With the ever-growing need of data in HPC applications, the congestion at the I O level becomes critical in super-computers. Architectural enhancement such as burst-buffers and pre-fetching are added to machines, but are not sufficient to prevent congestion. Recent online I O scheduling strategies have been put in place, but they add an additional congestion point and overheads in the computation of applications. In this work, we show how to take advantage of the periodic nature of HPC applications in order to develop efficient periodic scheduling strategies for their I O transfers. Our strategy computes once during the job scheduling phase a pattern where it defines the I O behavior for each application, after which the applications run independently, transferring their I O at the specified times. Our strategy limits the amount of I O congestion at the I O node level and can be easily integrated into current job schedulers. We validate this model through extensive simulations and experiments by comparing it to state-of-the-art online solutions, showing that not only our scheduler has the advantage of being de-centralized and thus overcoming the overhead of online schedulers, but also that it performs better than these solutions, improving the application dilation up to 13 and the maximum system efficiency up to 18 .
While many other studies suggest that I O congestion is one of the main problems for future scale platforms @cite_15 @cite_17 , few papers focus on finding a solution at the platform level. Some paper consider application-side I O management and transformation (using aggregate nodes, compression etc) @cite_22 @cite_13 @cite_7 . We consider those work to be orthogonal to our work and able to work jointly. Recently, numerous works focus on using machine learning for auto tuning and performance studies @cite_10 @cite_20 . However these solution also work at the application level for IO-scheduling and do not have a global view of the I O requirements of the system and they need to be supported by a platform level I O management for better results.
@cite_22 @cite_7 @cite_10 @cite_15 @cite_13 @cite_20 @cite_17
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A significant percentage of the computing capacity of large-scale platforms is wasted because of interferences incurred by multiple applications that access a shared parallel file system concurrently. One solution to handling I O bursts enlarge-scale HPC systems is to absorb them at an intermediate storage layer consisting of burst buffers. However, our analysis of the Argonne's Mira system shows that burst buffers cannot prevent congestion at all times. Consequently, I O performances dramatically degraded, showing in some cases a decrease in I O throughput of 67 . In this paper, we analyze the effects of interference on application I O bandwidth and propose several scheduling techniques to mitigate congestion. We show through extensive experiments that our global I O scheduler is able to reduce the effects of congestion, even on systems where burst buffers are used, and can increase the overall system throughput up to 56 . We also show that it outperforms current Mira I O schedulers. Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88 within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. Distributed graph processing platforms have helped emerging application domains use commodity clusters and Clouds to analyze large graphs. Vertex-centric programming models like Google Pregel, and their subgraph-centric variants, specify data-parallel application logic for a single vertex or component that execute iteratively. The locality and balancing of components within partitions affects the performance of such platforms. We propose three partitioning strategies for a subgraph-centric model, and analyze their impact on CPU utilization, communication, iterations, and makespan. We analyze these using Breadth First Search and PageRank algorithms on powerlaw and spatio-planar graphs. They are validated on a commodity cluster using our GoFFish subgraph-centric platform, and compared against Apache Giraph vertex-centric platform. Our experiments show upto 8 times improvement in utilization resulting to upto 5 times improvement of overall makespan for flat and hierarchical partitioning over the default strategy due to improved machine utilization. Further, these also exhibit better horizontal scalability relative to Giraph. Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the real density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method. Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload. Unmatched computation and storage performance in new HPC systems have led to a plethora of I O optimizations ranging from application-side collective I O to network and disk-level request scheduling on the file system side. As we deal with ever larger machines, the interferences produced by multiple applications accessing a shared parallel file system in a concurrent manner become a major problem. These interferences often break single-application I O optimizations, dramatically degrading application I O performance and, as a result, lowering machine wide efficiency. This paper focuses on CALCioM, a framework that aims to mitigate I O interference through the dynamic selection of appropriate scheduling policies. CALCioM allows several applications running on a supercomputer to communicate and coordinate their I O strategy in order to avoid interfering with one another. In this work, we examine