Data Cache Research Articles

Deadline Constrained Video Analysis via In-Transit Computational Environments

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

A Faster, More Intuitive RooFit

RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider as well as atBfactories. Larger datasets to be collected at e.g. the HighLuminosity LHC will enable measurements with higher precision, but will require faster data processing to keep fitting times stable. In this work, a simplification of RooFit’s interfaces and a redesign of its internal dataflow is presented. Interfaces are being extended to look and feel more STL-like to be more accessible both from C++ and Python to improve interoperability and ease of use, while maintaining compatibility with old code. The redesign of the dataflow improves cache locality and data loading, and can be used to process batches of data with vectorised SIMD computations. This reduces the time for computing unbinned likelihoods by a factor four to 16. This will allow to fit larger datasets of the future in the same time or faster than today’s fits.

Near-Threshold L1 Data Cache for Yield Management Under Process Variations

Near-threshold computing (NTC) has recently emerged and been considered as a strong candidate for future energy-efficient computing. However, adverse impacts from process variation such as delay and power fluctuations within die as well as across dies are much more severe than the super-threshold regime. In particular, static random access memory (SRAM)-based components (e.g., cache memories) are easily affected by process variation in NTC, resulting in large delay fluctuations. It incurs a huge loss in the maximum clock frequencies of processors, which eventually leads to huge yield losses. In this paper, we first analyze L1 data cache yield in NTC and reveal an inefficiency of frequency binning for yield improvement in NTC. We then introduce a variable latency L1 data cache for NTC to obtain a sufficient yield. By allowing the higher cache access cycles, we can improve cache yield with only a little performance overhead. Moreover, we propose an adaptive line migration technique which improves performance and energy efficiency of variable latency caches. The cache line which is expected to be frequently accessed in the near future is dynamically migrated to the fastest way in a cache set. According to our evaluation, our cache architecture greatly improves cache yield with only a little performance, energy, and area overhead.

Smart Caching at CMS: applying AI to XCache edge services

The projected Storage and Compute needs for the HL-LHC will be a factor up to 10 above what can be achieved by the evolution of current technology within a flat budget. The WLCG community is studying possible technical solutions to evolve the current computing in order to cope with the requirements; one of the main focus is resource optimization, with the ultimate aim of improving performance and efficiency, as well as simplifying and reducing operation costs. As of today the storage consolidation based on a Data Lake model is considered a good candidate for addressing HL-LHC data access challenges. The Data Lake model under evaluation can be seen as a logical system that hosts a distributed working set of analysis data. Compute power can be “close” to the lake, but also remote and thus completely external. In this context we expect data caching to play a central role as a technical solution to reduce the impact of latency and reduce network load. A geographically distributed caching layer will be functional to many satellite computing centers that might appear and disappear dynamically. In this talk we propose a system of caches, distributed at national level, describing both deployment and results of the studies made to measure the impact on the CPU efficiency. In this contribution, we also present the early results on novel caching strategy beyond the standard XRootD approach whose results will be a baseline for an AI-based smart caching system.

Enhanced cache sharing through cooperative data cache approach in MANET

In a mobile ad hoc network (MANET) under normal cache sharing scenarios, when the data is transmitted from source to destination, all the nodes along the path store the information on the cache layer before reaching the destination. This may result in increased node overhead, increased cache memory utility and very high end-to-end delay. In this paper, we propose an enhanced cache sharing through cooperative data cache (ECSCDC) approach for MANETs. During the transmission of desired data from the data centre back to the request originator, the data packets will be cached by the intermediate caching nodes only if required, by using the asymmetric cooperative cache approach. Those caching nodes that can retain the data in its cache, for future data retrieval is selected based on scaled power community index. By simulation results, we show that the proposed technique reduces the communication overhead, access latency and average traffic ratio near the data centre while increasing the cache hit ratio.

