Progress of research efforts in a novel technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant components of this technology and their relationships. Cloud computing is one contemporary technology in which the research community has recently embarked. Manifesting itself as the descendant of several other computing research areas such as service-oriented architecture, distributed and grid computing, and virtualization, cloud computing inherits their advancements and limitations. Towards the end-goal of a thorough comprehension of the field of cloud computing, and a more rapid adoption from the scientific community, we propose in this paper an ontology of this area which demonstrates a dissection of the cloud into five main layers, and illustrates their interrelations as well as their inter-dependency on preceding technologies. The contribution of this paper lies in being one of the first attempts to establish a detailed ontology of the cloud. Better comprehension of the technology would enable the community to design more efficient portals and gateways for the cloud, and facilitate the adoption of this novel computing approach in scientific environments. In turn, this will assist the scientific community to expedite its contributions and insights into this evolving computing field.

https://smestorage.com/files/8f047da34a2d3a3528136ba8b59a465d.pdf

Toward a Unified Ontology of Cloud Computing

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.

/pdf/virtualpower-coordinated-power-management-in-virtualized-2cmacwtqnd.pdf

VirtualPower: coordinated power management in virtualized enterprise systems

A provisioning server automatically configures a virtual machine (VM) according to user specifications and then deploys the VM on a physical host. The user may either choose from a list of pre-configured, ready-to-deploy VMs, or he may select which hardware, operating system and application(s) he would like the VM to have. The provisioning server then configures the VM accordingly, if the desired configuration is available, or it applies heuristics to configure a VM that best matches the user's request if it isn't. The invention also includes mechanisms for monitoring the status of VMs and hosts, for migrating VMs between hosts, and for creating a network of VMs.

Provisioning of computer systems using virtual machines

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.

/pdf/q-clouds-managing-performance-interference-effects-for-qos-3xaqkj245y.pdf

Q-clouds: managing performance interference effects for QoS-aware clouds

As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem. In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of “pressure” to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at “sensible” co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.

/pdf/bubble-up-increasing-utilization-in-modern-warehouse-scale-4sumeu2h5q.pdf

Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

Virtualization is an essential technology in modern datacenters Despite advantages such as security isolation, fault isolation, and environment isolation, current virtualization techniques do not provide effective performance isolation between virtual machines (VMs) Specifically, hidden contention for physical resources impacts performance differently in different workload configurations, causing significant variance in observed system throughput To this end, characterizing workloads that generate performance interference is important in order to maximize overall utility In this paper, we study the effects of performance interference by looking at system-level workload characteristics In a physical host, we allocate two VMs, each of which runs a sample application chosen from a wide range of benchmark and real-world workloads For each combination, we collect performance metrics and runtime characteristics using an instrumented Ken hypervisor Through subsequent analysis of collected data, we identify clusters of applications that generate certain types of performance interference Furthermore, we develop mathematical models to predict the performance of a new application from its workload characteristics Our evaluation shows our techniques were able to predict performance with average error of approximately 5%

An Analysis of Performance Interference Effects in Virtual Environments

The number of cores integrated onto a single die is expected to climb steadily in the foreseeable future. This move to many-core chips is driven by a need to optimize performance per watt. How best to connect these cores and how to program the resulting many-core processor, however, is an open research question. Designs vary from GPUs to cache-coherent shared memory multiprocessors to pure distributed memory chips. The 48-core SCC processor reported in this paper is an intermediate case, sharing traits of message passing and shared memory architectures. The hardware has been described elsewhere. In this paper, we describe the programmer's view of this chip. In particular we describe RCCE: the native message passing model created for the SCC processor.

The 48-core SCC Processor: the Programmer's View

Today's operating systems don't adequately handle the complexities of Multicore processors. Architectural features confound existing OS techniques for task scheduling, load balancing, and power management. This article shows that the OS can use data obtained from dynamic runtime observation of task behavior to ameliorate performance variability and more effectively exploit multicore processor resources. The authors' research prototypes demonstrate the utility of observation-based policy.

Using OS Observations to Improve Performance in Multicore Systems

A heterogeneous processor consists of cores that are asymmetric in performance and functionality. Such a design provides a cost-effective solution for processor manufacturers to continuously improve both single-thread performance and multi-thread throughput. This design, however, faces significant challenges in the operating system, which traditionally assumes only homogeneous hardware. This paper presents a comprehensive study of OS support for heterogeneous architectures in which cores have asymmetric performance and overlapping, but non-identical instruction sets. Our algorithms allow applications to transparently execute and fairly share different types of cores. We have implemented these algorithms in the Linux 2.6.24 kernel and evaluated them on an actual heterogeneous platform. Evaluation results demonstrate that our designs efficiently manage heterogeneous hardware and enable significant performance improvements for a range of applications.

/pdf/operating-system-support-for-overlapping-isa-heterogeneous-4cy93zg27a.pdf

Paul Brett

Papers

An Analysis of Performance Interference Effects in Virtual Environments

The 48-core SCC Processor: the Programmer's View

Using OS Observations to Improve Performance in Multicore Systems

Operating system support for overlapping-ISA heterogeneous multi-core architectures

U os o i p m s