four I O strategies that can be accommodated in this framework: serializing, interrupting, interfering and coordinating. Experiments on Argonne's BG P Surveyor machine and on several clusters of the French Grid'5000 show how CALCioM can be used to efficiently and transparently improve the scheduling strategy between two otherwise interfering applications, given specified metrics of machine wide efficiency. Reading and writing data efficiently from storage systems is critical for high performance data-centric applications. These I O systems are being increasingly characterized by complex topologies and deeper memory hierarchies. Effective parallel I O solutions are needed to scale applications on current and future supercomputers. Data aggregation is an efficient approach consisting of electing some processes in charge of aggregating data from a set of neighbors and writing the aggregated data into storage. Thus, the bandwidth use can be optimized while the contention is reduced. In this work, we take into account the network topology for mapping aggregators and we propose an optimized buffering system in order to reduce the aggregation cost. We validate our approach using micro-benchmarks and the I O kernel of a large-scale cosmology simulation. We show improvements up to 15× faster for I O operations compared to a standard implementation of MPI I O. Developers of rapidly growing applications must be able to anticipate potential scalability problems before they cause performance issues in production environments. A new type of data independence, called scale independence, seeks to address this challenge by guaranteeing a bounded amount of work is required to execute all queries in an application, independent of the size of the underlying data. While optimization strategies have been developed to provide these guarantees for the class of queries that are scale-independent when executed using simple indexes, there are important queries for which such techniques are insufficient. Executing these more complex queries scale-independently requires precomputation using incrementally-maintained materialized views. However, since this precomputation effectively shifts some of the query processing burden from execution time to insertion time, a scale-independent system must be careful to ensure that storage and maintenance costs do not threaten scalability. In this paper, we describe a scale-independent view selection and maintenance system, which uses novel static analysis techniques that ensure that created views do not themselves become scaling bottlenecks. Finally, we present an empirical analysis that includes all the queries from the TPC-W benchmark and validates our implementation's ability to maintain nearly constant high-quantile query and update latency even as an application scales to hundreds of machines. SUMMARY Public Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines to reside in apparent isolation on the same physical host. Dynamically scaling applications running on IaaS clouds can lead to varied and unpredictable results because of the performance interference effects associated with co-located virtual machines. Determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial. One principle advantage exhibited by IaaS clouds over their traditional hosting counterparts is the ability to scale resources on-demand. However, a problem arises concerning resource allocation as to which resources should be added and removed when the underlying performance of the resource is in a constant state of flux. Decision theoretic frameworks such as Markov Decision Processes are particularly suited to decision making under uncertainty. By applying a temporal difference, reinforcement learning algorithm known as Q-learning, optimal scaling policies can be determined. Additionally, reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable. To address this challenge, we also present a novel parallel Q-learning approach aimed at reducing the time taken to determine optimal policies whilst learning online. Copyright © 2012 John Wiley & Sons, Ltd. Significant challenges exist for achieving peak or even consistent levels of performance when using IO systems at scale. They stem from sharing IO system resources across the processes of single largescale applications and or multiple simultaneous programs causing internal and external interference, which in turn, causes substantial reductions in IO performance. This paper presents interference effects measurements for two different file systems at multiple supercomputing sites. These measurements motivate developing a 'managed' IO approach using adaptive algorithms varying the IO system workload based on current levels and use areas. An implementation of these methods deployed for the shared, general scratch storage system on Oak Ridge National Laboratory machines achieves higher overall performance and less variability in both a typical usage environment and with artificially introduced levels of 'noise'. The latter serving to clearly delineate and illustrate potential problems arising from shared system usage and the advantages derived from actively managing it. Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. This paper presents a two-level hierarchical approach that combines an expert system (ES) with an elite particle swarm optimization (EPSO) to solve the unit commitment (UC) problem. Existing UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to large-scale, heavily constrained UC applications. In this work, an ES is developed to handle all constraints. The ES is initially used as a pre-dispatch tool to create a robust swarm. Then, the ES and EPSO are combined to seek the optimal solution. All constraints are incorporated and satisfied during the pre-dispatch and the evolution process. Hence, infeasible positions in the solution space will not be visited. The execution time of the proposed approach grows approximately linearly, rather than geometrically, with problem size. This feature is attractive in large-scale systems. The proposed approach is successfully applied to a popular test system of up to 100 units and the real Taipower 40-unit 168-h system. Both solution cost and execution time are superior to those of published methods. Total fuel costs of the proposed approach are several millions of dollars less than those of existing methods in the Taipower case. We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications. It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images from their hidden representations, in most commonly used network architectures. In this paper we show that this loss of information is not a necessary condition to learn representations that generalize well on complicated problems, such as ImageNet. Via a cascade of homeomorphic layers, we build the i-RevNet, a network that can be fully inverted up to the final projection onto the classes, i.e. no information is discarded. Building an invertible architecture is difficult, for example, because the local inversion is ill-conditioned, we overcome this by providing an explicit inverse. An analysis of i-RevNet’s learned representations suggests an explanation of the good accuracy by a progressive contraction and linear separation with depth. To shed light on the nature of the model learned by the i-RevNet we reconstruct linear interpolations between natural images representations. We present an auto-tuning system for optimizing I O performance of HDF5 applications and demonstrate its value across platforms, applications, and at scale. The system uses a genetic algorithm to search a large space of tunable parameters and to identify effective settings at all layers of the parallel I O stack. The parameter settings are applied transparently by the auto-tuning system via dynamically intercepted HDF5 calls. To validate our auto-tuning system, we applied it to three I O benchmarks (VPIC, VORPAL, and GCRM) that replicate the I O activity of their respective applications. We tested the system with different weak-scaling configurations (128, 2048, and 4096 CPU cores) that generate 30 GB to 1 TB of data, and executed these configurations on diverse HPC platforms (Cray XE6, IBM BG P, and Dell Cluster). In all cases, the auto-tuning framework identified tunable parameters that substantially improved write performance over default system settings. We consistently demonstrate I O write speedups between 2x and 100x for test configurations. Abstract In this paper, we present a unifying analysis for redundancy systems with cancel-on-start ( c . o . s . ) and cancel-on-complete ( c . o . c . ) with exponentially distributed service requirements. With c . o . s . ( c . o . c . ) all redundant copies are removed as soon as one of the copies starts (completes) service. As a consequence, c . o . s . does not waste any computing resources, as opposed to c . o . c . We show that the c . o . s . model is equivalent to a queueing system with multi-type jobs and servers, which was analyzed in , (2012), and show that c . o . c . (under the assumption of i.i.d. copies) can be analyzed by a generalization of , (2012) where state-dependent departure rates are permitted. This allows us to show that the stationary distribution for both the c . o . c . and c . o . s . models has a product form. We give a detailed first-time analysis for c . o . s and derive a closed form expression for important metrics like mean number of jobs in the system, and probability of waiting. We also note that the c . o . s . model is equivalent to Join-Shortest-Work queue with power of d (JSW( d )). In the latter, an incoming job is dispatched to the server with smallest workload among d randomly chosen ones. Thus, all our results apply mutatis-mutandis to JSW( d ). Comparing the performance of c . o . s . with that of c . o . c . with i.i.d. copies gives the unexpected conclusion (since c . o . s . does not waste any resources) that c . o . s . is worse in terms of mean number of jobs. As part of ancillary results, we illustrate that this is primarily due to the assumption of i.i.d. copies in case of c . o . c . (together with exponentially distributed requirements) and that such assumptions might lead to conclusions that are qualitatively different from that observed in practice. The use of virtualization is progressively accommodating diverse and unpredictable workloads as being adopted in virtual desktop and cloud computing environments. Since a virtual machine monitor lacks knowledge of each virtual machine, the unpredictableness of workloads makes resource allocation difficult. Particularly, virtual machine scheduling has a critical impact on I O performance in cases where the virtual machine monitor is agnostic about the internal workloads of virtual machines. This paper presents a task-aware virtual machine scheduling mechanism based on inference techniques using gray-box knowledge. The proposed mechanism infers the I O-boundness of guest-level tasks and correlates incoming events with I O-bound tasks. With this