Energy-Efficient High-Throughput Data Transfers via Dynamic CPU Frequency and Core Scaling

The energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. Depending on the number of switches, routers, and hubs between the source and destination nodes, the networking infrastructure consumes 10% - 75% of the total energy during active data transfers, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application level parameters such as parallelism, pipelining, and concurrency. In this paper, we introduce three novel application-level parameter tuning algorithms which employ dynamic CPU frequency and core scaling, combining heuristics and runtime measurements to achieve energy efficient data transfers. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 48% reduced energy consumption and 80% higher throughput

Continuous Partial Quorums for Consistency-Latency Tuning in Distributed NoSQL Storage Systems

NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases when the need for scalability outweighs the need for transactions. Several of these systems provide quorum-based replication and present the application developer with a choice of multiple client-side "consistency levels" that determine the number of replicas accessed by reads and writes, which in turn affects both latency and the consistency observed by the client application. Since using a fixed combination of read and write consistency levels for a given application provides only a limited number of discrete options, we investigate techniques that allow more fine-grained tuning of the consistency-latency trade-off, as may be required to support consistency-based service level agreements (SLAs). We propose a novel technique called \emph{continuous partial quorums} (CPQ) that assigns the consistency level on a per-operation basis by choosing randomly between two options, such as eventual and strong consistency, with a tunable probability. We evaluate our technique experimentally using Apache Cassandra and demonstrate that it outperforms an alternative tuning technique that delays operations artificially.Comment: 6 page

Towards automated web application logic reconstruction for application level security

Modern overlay security mechanisms like Web Application Firewalls (WAF) suffer from inability to recognize custom high-level application logic and data objects, which results in low accuracy, high false positives rates, and overhelming manual effort for fine tuning. In this paper we propose an approach to web application modeling for security purposes that could help next-generation WAFs to adapt to specific web applications, and do it automatically whenever possible. We aim at creating multi-layer models that adequately simulate various aspects of web application functionality that are significant for intrusion detection and prevention, including request parsing and routing, reconstruction of actions and data objects, and action interdependencies

Evaluation of the RIKEN Post-K Processor Simulator

For the purpose of developing applications for Post-K at an early stage, RIKEN has developed a post-K processor simulator. This simulator is based on the general-purpose processor simulator gem5. It does not simulate the actual hardware of a post-K processor. However, we believe that sufficient simulation accuracy can be obtained since it simulates the instruction pipeline of out-of-order execution with cycle-level accuracy along with performing detailed parameter tuning of out-of-order resources and function expansion of cache/memory hierarchy. In this simulator, we aim to estimate the execution cycles of one node application on a post-K processor with accuracy that enables relative evaluation and application tuning. In this paper, we show the details of the implementation of this simulator and verify its accuracy compared with that of a post-K test chip.Comment: 6 pages, 5 figure

Application Level High Speed Transfer Optimization Based on Historical Analysis and Real-time Tuning

Data-intensive scientific and commercial applications increasingly require frequent movement of large datasets from one site to the other(s). Despite growing network capacities, these data movements rarely achieve the promised data transfer rates of the underlying physical network due to poorly tuned data transfer protocols. Accurately and efficiently tuning the data transfer protocol parameters in a dynamically changing network environment is a major challenge and remains as an open research problem. In this paper, we present predictive end-to-end data transfer optimization algorithms based on historical data analysis and real-time background traffic probing, dubbed HARP. Most of the previous work in this area are solely based on real time network probing which results either in an excessive sampling overhead or fails to accurately predict the optimal transfer parameters. Combining historical data analysis with real time sampling enables our algorithms to tune the application level data transfer parameters accurately and efficiently to achieve close-to-optimal end-to-end data transfer throughput with very low overhead. Our experimental analysis over a variety of network settings shows that HARP outperforms existing solutions by up to 50% in terms of the achieved throughput

