Research On Some Key Issues For Resource Management In Green Virtualized Data Center

With the prosperity of Internet and development of cloud computing technology,data center becomes more and more important. Currently data center hosts overhundreds of thousands of servers as well as other IT infrastructure devices which notonly makes managerial work more difficult, but also consumes huge amount of energyat the same time. Furthermore, this not only reduces the profit margin of serviceprovider, but also leads to high carbon emission. A unanimously approved trend is thatdata center resource virtualization and resource management has become dynamic,flexible, automatic and energy efficient. One of the hot issues in this research domain isenergy-efficient adaptive resource management. Based on this proposal, this thesisanalyzes and concludes the state-of-art techniques and research on energy efficientresource management in data center at first, then do depth study of some key issuesaround resource management in virtualized data center. The main contributions in thisthesis include:1. An administrator’s preference based method for capacity planning in virtualizedenterprise data center is proposed. First, based on granular rough theory, a family ofatomic preference granules supporting semantic alongwith two operators, namedvertical aggregation operator and horizon combination operator are proposed, and anew preference model, which makes use of rough granules to describe user preference,is also built. The new model can address the said issue that existing scheme cannot dealwith using semantic preference representation and uncertain data. Second, thepreference model is applied to describe the administrator’s preference of resourceselection. Meanwhile, due to the peak-valley of the workload of applications is indifferent time, a way to problem modeling is proposed based on workload timesharinganalysis, which can achieve maximal resource utilization on the premise of keeping therequired QoS. The associated services and the mutually exclusive services, thecompatibility between services and servers are taken into account and five principles ofconsolidation are proposed. The establishing model is regarded as a multi-dimension binpacking problem with several constraints and a GGA-based algorithm is proposed to search for the global optimal solution. Finally, the proposed method is proved with thehelp of experiments.2. Since existing methods of energy efficient dynamic resource optimization indata center just maximize resource utilization without considering the overhead of VM(Virtual Machine) placement change (e.g. VM deployment, start, migration and reclaim),we propose a new dynamical resource optimization method which can minimize theenergy consumption and VM placement change at the same time. The new methodadopts control loop based resource management frame work, and regards dynamicalresource optimization as a dual-objective combinational optimization problem, andmoreover it designs a network-flow-theory and iterative optimization basedapproximate algorithm, called NFT-DRP, to solve it. The experimental results whencompared to existing work show that, the proposed method can slightly decrease theenergy consumption but greatly decrease the number of VM placement change.3. Since current researches only consider part of energy consumed by servers, anovel method is proposed to reduce energy consumption from both servers and networkdevices. The proposal achieves minimum energy consumption by minimizing the activeservers and network devices through analyzing the net topology. Moreover, the workformally models the problem and designs a hybrid particle swarm algorithm calledHPSO-NA to implement virtual machine consolidation. The experimental results showthat the proposed method can effectively reduce the energy consumption.4. Since current hotspot detecting algorithms work based on traditional resource(i.e. CPU, memory, bandwidth, etc.), a new multi-criteria decision making based modelis proposed to detect hotspot and make decisions of VM scheduling in virtualized datacenter. By incorporating system eigenvalue with different dimensions into the unifieddecision model, it can score the servers and VM in system. Moreover, a HDT ispredefined and applied to detect both server hotspot and VM hotspot. If the server’sscore is higher than HDT, we select suitable VM and shift them to remove hotspot.When VM’s score is higher than HDT, the VM’s resource is reallocated to removehotspot if it is possible; else the VM would be migrated to other server. Experimentalresults prove the efficiency of the model.5. Since that CMOS multi-core embedded processor only provides global DVS andits leakage power is negligible, this work proposes a new power-aware scheduling algorithm for hard real-time tasks in multi-core embedded environment, calledGRR&CS. The power saving is achieved by three steps including greedy-based statictasks partition, and global resource reclamation based dynamic load balance anddynamic core scaling. The algorithm also keeps the schedulability of tasks. Experimentsshow that the proposed algorithm saves14.8%-41.2%energy more than other existingworks.