information, we introduce partial boosting, which is a priority boosting mechanism with task-level granularity, so that an I O-bound task is selectively scheduled to handle its incoming events promptly. Our technique focuses on improving the performance of I O-bound tasks within heterogeneous workloads by lightweight mechanisms with complete CPU fairness among virtual machines. All implementation is confined to the virtualization layer based on the Xen virtual machine monitor and the credit scheduler. We evaluate our prototype in terms of I O performance and CPU fairness over synthetic mixed workloads and realistic applications. Supporting massive device transmission is challenging in machine-to-machine (M2M) communications. Particularly, in event-driven M2M communications, a large number of devices become activated within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for random access control of M2M communication systems with bursty traffic. Instead of the observation in a single slot, the statistics of consecutive idle and collision slots are used in FASA to accelerate the tracking process of network status that is critical for optimizing S-ALOHA systems. With a design based on drift analysis, the estimate of the number of the active devices under FASA converges fast to the true value. Furthermore, by examining the T-slot drifts, we prove that the proposed FASA scheme is stable as long as the average arrival rate is smaller than e-1, in the sense that the Markov chain derived from the scheme is geometrically ergodic. Simulation results demonstrate that under highly bursty traffic, the proposed FASA scheme outperforms traditional additive schemes such as PB-ALOHA and achieves near-optimal performance in reducing access delays. Moreover, compared to multiplicative schemes, FASA shows its robustness under heavy traffic load in addition to better delay performance. The proliferation of high-volume time-critical Web services such as online trading calls for a scalable server design that allows meeting individual response-time guarantees of real time transactions. A main challenge is to honor these guarantees despite unpredictability in incoming server load. The extremely high volume of real-time service requests mandates constant-time scheduling and schedulability analysis algorithms (as opposed to polynomial or logarithmic ones in the number of current requests). The paper makes two major contributions towards developing an architecture and theoretical foundations for scalable real-time servers operating in dynamic environments. First, we derive a tight utilization bound for schedulability of aperiodic tasks (requests) that allows implementing a constant time schedulability test on the server. We demonstrate that Liu and Layland's schedulable utilization bound of ln 2 does not apply to aperiodic tasks, and prove that an optimal arrival-time independent scheduling policy will meet all aperiodic task deadlines if utilization is maintained below 1 1+ spl radic (1 2). Second, we show that aperiodic deadline-monotonic scheduling is the optimal arrival-time-independent scheduling policy for aperiodic tasks. This result is used to optimally prioritize server requests. Evaluation of a utilization control loop that maintains server utilization below the bound shows that the approach is effective in meeting all individual deadlines in a high performance real-time server. We investigate a wireless system of multiple cells, each having a downlink shared channel in support of high-speed packet data services. In practice, such a system consists of hierarchically organized entities including a central server, Base Stations (BSs), and Mobile Stations (MSs). Our goal is to improve global resource utilization and reduce regional congestion given asymmetric arrivals and departures of mobile users, a goal requiring load balancing among multiple cells. For this purpose, we propose a scalable cross-layer framework to coordinate packet-level scheduling, call-level cell-site selection and handoff, and system-level cell coverage based on load, throughput, and channel measurements. In this framework, an opportunistic scheduling algorithm--the weighted Alpha-Rule--exploits the gain of multiuser diversity in each cell independently, trading aggregate (mean) down-link throughput for fairness and minimum rate guarantees among MSs. Each MS adapts to its channel dynamics and the load fluctuations in neighboring cells, in accordance with MSs' mobility or their arrival and departure, by initiating load-aware handoff and cell-site selection. The central server adjusts schedulers of all cells to coordinate their coverage by prompting cell breathing or distributed MS handoffs. Across the whole system, BSs and MSs constantly monitor their load, throughput, or channel quality in order to facilitate the overall system coordination. Our specific contributions in such a framework are highlighted by the minimum-rate guaranteed weighted Alpha-Rule scheduling, the load-aware MS handoff cell-site selection, and the Media Access Control (MAC)-layer cell breathing. Our evaluations show that the proposed framework can improve global resource utilization and load balancing, resulting in a smaller blocking rate of MS arrivals without extra resources while the aggregate throughput remains roughly the same or improved at the hot-spots. Our simulation tests also show that the coordinated system is robust to dynamic load fluctuations and is scalable to both the system dimension and the size of MS population.