Enhanced cache sharing through cooperative data cache approach in MANET

Enhanced cache sharing through cooperative data cache approach in MANET

Computing activities at the Spanish Tier-1 and Tier-2s for the ATLAS experiment towards the LHC Run3 and High-Luminosity periods

The ATLAS Spanish Tier-1 and Tier-2s have more than 15 years of experience in the deployment and development of LHC computing components and their successful operations. The sites are already actively participating in, and even coordinating, emerging R&D computing activities and developing new computing models needed for the Run3 and HighLuminosity LHC periods. In this contribution, we present details on the integration of new components, such as High Performance Computing resources to execute ATLAS simulation workflows. The development of new techniques to improve efficiency in a cost-effective way, such as storage and CPU federations is shown in this document. Improvements in data organization, management and access through storage consolidations (“data-lakes”), the use of data caches, and improving experiment data catalogs, like Event Index, are explained in this proceeding. The design and deployment of new analysis facilities using GPUs together with CPUs and techniques like Machine Learning will also be presented. Tier-1 and Tier-2 sites, are, and will be, contributing to significant R&D in computing, evaluating different models for improving performance of computing and data storage capacity in the High-Luminosity LHC era.

ТЕХНОЛОГИЯ АКТУАЛИЗАЦИИ КЭШИРОВАННЫХ ДАННЫХ

The paper presents a method for updating data stored in cache using data caching algorithm based on the predicates truth spaces. A version of detailed audit of database changes has been developed to track the data state on the server. To check the relevance of the data, an algorithm has been developed that sends a request to the server in order to compare the data in the cache and the database and executes query to the server to get data in case the cached data is out of date

A New Framework for Mobile Edge Caching by Proposing Flexible User in Heterogeneous Cellular Networks

The bursting increase in requesting wireless data has caused several issues in network peak-traffic duration. This negatively results in significant data delivery delay imposed on users that can eventually impact the network's quality of service and users' quality of experience. In this research, regarding mobile edge caching as a potential solution to decrease such delay, we propose a new framework in which we introduce the concept of the flexible user where he requests for a set of multiple files from the library with a unique feature, e.g., 5 movies within comedy genre from the library in the peak-traffic duration. The satisfactory criterion for the flexible user is to receive any of the files within the requested set. This definition of the flexible user indicates a new concept which captures interesting scenarios. In order to model this concept, we generalize the conventional Zipf distribution to a multivariate one as the modeling method for popular data. We formulate the problem of finding the optimal cache data placement, which minimizes the average total delivery delay in the network while satisfying the helpers' cache size constraints. To this end, we derive the average delivery delay per user as well as the average total delivery delay in the network, according to the new generalized Zipf distribution. Finding the optimal solution is proved to be NP-Hard. We leverage on the problem property to propose an efficient approximation method, called greedy algorithm, which performs within a constant factor as good as the optimal solution. Afterwards, we propose an algorithm called speedy-greedy to significantly reduce the computational complexity of the greedy algorithm while achieving the same performance. Simulation results indicate that our proposed framework significantly decreases the average total delivery delay of the system model that can help the network maintain its quality of service in network peak-traffic duration.

Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks

This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand and inter-SBS interference. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By exploiting mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. The simulation results also show that the proposed caching algorithm is robust to imperfect popularity information, while ensuring a low computational complexity.

An efficient cache management algorithm for streaming workloads

In this paper, we present a novel cache management algorithm for real-world streaming workloads. Streaming workloads are believed to exhibit very large and sequential access patterns, which has been the main consideration in designing media caching algorithms. However, legacy caching algorithms do not fully utilise fine-grained access patterns of streaming workloads and also tend to ignore human interactivity. In this paper, we present the least expectation first (LEF) algorithm, which manages a large number of block caches as two-level grouping. Specifically, we select caching and eviction targets based on the expected gain of the cached data blocks, thereby improving the cache hit ratio significantly. Experimental results show that the proposed algorithm performs better than well-known interval caching and LRU algorithms with respect to the hit ratio and the I/O bandwidth.

An Efficient Learning Assistant for a Contextual Road Navigation

Abstract Due to traffic conditions that are very context dependent, the computation of optimized or shortest paths is a very complex problem for both drivers and autonomous vehicles. In this paper, we introduce a learning mechanism that is able to efficiently evaluate path durations based on an abstraction of the available traffic information. We demonstrate that a cache data structure allows a permanent access to the results whereas a lazy politics taking new data into account is used to increase the viability of those results. Our measures highlight the performance of each mechanism, according to different learning strategies.