A Heuristic Approach to Protocol Tuning for High Performance Data Transfers

Obtaining optimal data transfer performance is of utmost importance to today's data-intensive distributed applications and wide-area data replication services. Doing so necessitates effectively utilizing available network bandwidth and resources, yet in practice transfers seldom reach the levels of utilization they potentially could. Tuning protocol parameters such as pipelining, parallelism, and concurrency can significantly increase utilization and performance, however determining the best settings for these parameters is a difficult problem, as network conditions can vary greatly between sites and over time. Nevertheless, it is an important problem, since poor tuning can cause either under- or over-utilization of network resources and thus degrade transfer performance. In this paper, we present three algorithms for application-level tuning of different protocol parameters for maximizing transfer throughput in wide-area networks. Our algorithms dynamically tune the number of parallel data streams per file (for large file optimization), the level of control channel pipelining (for small file optimization), and the number of concurrent file transfers to increase I/O throughput (a technique useful for all types of files). The proposed heuristic algorithms improve the transfer throughput up to 10x compared to the baseline and 7x compared to the state of the art solutions

A Novel Approach to Fine-Tuned Supersymmetric Standard Models -- Case of Non-Universal Higgs Masses model

Discarding the prejudice about fine tuning, we propose a novel and efficient approach to identify relevant regions of fundamental parameter space in supersymmetric models with some amount of fine tuning. The essential idea is the mapping of experimental constraints at a low energy scale, rather than the parameter sets, to those of the fundamental parameter space. Applying this method to the non-universal Higgs masses model, we identify a new interesting superparticle mass pattern where some of the first two generation squarks are light whilst the stops are kept heavy as 6TeV. Furthermore, as another application of this method, we show that the discrepancy of the muon anomalous magnetic dipole moment can be filled by a supersymmetric contribution within the 1 {\sigma} level of the experimental and theoretical errors, which was overlooked by the previous studies due to the required terrible fine tuning.Comment: 25 pages, 9 figure

LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models

Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smaller models and accuracy, etc. With deep learning, not only are the model's representations opaque, the model's behavior when parameters "knobs" are changed may also be unpredictable. Thus, picking the "best" model often requires time-consuming model comparison. In this work, we introduce LAMVI-2, a visual analytics system to support a developer in comparing hyperparameter settings and outcomes. By focusing on word-embedding models ("deep learning for text") we integrate views to compare both high-level statistics as well as internal model behaviors (e.g., comparing word 'distances'). We demonstrate how developers can work with LAMVI-2 to more quickly and accurately narrow down an appropriate and effective application-specific model

Energy-Efficient Data Transfer Algorithms for HTTP-Based Services

According to recent statistics, more than 1 zettabytes of data is moved over the Internet annually, which consumes several terawatt hours of electricity, and costs billions of US dollars to the world economy. HTTP protocol is used in the majority of these data transfers, accounting for 70% of the global Internet traffic. We claim that HTTP transfers, and the services based on HTTP, can become more energy efficient without any performance degradation by application-level tuning of certain protocol parameters. In this paper, we analyze several application-level parameters that affect the throughput and energy consumption in HTTP data transfers, such as the level of parallelism, concurrency, and pipelining. We introduce SLA-based algorithms which can decide the best combination of these parameters based on user-defined energy efficiency and performance criteria. Our experimental results show that up to 80% energy savings can be achieved at the client and server hosts during HTTP data transfers and the end-to-end data throughput can be increased at the same time

Hybrid Particle-Continuum Simulations Coupling Brownian Dynamics and Local Dynamic Density Functional Theory

We present a multiscale hybrid particle-field scheme for the simulation of relaxation and diffusion behavior of soft condensed matter systems. It combines particle-based Brownian dynamics and field-based local dynamics in an adaptive sense such that particles can switch their level of resolution on the fly. The switching of resolution is controlled by a tuning function which can be chosen at will according to the geometry of the system. As an application, the hybrid scheme is used to study the kinetics of interfacial broadening of a polymer blend, and is validated by comparing the results to the predictions from pure Brownian dynamics and pure local dynamics calculations.Comment: 10 Pages, 5 Figure