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Che Renhai - One of the best experts on this subject based on the ideXlab platform.
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Hardware/software codesign for performance and lifetime enhancement in NAND-flash-based embedded storage systems
The Hong Kong Polytechnic University, 2016Co-Authors: Che RenhaiAbstract:PolyU Library Call No.: [THS] LG51 .H577P COMP 2016 Chenxvi, 121 pages :color illustrationsEmbedded systems (e.g. smartphones) have become an integral part in peoples' daily life. In the last several years, most of efforts in improving embedded systems have been focusing on enhancing the network performance and CPU speed. On the other hand, the performance of NAND-flash-based storage in embedded systems is stagnant, and has become one of the major performance bottlenecks in embedded systems. Even worse, the performance and lifetimes ofNAND flash memory are substantially degraded with the advent of multi-level cell and triple-level cell flash memory for increasing the capacity. In this thesis, we have addressed these issues from several aspects including the integration of the emerging hardware and the cross-layer software management for the optimization of the performance and lifetime. First, we focus on employing the self-healing NAND flash memory to improve the lifetime and performance. Researchers have recently discovered that heating can cause worn-out NAND flash cells to become reusable and greatly extend the lifetime of flash memory cells. However, the heating process consumes a substantial amount of power, and some fundamental changes are required for existing NAND flash management techniques. In particular, all existing wear-leveling techniques are based on the principle of evenly distributing writes and erases. For self-healing NAND flash, this may cause NAND flash cells to be worn out in a short period of time. Moreover, frequently healing these cells may drain the energy quickly in battery-driven mobile devices, which is defined as the concentrated heating problem. We propose a novel wear-leveling scheme called DHeating (Dispersed Heating) to address the problem. In DHeating, rather than evenly distributing writes and erases over a time period, write and erase operations are scheduled on a small number of flash memory cells at a time, so that these cells can be worn out and healed much earlier than other cells. In this way, we can avoid quick energy depletion caused by concentrated heating. In addition, the heating process takes several seconds and has become the new performance bottleneck. In order to address this issue, we propose a lazy heating repair scheme. The lazy heating repair scheme can ease the long time heating effect by delaying the heating operation and using the system idle time to repair. Furthermore, the flash memory's reliability becomes worse with the flash memory cells reaching the expected worn-out time. We propose an early heating strategy to solve the reliability problem. With the extended lifetime provided by self-healing, we can trade some lifetimes for reliability. The idea is to start the healing process earlier than the expected worn-out time. We evaluate our scheme based on an embedded platform. The experimental results show that the proposed scheme can effectively prolong the consecutive heating time interval, alleviate the long time heating effect, and enhance the reliability for self-healing flash memory.Second, we jointly optimize the NAND flash memory's lifetime and performance with the integration of NVMs. Novel NVMs (non-volatile memories), such as PCM (Phase Change Memory) and STT-RAM (Spin-TransferTorque Random Access Memory), can provide fast read/write operations. In this thesis, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, vFlash (Virtualized Flash), is also proposed to manage the unified architecture. Within vFlash, inter-app and intra-app techniques are proposed to optimize the Application performance by exploiting the historical locality and I/O access patterns of Applications. Since vFlash is on the bottom of the I/O stack, the Application features willbe lost. Therefore, we also propose a cross-layer technique to transfer the Application Information from the Application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. Third, we study the problem of performance and lifetime enhancement in the mobile virtualization environment. Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This thesis targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this thesis, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.Department of ComputingPh.D., Department of Computing, The Hong Kong Polytechnic University, 2016Doctorat
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Hardware/software codesign for performance and lifetime enhancement in NAND-flash-based embedded storage systems
