (pages: 7-18) Education, Research, and Methodology: A Transdisciplinary Cybernetic Whole Nagib Callaos , Cristo Leon (pages: 19-33) Enhancing Educational Effectiveness Through Transdisciplinary Practice: The ETCOP Model Birgit Oberer , Alptekin Erkollar , Andreas Kropfberger (pages: 34-40) From Instruction to Interaction: Reflexive Learning Design for Cross-Generational Engagement at the Workplace Gita Aulia Nurani , Ya-Hui Lee (pages: 41-44) GIS in Aquatic Animal Health Surveillance: A Transdisciplinary eLearning Initiative Integrating Education, Research, and Methodology (The Aquae Strength Project) Eleonora Franzago , Rodrigo Macario , Matteo Mazzucato , Federica Sbettega , Manuela Cassani , Guido Ricaldi , Francesco Bissoli , Anna Nadin , Fabrizio Personeni , Manuela Dalla Pozza , Grazia Manca , Nicola Ferré (pages: 45-50) Reflexivity as a Compass: The European AI Act and Its Implications for U.S. Higher Education Institutions Jasmin Cowin (pages: 51-56) Required General Education Program Evaluation: Bridging the Gap Between Educators and Administrators James Lipuma , Cristo Leon , Jeremy Reich (pages: 57-61) Researching Ourselves Jeremy Horne (pages: 62-72) The Self-Aware, Reflective Learner: Fostering Metacognitive Awareness and Reflexivity in Undergraduates Through Service-Learning Genejane Adarlo (pages: 73-81)
ABSTRACT
An Adaptive Process Allocation Scheme in Grid Environment Tibor Gyires
The Grid is an interconnected set of distributed compute servers. An application running on the Grid consists of processes, which can be executed in parallel or in a sequential manner. An application can specify application level and network level Quality of Service parameters including number of processors, memory, special software, network bandwidth, delay, jitter, packet loss, etc. We investigate the question: Which processes are allocated to which compute servers that collectively satisfy the application’s resource requirements and optimize performance and cost parameters. We describe a protocol to identify those compute servers that can execute the application with minimal cost and provide the required application level and network level Quality of Service.
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