Browsing by Author "Zheng, Jie"

Now showing 1 - 6 of 6
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    COMMA: Coordinating the Migration of Multi-tier Applications
    (2014-11-24) Liu, Zhaolei; Ng, T. S. Eugene; Sripanidkulchai, Kunwadee; Zheng, Jie
    Multi-tier applications are widely deployed in today’s virtualized cloud computing environments. At the same time, management operations in these virtualized environments, such as load balancing, hardware maintenance, workload consolidation, etc., often make use of live virtual machine (VM) migration to control the placement of VMs. Although existing solutions are able to migrate a single VM efficiently, little attention has been devoted to migrating related VMs in multi-tier applications. Ignoring the relatedness of VMs during migration can lead to serious application performance degradation. This paper formulates the multi-tier application migration problem, and presents a new communication-impact driven coordinated approach, as well as a system called COMMA that realizes this approach. Through extensive testbed experiments, numerical analyses, and a demonstration of COMMA on Amazon EC2, we show that this approach is highly effective in minimizing migration’s impact on multi-tier applications’ performance.
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    Long-distance in-situ methane detection using near-infrared light-induced thermo-elastic spectroscopy
    (Elsevier, 2021) Hu, Lien; Zheng, Chuantao; Zhang, Minghui; Zheng, Kaiyuan; Zheng, Jie; Song, Zhanwei; Li, Xiuying; Zhang, Yu; Wang, Yiding; Tittel, Frank K.
    A wavelength-locked light-induced thermo-elastic spectroscopy (WL-LITES) gas sensor system was proposed for long-distance in-situ methane (CH4) detection using a fiber-coupled sensing probe. The wavelength-locked scheme was used to speed the sensor response without scanning the laser wavelength across the CH4 absorption line. A small-size piezoelectric quartz tuning fork (QTF) with a wide spectral response range was adopted to enhance the photo-thermal signal. The optical excitation parameters of the QTF were optimized based on experiment and simulation for improving the signal-to-noise ratio of the LITES technique. An Allan deviation analysis was employed to evaluate the limit of detection of the proposed sensor system. With a 0.3 s lock-in integration time and a ∼ 100 m optical fiber, the WL-LITES gas sensor system demonstrates a minimum detection limit (MDL) of ∼ 11 ppm in volume (ppmv) for CH4 detection, and the MDL can be further reduced to ∼ 1 ppmv with an averaging time of ∼ 35 s. A real-time in-situ monitoring of CH4 leakage reveals that the proposed sensor system can realize a fast response (< 12 s) for field application.
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    Pacer: Taking the Guesswork Out of Live Migrations in Hybrid Cloud Computing
    (2013-10-01) Zheng, Jie; Ng, T. S. Eugene; Sripanidkulchai, Kunwadee; Liu, Zhaolei
    Hybrid cloud computing, where private and public cloud resources are combined and applications can migrate freely, ushers in unprecedented flexibility for businesses. To unleash the benefits, commercial products already enable the live migration of full virtual machines (CPU, memory, disk, network) between distant cloud datacenters. Unfortunately, no live migration progress management system exists, leading to (1) guesswork over how long a migration might take and the inability to schedule dependent tasks accordingly; (2) unacceptable application degradations – application components could become split over distant cloud datacenters for an arbitrary period during migration; (3) inability to balance application performance and migration time – e.g. to finish migration later for less performance interference. Pacer is the first migration progress management system. Pacer’s techniques are based on robust and lightweight run-time measurements of system and workload characteristics, novel efficient and accurate analytic models for progress predictions, and online adaptation to maintain user-defined migration objectives for coordinated and timely migrations.
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    The Preliminary Design and Implementation of the Maestro Network Control Platform
    (2008-10-01) Cai, Zheng; Cox, Alan L.; Dinu, Florin; Ng, T. S. Eugene; Zheng, Jie
    Network operation is inherently complex because it consists of many functions such as routing, firewalling, VPN provisioning, traffic load-balancing, network maintenance, etc. To cope with this, network designers have created modular components to handle each function. Un fortunately, in reality, unavoidable dependencies exist between some of the components and they may interact accidentally. At the same time, some policies are realized by compositions of different components, but the methods of composition are ad hoc and fragile. In other words, there is no single mechanism for systematically governing the interactions between the various components. To address these problems, we propose a clean-late system called Maestro. Maestro is an “operating system” that orchestrates the network control applications that govern the behavior of a network, and directly controls the underlying network devices. Maestro provides abstractions for the modular implementation of network control applications, and is the first system to address the fundamental problems originating from the concurrent operations of network control applications, namely communication between applications, scheduling of application executions, feedback management, concurrency management, and network state transition management. As the networking industry moves towards building directly controllable devices like the OpenFlow Switch, we believe Maestro can become a common platform.
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    Virtual Machine Live Migration in Cloud Computing
    (2013-11-01) Zheng, Jie; Ng, T. S. Eugene; Cox, Alan L.; Jermaine, Christopher M.; Knightly, Edward W.; Sripanidkulchai, Kunwadee
    Hybrid cloud computing, where private and public cloud resources are combined and applications can migrate freely, ushers in unprecedented flexibility for businesses. To unleash the benefits, commercial products already enable the live migration of full virtual machines between distant cloud datacenters. Unfortunately, two problems exist. First, no {\em live migration progress management system} exists, leading to (1) guesswork over how long a migration might take and the inability to schedule dependent tasks accordingly; (2) unacceptable application degradations -- application components could become split over distant cloud datacenters for an arbitrary period during migration; (3) inability to balance application performance and migration time -- e.g. to finish migration later for less performance interference. Second, multi-tier application architectures are widely employed in today's virtualized cloud computing environments. Although existing solutions are able to migrate a single VM efficiently, little attention has been devoted to migrating related VMs in multi-tier applications. Ignoring the relatedness of VMs during migration can lead to serious application performance degradation. In this thesis, we design the first migration progress management system called Pacer. Pacer's techniques are based on robust and lightweight run-time measurements of system and workload characteristics, efficient and accurate analytic models for progress predictions, and online adaptation to maintain user-defined migration objectives for coordinated and timely migrations. We formulates the multi-tier application migration problem, and presents a new communication-cost-driven coordinated approach, as well as a system called COMMA that realizes this approach. We experimentally show that using COMMA for the migration of a 3-tier application reduces the amount of inter-component communication impacted by migration by up to 475 times compared to naive parallel migration.
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    Workload-aware live storage migration for clouds
    (2010) Zheng, Jie; Ng, T. S. Eugene
    The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This thesis presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.