The Hong Kong Polytechnic University, 2016Co-Authors: Che RenhaiAbstract:PolyU Library Call No.: [THS] LG51 .H577P COMP 2016 Chenxvi, 121 pages :color illustrationsEmbedded systems (e.g. smartphones) have become an integral part in peoples' daily life. In the last several years, most of efforts in improving embedded systems have been focusing on enhancing the network performance and CPU speed. On the other hand, the performance of NAND-flash-based storage in embedded systems is stagnant, and has become one of the major performance bottlenecks in embedded systems. Even worse, the performance and lifetimes ofNAND flash memory are substantially degraded with the advent of multi-level cell and triple-level cell flash memory for increasing the capacity. In this thesis, we have addressed these issues from several aspects including the integration of the emerging hardware and the cross-layer software management for the optimization of the performance and lifetime. First, we focus on employing the self-healing NAND flash memory to improve the lifetime and performance. Researchers have recently discovered that heating can cause worn-out NAND flash cells to become reusable and greatly extend the lifetime of flash memory cells. However, the heating process consumes a substantial amount of power, and some fundamental changes are required for existing NAND flash management techniques. In particular, all existing wear-leveling techniques are based on the principle of evenly distributing writes and erases. For self-healing NAND flash, this may cause NAND flash cells to be worn out in a short period of time. Moreover, frequently healing these cells may drain the energy quickly in battery-driven mobile devices, which is defined as the concentrated heating problem. We propose a novel wear-leveling scheme called DHeating (Dispersed Heating) to address the problem. In DHeating, rather than evenly distributing writes and erases over a time period, write and erase operations are scheduled on a small number of flash memory cells at a time, so that these cells can be worn out and healed much earlier than other cells. In this way, we can avoid quick energy depletion caused by concentrated heating. In addition, the heating process takes several seconds and has become the new performance bottleneck. In order to address this issue, we propose a lazy heating repair scheme. The lazy heating repair scheme can ease the long time heating effect by delaying the heating operation and using the system idle time to repair. Furthermore, the flash memory's reliability becomes worse with the flash memory cells reaching the expected worn-out time. We propose an early heating strategy to solve the reliability problem. With the extended lifetime provided by self-healing, we can trade some lifetimes for reliability. The idea is to start the healing process earlier than the expected worn-out time. We evaluate our scheme based on an embedded platform. The experimental results show that the proposed scheme can effectively prolong the consecutive heating time interval, alleviate the long time heating effect, and enhance the reliability for self-healing flash memory.Second, we jointly optimize the NAND flash memory's lifetime and performance with the integration of NVMs. Novel NVMs (non-volatile memories), such as PCM (Phase Change Memory) and STT-RAM (Spin-TransferTorque Random Access Memory), can provide fast read/write operations. In this thesis, we propose a unified NVM/flash architecture to improve the I/O performance. A transparent scheme, vFlash (Virtualized Flash), is also proposed to manage the unified architecture. Within vFlash, inter-app and intra-app techniques are proposed to optimize the Application performance by exploiting the historical locality and I/O access patterns of Applications. Since vFlash is on the bottom of the I/O stack, the Application features willbe lost. Therefore, we also propose a cross-layer technique to transfer the Application Information from the Application layer to the vFlash layer. The proposed scheme is evaluated based on an Android platform, and the experimental results show that the proposed scheme can effectively improve the I/O performance of mobile devices. Third, we study the problem of performance and lifetime enhancement in the mobile virtualization environment. Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This thesis targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this thesis, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.Department of ComputingPh.D., Department of Computing, The Hong Kong Polytechnic University, 2016Doctoratepublished_fina
Phuoc Tran-gia - One of the best experts on this subject based on the ideXlab platform.
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SDN-based Application-aware networking on the example of youtube video streaming
Proceedings - 2013 2nd European Workshop on Software Defined Networks, EWSDN 2013, 2013Co-Authors: Michael Jarschel, Thomas Hohn, Florian Wamser, Thomas Zinner, Phuoc Tran-giaAbstract:Application-Aware Networking is a promising approach to provide good Application quality to users in scenarios with limited network resources, like today's access networks. With SDN, a particularly interesting method to enable flowbased traffic management in networks has become available. In this work we take a look at how a specific Application, i.e., YouTube Streaming, can benefit from such an SDN-based Application-Aware Network. We implement and investigate an approach based on Deep Packet Inspection (DPI) and one based on direct Information input from the Application in an OpenFlow testbed in order to show, how these different types of Application Information can be exploited to enhance the Quality of Experience (QoE). Furthermore, we determine the overhead caused by each of the presented approaches.
Peter Petrov - One of the best experts on this subject based on the ideXlab platform.
