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Bolt: faster reconfiguration in operating systems
- In Proc. USENIX Annual Technical Conference
, 2015
"... Abstract Dynamic resource scaling enables provisioning extra resources during peak loads and saving energy by reclaiming those resources during off-peak times. Scaling the number of CPU cores is particularly valuable as it allows power savings during low-usage periods. Current systems perform scali ..."
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Abstract Dynamic resource scaling enables provisioning extra resources during peak loads and saving energy by reclaiming those resources during off-peak times. Scaling the number of CPU cores is particularly valuable as it allows power savings during low-usage periods. Current systems perform scaling with a slow hotplug mechanism, which was primarily designed to remove or replace faulty cores. The high cost of scaling is reflected in power management policies that perform scaling at coarser time scales to amortize the high reconfiguration latency. We describe Bolt, a new mechanism built on existing hotplug infrastructure to reduce scaling latency. Bolt also supports a new bulk interface to add or remove multiple cores at once. We implemented Bolt for x86 and ARM architectures. Our evaluation shows that Bolt can achieve over 20x speedup for entering offline state. While turning on CPUs, Bolt achieve speedups of 1.3x and 21x for x86 and ARM. The speedup is limited by high latency hardware intialization. On an ideal processor with zerolatency initialization, the speedup on x86 rises to 10x.
Accurate CPU Power Modeling for Multicore Smartphones
"... ABSTRACT CPU is a major source of power consumption in smartphones. Power modeling is a key technology to understand CPU power consumption and also an important tool for power management on smartphones. However, we have found that existing CPU power models on smartphones are ill-suited for modern m ..."
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ABSTRACT CPU is a major source of power consumption in smartphones. Power modeling is a key technology to understand CPU power consumption and also an important tool for power management on smartphones. However, we have found that existing CPU power models on smartphones are ill-suited for modern multicore CPUs: they can give high estimation errors (up to 34%) and high estimation accuracy variation (more than 30%) for different types of workloads on mainstream multicore smartphones. The root cause is that those models estimate the power consumption of a CPU based on only frequency and utilization of the CPU, but do not consider CPU idle power states. However, we have found that CPU idle power states play a critical role in power consumption of modern multicore CPUs. Therefore, we have developed a new approach for CPU power modeling, which takes CPU idle power states into consideration, and thus can significantly improve the power estimation accuracy and stability for multicore smartphones. We present the detailed design of our power modeling approach and a prototype implementation on commercial multicore smartphones. Evaluation results show that our approach consistently achieves a high average accuracy of 98% for various benchmarks, and 96% for real applications, which significantly outperforms the existing approaches.
Prebaked µVMs: Scalable, Instant VM Startup for IaaS Clouds
"... Abstract-IaaS clouds promise instantaneously available resources to elastic applications. In practice, however, virtual machine (VM) startup times are in the order of several minutes, or at best, several tens of seconds, negatively impacting the elasticity of applications like Web servers that need ..."
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Abstract-IaaS clouds promise instantaneously available resources to elastic applications. In practice, however, virtual machine (VM) startup times are in the order of several minutes, or at best, several tens of seconds, negatively impacting the elasticity of applications like Web servers that need to scale out to handle dynamically increasing load. VM startup time is strongly influenced by booting the VM's operating system. In this work, we propose using so-called prebaked µVMs to speed up VM startup. µVMs are snapshots of minimal VMs that can be quickly resumed and then configured to application needs by hot-plugging resources. To serve µVMs, we extend our VM boot cache service, Squirrel, allowing to store µVMs for large numbers of VM images on the hosts of a data center. Our experiments show that µVMs can start up in less than one second on a standard file system. Using 1000+ VM images from a production cloud, we show that the respective µVMs can be stored in a compressed and deduplicated file system within 50 GB storage per host, while starting up within 2-3 seconds on average.
USENIX Association 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’14) 17 Decoupling Cores, Kernels, and Operating Systems
"... We present Barrelfish/DC, an extension to the Bar-relfish OS which decouples physical cores from a native OS kernel, and furthermore the kernel itself from the rest of the OS and application state. In Barrelfish/DC, native kernel code on any core can be quickly replaced, kernel state moved between c ..."
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We present Barrelfish/DC, an extension to the Bar-relfish OS which decouples physical cores from a native OS kernel, and furthermore the kernel itself from the rest of the OS and application state. In Barrelfish/DC, native kernel code on any core can be quickly replaced, kernel state moved between cores, and cores added and removed from the system transparently to applications and OS processes, which continue to execute. Barrelfish/DC is a multikernel with two novel ideas: the use of boot drivers to abstract cores as regular devices, and a partitioned capability system for memory management which externalizes core-local kernel state. We show by performance measurements of real appli-cations and device drivers that the approach is practical enough to be used for a number of purposes, such as online kernel upgrades, and temporarily delivering hard real-time performance by executing a process under a specialized, single-application kernel. 1
Performance-oriented Cloud Provisioning: Taxonomy and Survey
, 2014
"... Abstract—Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such ..."
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Abstract—Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.
