As the trend of more cores sharing common resources on a single die and more systems crammed into enterprise computing space continue, optimizing the economies of scale for a given compute capacity is becoming more critical. One major challenge in performance scalability is the growing L2 cache contention caused by multiple contexts running on a multi-core processor either natively or under a virtual machine environment. Currently, an OS, at best, relies on history based affinity information to dispatch a process or thread onto a particular processor core. Unfortunately, this simple method can easily lead to destructive performance effect due to conflicts in common resources, thereby slowing down all processes. To ameliorate the allocation/management policy of a shared cache on a multi-core, in this paper, we propose Bloom filter signatures, a low-complexity architectural support to allow an OS or a Virtual Machine Monitor to infer cache footprint characteristics and interference of applications, and then perform job scheduling based on symbiosis. Our scheme integrates hardware-level counting Bloom filters in caches to efficiently summarize cache usage behavior on a per-core, per-process or per-VM basis. We then proposed and studied three resource allocation algorithms to determine the optimal process-to-core mapping to minimize interference in the L2. We executed applications using allocation generated by our new process to-core mapping algorithms on an Intel Core 2 Duo machine and showed an averaged 22% (up to 54%) improvement when applications run natively, and an averaged 9.5% improvement (up to 26%)when running inside VMs.

Cloud computing offers users the ability to access large pools of computational and storage resources on demand. Multiple commercial clouds already allow businesses to replace, or supplement, privately owned IT assets, alleviating them from the burden of managing and maintaining these facilities. However, there are issues that must be addressed before this vision of utility computing can be fully realized. In existing systems, customers are charged based upon the amount of resources used or reserved, but no guarantees are made regarding the application level performance or quality-of-service (QoS) that the given resources will provide. As cloud providers continue to utilize virtualization technologies in their systems, this can become problematic. In particular, the consolidation of multiple customer applications onto multicore servers introduces performance interference between collocated workloads, significantly impacting application QoS. To address this challenge, we advocate that the cloud should transparently provision additional resources as necessary to achieve the performance that customers would have realized if they were running in isolation. Accordingly, we have developed Q-Clouds, a QoS-aware control framework that tunes resource allocations to mitigate performance interference effects. Q-Clouds uses online feedback to build a multi-input multi-output (MIMO) model that captures performance interference interactions, and uses it to perform closed loop resource management. In addition, we utilize this functionality to allow applications to specify multiple levels of QoS as application Q-states. For such applications, Q-Clouds dynamically provisions underutilized resources to enable elevated QoS levels, thereby improving system efficiency. Experimental evaluations of our solution using benchmark applications illustrate the benefits: performance interference is mitigated completely when feasible, and system utilization is improved by up to 35% using Q-states.

Power consumption and cooling overheads are becoming increasingly significant for large scale machines, affecting overall costs and the ability to extend resource capacities and performance capabilities. To help mitigate these issues, active power management technologies are being deployed aggressively, including power budgeting, which enables improved power provisioning and can address critical periods when power delivery or cooling capabilities are temporarily reduced. Given the use of virtualization to encapsulate application components into virtual machines (VMs), however, such power management capabilities must address the interplay between budgeting physical resources and the performance of the virtual machines used to run these applications. This paper proposes a set of cluster- and datacenter-level management components and abstractions for use by power budgeting policies. The key idea is to manage power from a VM-centric point of view, where the goal is to be aware of global utility tradeoffs between different virtual machines (and their applications) when maintaining power constraints for the physical hardware on which they run. Our approach to VM-aware power budgeting uses multiple distributed managers integrated into the VirtualPower Management (VPM) framework whose actions are coordinated via a new abstraction, termed VPM tokens. An implementation with the Xen hypervisor illustrates technical benefits of VPM tokens that include up to 43% improvements in global utility, highlighting the ability to dynamically improve cluster performance while still meeting power budgets.

Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs `soft' versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.

Autonomous robot systems have to manage their energy wisely in order to complete their missions. Typical approaches seek to conserve energy by energy-efficient motion or sensor planning. This paper puts forth a distributed systems approach to power management. Specifically, it develops and presents AutoPower, which is a model that characterizes robot software systems' computation and communication energy behaviors. With AutoPower, it is possible to make principled decisions about (1) where to deploy software components across the distributed computing resources of autonomous robotic systems, and (2) how the different systems involved should communicate to best meet overall mission objectives. We showcase AutoPower by using a multi-robot search-and-rescue mission as a guiding application. For this scenario, application of the model shows that there are counter-intuitive energy trade-offs in configuring such application software. Further, by using AutoPower to guide deployment and interconnects at runtime, for certain configurations, overall computing system lifetimes can be increased by up to 57% over a base-line configuration

Mobile and embedded platforms have experienced dramatic advances in capabilities, largely due to the development of associated peripheral devices for storage and communication. The incorporation of these I/O devices has increased the overall power envelope of these platforms. In fact, system-level power consumption of mobile platforms is often dominated by peripheral devices. Since battery technologies alone have been unable to provide the lifetimes required by many platforms, in order to conserve energy, most devices provide the ability to transition into low power states during idle periods. The resulting energy savings are heavily dependent upon the lengths and number of idle periods experienced by a device. This paper presents an infrastructure designed to take advantage of device low power states by increasing the burstiness of device accesses and idle periods to provide a reduced power profile, and thereby an improvement in battery life. Our approach combines compiler-based source modifications with operating system support to implement a dynamic solution for enhanced energy consumption. We evaluate our infrastructure on an XScale-based embedded platform with a Linux implementation.

We study the delay performance of policed traffic to provide real-time guarantees over wireless networks. A number of models have been presented in the literature to describe wireless (radio or optical) networks in terms of the wireless channel and the underlying error control mechanisms. We describe a general framework to incorporate such models into delay guarantee computations for real-time traffic. Static-priority scheduling is considered, and two different admission control mechanisms are used to achieve the trade-off between resource utilization and admission overhead.