Enhancing virtual environments with QoS aware resource management

Abstract

Nowadays, the consolidation of application servers is the most common use for current virtualization solutions. Each application server takes the form of a virtual machine (VM) that can be hosted into one physical machine. In a default Xen implementation, the scheduler is configured to handle equally all of the VMs that run on a single machine. As a consequence, the scheduler shares equally all of the available physical CPU resources among the running VMs. However, when the applications that run in the VM dynamically change their resource requirements, a different solution is needed. Furthermore, if the resource usage is associated with service-level agreements, a predefined equal share of the processor power is insufficient for the VMs. Within the Xen's primitives, even though it is possible to tune the scheduler parameters, there is no tool to achieve the dynamic change of the share of the processor power assigned to each VM. A combination of a number of primitives, however, appears to be suited as a base for achieving this. In this paper, we present an approach to efficiently manage the quality of service (QoS) of virtualized resources in multicore machines. We evaluate different alternatives within Xen for building an enhanced management of virtual CPU resources. We compare these alternatives in terms of performance, flexibility, and ease of use. We devise an architecture to build a high-level service that combines interdomain communication mechanisms with monitoring and control primitives for local resource management. We achieve this by our solution, a local resource manager (LRM), which adjusts the resources needed by each VM according to an agreed QoS. The LRM has been implemented as a prototype and deployed on Xen-virtualized machines. By means of experiments, we show that the implemented management component can meet the service-level objectives even under dynamic conditions by adapting the resources assigned to the virtualized machines according to demand. With the LRM, we therefore achieve both fine-grain resource allocation and efficient assignment.

Resource management framework for collaborative computing systems over multiple virtual machines

“…To ease the burden of administrators, when we deploy ATN access control policies and credentials in virtual computing environment, some scholars proposed an automated trusted negotiation architecture called virtual automated trust negotiation (VATN) to centralize ATN policies and credentials for multiple virtual machines in a physical node into a privileged virtual machine [34]. In addition to that, some scholars investigated spatio-temporal thermal aware job scheduling in order to improve the temperature distribution within data centers and consequently, the energy efficiency [35]. Some scholars employed a trace-based work load placement controller that uses historical information to periodically and proactively reassign work loads to servers subject to their quality of service objectives [36].…”

Formal Description of the Virtual Machines System Framework

“…There are also some work on dynamically allocate CPU resources. Fernando Rodríguez-Haro et al [18] present a resources management approach to support dynamically change resource requirements of VMs with monitoring and control primitives. XMM [19] is a resources management tool to support real time task.…”

WARS: A workload-aware CPU resources scheduling for the cloud computing environment

“…Fernando Rodrguez-Haro et al [5] used Xen primitives to construct an applicationaware management component that adjusts the CPU resources according to demand. By integrating the lightweight metering and controller components, they hide the low-level primitives and enhance the CPU management through high QoS parameters such as needed CPU cycles and application metrics.…”