An energy conservation strategy must address two issues -placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
A Cloud computing system provides infrastructure layer services to users by managing virtualized infrastructure resources. The infrastructure resources include CPU, hypervisor, storage, and networking. Each category of infrastructure resources is a subsystem in a cloud computing system. The cloud computing system coordinates infrastructure subsystems to provide services to users.Most current cloud computing systems lacks pluggability in their infrastructure subsystems and decision algorithms, which restricts the development of infrastructure subsystems and decision algorithms in cloud computing system. A cloud computing system should have the flexibility to switch from one infrastructure subsystem to another, and one decision algorithm to another with ease. This paper describes Roystonea, a hierarchical distributed cloud computing system with pluggable component architecture. The component pluggability ability gives administrators the flexibility to use the most appropriate subsystem as they wish. The component pluggability of Roystonea is based on a specifically designed interfaces among Roystonea controlling system and infrastructure subsystems components. The component pluggability also encourages the development of infrastructure subsystems in cloud computing.Roystonea provides a testbed for designing decision algorithms used in cloud computing system. The decision algorithms are totally isolated from other components in Roystonea architecture, so the designers of the decision algorithms can focus on algorithm design without worrying about how his algorithm will interact with other Roystonea components. We believed that component pluggability will be one of the most important issues in the research of cloud computing system.
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