Browsing by Author "Sripanidkulchai, Kunwadee"
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Item COMMA: Coordinating the Migration of Multi-tier Applications(2014-11-24) Liu, Zhaolei; Ng, T. S. Eugene; Sripanidkulchai, Kunwadee; Zheng, JieMulti-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.Item Pacer: Taking the Guesswork Out of Live Migrations in Hybrid Cloud Computing(2013-10-01) Zheng, Jie; Ng, T. S. Eugene; Sripanidkulchai, Kunwadee; Liu, ZhaoleiHybrid 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.Item 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, KunwadeeHybrid 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.Item λ group: Using Optics to Take Group Data Delivery in the Datacenter to the Next Degree(2014-02-17) Bergman, Keren; Ng, T. S. Eugene; Sripanidkulchai, Kunwadee; Wang, Howard; Xia, YitingThe increasing number of datacenter applications with heavy one-to-many communications has raised the need for an efficient group data delivery solution. This paper presents an unconventional clean-slate architecture called λ group that uses optical networking technologies to enable ultra-fast, energy-efficient, low cost, and highly reliable group data delivery in the datacenter. By replicating data purely optically, λ group enables reliable one-to-many communication independent of modulated data rate, while simultaneously being energy-efficient. λ group makes use of software defined network switches to facilitate direct interactions between applications and the network, eliminating the need for conventional multicast routing and group management protocols without sacrificing link-stress or latency performance. From an end-to-end perspective, λ group provides predictable latency and near-zero packet loss between application end points, allowing even simple end-to-end flow control and packet loss recovery mechanisms to work well. We implement a λ group prototype to measure its physical layer characteristics and end-to-end performance benefits to applications. Extensive simulations using synthetic traffic show λ group provides an order of magnitude performance improvement relative to a number of existing alternative group data delivery approaches.