In hardware virtualization a hypervisor provides multiple Virtual Machines (VMs) on a single physical system, each executing a separate operating system instance. The hypervisor schedules execution of these VMs much as the scheduler in an operating system does, balancing factors such as fairness and I O performance. As in an operating system, the scheduler may be vulnerable to malicious behavior on the part of users seeking to deny service to others or maximize their own resource usage. Recently, publically available cloud computing services such as Amazon EC2 have used virtualization to provide customers with virtual machines running on the provider's hardware, typically charging by wall clock time rather than resources consumed. Under this business model, manipulation of the scheduler may allow theft of service at the expense of other customers, rather than merely reallocating resources within the same administrative domain. We describe a flaw in the Xen scheduler allowing virtual machines to consume almost all CPU time, in preference to other users, and demonstrate kernel-based and user-space versions of the attack. We show results demonstrating the vulnerability in the lab, consuming as much as 98 of CPU time regardless of fair share, as well as on Amazon EC2, where Xen modifications protect other users but still allow theft of service. In case of EC2, following the responsible disclosure model, we have reported this vulnerability to Amazon; they have since implemented a fix that we have tested and verified (See Appendix B). We provide a novel analysis of the necessary conditions for such attacks, and describe scheduler modifications to eliminate the vulnerability. We present experimental results demonstrating the effectiveness of these defenses while imposing negligible overhead.
Cherkasova and Gupta @cite_11 @cite_12 have done an extensive performance analysis of scheduling in the Xen VMM. They studied I O performance for the three schedulers: BVT, SEDF and Credit scheduler. Their work showed that both the CPU scheduling algorithm and the scheduler parameters drastically impact the I O performance. Furthermore, they stressed that the I O model on Xen remains an issue in resource allocation and accounting among VMs. Since Domain-0 is indirectly involved in servicing I O for guest domains, I O intensive domains may receive excess CPU resources by focusing on the processing resources used by Domain-0 on behalf of I O bound domains. To tackle this problem, Gupta @cite_20 introduced the SEDF-DC scheduler, derived from Xen's SEDF scheduler, that charges guest domains for the time spent in Domain-0 on their behalf.
@cite_20 @cite_12 @cite_11
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Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power Computation per Provisioned Watt (CPW). Our technique is able to double the CPWoffered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10 based on tails of power profiles yields a further improvement of 20 . Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec--a degradation of 12 . Virtual machines (VMs) have recently emerged as the basis for allocating resources in enterprise settings and hosting centers. One benefit of VMs in these environments is the ability to multiplex several operating systems on hardware based on dynamically changing system characteristics. However, such multiplexing must often be done while observing per-VM performance guarantees or service level agreements. Thus, one important requirement in this environment is effective performance isolation among VMs. In this paper, we address performance isolation across virtual machines in Xen [1]. For instance, while Xen can allocate fixed shares of CPU among competing VMs, it does not currently account for work done on behalf of individual VMs in device drivers. Thus, the behavior of one VM can negatively impact resources available to other VMs even if appropriate per-VM resource limits are in place. In this paper, we present the design and evaluation of a set of primitives implemented in Xen to address this issue. First, XenMon accurately measures per-VM resource consumption, including work done on behalf of a particular VM in Xen's driver domains. Next, our SEDF-DC scheduler accounts for aggregate VM resource consumption in allocating CPU. Finally, ShareGuard limits the total amount of resources consumed in privileged and driver domains based on administrator-specified limits. Our performance evaluation indicates that our mechanisms effectively enforce performance isolation for a variety of workloads and configurations. In the area of system architecture, there are two most significant trends: multi-core and system virtualization technology. Both of them have quite close relationship with energy efficient computing. Industry turns to integrate more cores on a single chip instead of increasing frequency to solve the heat problem. Meanwhile system virtualization could decrease the total power consumption by sharing the same platform among different operating systems. So the necessity of power management on the multi-core virtualization platform has become increasingly evident. However, traditional virtual machine monitor schedulers could not make efficient use of DVFS, and thus could not take it into considering that the guest OSes may run at different frequency. In order to address this problem, this paper designs a power efficient scheduler which uses the load and the power level of the guest OSes as feedback, and implements a prototype based on Xen virtual machine monitor. This scheduler allocates power credit to VCPU of each guest OS, accounts the power consumption of VCPU sat different speed levels and makes scheduler decision by integrating it to the credit oriented to CPU time slice sharing. It also uses utilization of processors as feedback and sets frequency according to the load change trends instead of the simple static relationship between load and frequency policies, so as to decrease the speed steps required by response to burst load change. Experiment results show that the scheduling fairness of guest OS improved when using DVFS as main power saving method, and the power consumption of the whole system can be reduced by 5 percent - 30 percent. Therefore, this framework for feedback scheduling could make efficient use of varieties of power saving methods and maintain ideal balance between overall system power saving and single core over heat. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.