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rapid and low cost context switch through embedded processor customization for real time and control Applications
Design Automation Conference, 2006Co-Authors: Xiangrong Zhou, Peter PetrovAbstract:In this paper, we present a methodology for low-cost and rapid context switch for multithreaded embedded processors with real-time guarantees. Context-switch, which involves saving and restoring the thread state, has constituted not only a large performance overhead for many multithreaded embedded systems, but also an obstacle creating a significant delay in the response time for many time-critical control Applications. The proposed technique exploits Application Information extracted during compile time to make sure that only a minimal amount of thread state is saved and subsequently restored on preemption. The register liveness within the Application inner loops is analyzed and a few points, referred to as switch points, are identified where the program has minimal number of live registers. At run-time the preemption point is deferred to a switch point and the Real-Time Operating System (RTOS) kernel invokes a switch point specific code generated by the compiler to save and restore the thread state in a custom fashion. Through the utilization of these novel mechanisms, a drastic improvement on both performance and response time is achieved. The presented experimental results demonstrate the effectiveness of the proposed technique on a number of widely-used computational kernels and embedded Applications.
Neil C Rowe - One of the best experts on this subject based on the ideXlab platform.
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using local optimality criteria for efficient Information retrieval with redundant Information filters
arXiv: Information Retrieval, 1998Co-Authors: Neil C RoweAbstract:We consider Information retrieval when the data, for instance multimedia, is coputationally expensive to fetch. Our approach uses "Information filters" to considerably narrow the universe of possiblities before retrieval. We are especially interested in redundant Information filters that save time over more general but more costly filters. Efficient retrieval requires that decision must be made about the necessity, order, and concurrent processing of proposed filters (an "execution plan"). We develop simple polynomial-time local criteria for optimal execution plans, and show that most forms of concurrency are suboptimal with Information filters. Although the general problem of finding an optimal execution plan is likely exponential in the number of filters, we show experimentally that our local optimality criteria, used in a polynomial-time algorithm, nearly always find the global optimum with 15 filters or less, a sufficient number of filters for most Applications. Our methods do not require special hardware and avoid the high processor idleness that is characteristic of massive parallelism solutions to this problem. We apply our ideas to an important Application, Information retrieval of cpationed data using natural-language understanding, a problem for which the natural-language processing can be the bottleneck if not implemented well.
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using local optimality criteria for efficient Information retrieval with redundant Information filters
ACM Transactions on Information Systems, 1996Co-Authors: Neil C RoweAbstract:We consider Information retrieval when the data—for instance, multimedia—is computationally expensive to fetch. Our approach uses “Information filters” to considerably narrow the universe of possibilities before retrieval. We are especially interested in redundant Information filters that save time over more general but more costly filters. Efficient retrieval requires that decisions must be made about the necessity, order, and concurrent processing of proposed filters (an “execution plan”). We develop simple polynomial-time local criteria for optimal execution plans and show that most forms of concurrency are suboptimal with Information filters. Although the general problem of finding an optimal execution plan is likely to be exponential in the number of filters, we show experimentally that our local optimality criteria, used in a polynomial-time algorithm, nearly always find the global optimum with 15 filters or less, a sufficient number of filters for most Applications. Our methods require no special hardware and avoid the high processor idleness that is characteristic of massive-parallelism solutions to this problem. We apply our ideas to an important Application, Information retrieval of captioned data using natural-language understanding, a problem for which the natural-language processing can be the bottleneck if not implemented well.
Jia Wang - One of the best experts on this subject based on the ideXlab platform.
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lightweight Application classification for network management
ACM Special Interest Group on Data Communication, 2007Co-Authors: Hongbo Jiang, Andrew W Moore, Shudong Jin, Jia WangAbstract:Traffic Application classification is an essential step in the network management process to provide high availability of network services. However, network management has seen limited use of traffic classification because of the significant overheads of existing techniques. In this context we explore the feasibility and performance of lightweight traffic classification based on NetFlow records. In our experiments, the NetFlow records are created from packet-trace data and pre-tagged based upon packet content. This provides us with NetFlow records that are tagged with a high accuracy for ground-truth. Our experiments show that NetFlow records can be usefully employed for Application classification. We demonstrate that our machine learning technique is able to provide an identification accuracy (a 91%) that, while a little lower than that based upon previous packet-based machine learning work (> 95%), is significantly higher than the commonly used port-based approach (50--70%). Trade-offs such as the complexity of feature selection and packet sampling are also studied. We conclude that a lightweight mechanism of classification can provide Application Information with a considerably high accuracy, and can be a useful practice towards more effective network management.