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Abstract. Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for poweraware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.

Recently cloud computing has offered attractive solutions for academic and research institutions due to several reasons. In this chapter, the authors present a study of how cloud computing can be used for research and teaching activities in higher educational and research institutions in developing countries. Instead of focusing on cloud computing offering for basic IT infrastructures used in daily work of these institutions, the authors concentrate on the use of cloud computing for satisfying ad hoc needs of computing resources in research and teaching activities. Thorough analyses of research and teaching activities, requirements for cloud computing, benefits of utilizing cloud computing, and adoption barriers for these activities are also included. The authors then present the selected challenges in tackling these barriers and discuss possible approaches for solving these challenges and report lessons learned and experiences in utilizing and developing cloud computing solutions for teaching and research activities in Vietnam.

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energyefficient resource allocation for VMs in HPC cloud is trade-off between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and/or user applications in the future. Users will request short-term resources at fixed start-times and non-interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance-per-watt oriented Best-fit (EPOBF)) that uses metric of performance-per-watt to choose which most energyefficient PM for mapping each VM (e.g. maximum of MIPS/Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state-of-the-art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with stateof-the-art allocation heuristics.