An efficient cache management algorithm for streaming workloads

In this paper, we present a novel cache management algorithm for real-world streaming workloads. Streaming workloads are believed to exhibit very large and sequential access patterns, which has been the main consideration in designing media caching algorithms. However, legacy caching algorithms do not fully utilise fine-grained access patterns of streaming workloads and also tend to ignore human interactivity. In this paper, we present the least expectation first (LEF) algorithm, which manages a large number of block caches as two-level grouping. Specifically, we select caching and eviction targets based on the expected gain of the cached data blocks, thereby improving the cache hit ratio significantly. Experimental results show that the proposed algorithm performs better than well-known interval caching and LRU algorithms with respect to the hit ratio and the I/O bandwidth.

Does blame shifting work?

Contract systems, especially of the higher-order flavor, go hand in hand with blame. The pragmatic purpose of blame is to narrow down the code that a programmer needs to examine to locate the bug when the contract system discovers a contract violation. Or so the literature on higher-order contracts claims. In reality, however, there is neither empirical nor theoretical evidence that connects blame with the location of bugs. The reputation of blame as a tool for weeding out bugs rests on anecdotes about how programmers use contracts to shift blame and their attention from one part of a program to another until they discover the source of the problem. This paper aims to fill the apparent gap and shed light to the relation between blame and bugs. To that end, we introduce an empirical methodology for investigating whether, for a given contract system, it is possible to translate blame information to the location of bugs in a systematic manner. Our methodology is inspired by how programmers attempt to increase the precision of the contracts of a blamed component in order to shift blame to another component, which becomes the next candidate for containing the bug. In particular, we construct a framework that enables us to ask for a contract system whether (i) the process of blame shifting causes blame to eventually settle to the component that contains the bug; and (ii) every shift moves blame ``closer'' to the faulty component. Our methodology offers a rigorous means for evaluating the pragmatics of contract systems, and we employ it to analyze Racket's contract system. Along the way, we uncover subtle points about the pragmatic meaning of contracts and blame in Racket: (i) the expressiveness of Racket's off-the-shelf contract language is not sufficient to narrow down the blamed portion of the code to the faulty component in all cases; and (ii) contracts that trigger state changes (even unexpectedly, perhaps in the runtime system's data structures or caches) interfere with program evaluation in subtle ways and thus blame shifting can lead programmers on a detour when searching for a bug. These points highlight how evaluations such as ours suggest fixes to language design.

SDN/NFV-Empowered Future IoV With Enhanced Communication, Computing, and Caching

Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

Intelligent data cache based on content popularity and user location for Content Centric Networks

Content cache as well as data cache is vital to Content Centric Network (CCN). A sophisticated cache scheme is necessary but unsatisfied currently. Existing content cache scheme wastes router’s cache capacity due to redundant replica data in CCN routers. The paper presents an intelligent data cache scheme, viz content popularity and user location (CPUL) scheme. It tackles the cache problem of CCN routers for pursuing better hit rate and storage utilization. The proposed CPUL scheme not only considers the location where user sends request but also classifies data into popular and normal content with correspond to different cache policies. Simulation results showed that the CPUL scheme yields the highest cache hit rate and the lowest total size of cache data with compared to the original cache scheme in CCN and the Most Popular Content (MPC) scheme. The CPUL scheme is superior to both compared schemes in terms of around 8% to 13% higher hit rate and around 4% to 16% lower cache size. In addition, the CPUL scheme achieves more than 20% and 10% higher cache utilization when the released cache size increases and the categories of requested data increases, respectively.

Cyber-Physical Security Design in Multimedia Data Cache Resource Allocation for Industrial Networks

For cyber-physical industrial networks, more and more multimedia data is faced in high-speed information transmission. The explosive data brings more challenges to the architecture of modern industrial networks. In this way, cache resource allocation technology is necessary for practical applications. In order to design reasonable caching framework, how to predict the multimedia data request is an important issue. In order to keep efficient and reliable data transmission in wireless industrial networks, security design is also critical for existing cache resource allocation. Based on previous works, some promising technologies have been applied, such as heterogeneous ultradense networks, wireless edge caching, and web content popularity prediction. In this paper, we summarize these promising technologies and provide a useful guidance for security design in cyber-physical cache resource allocation system. Specially, we can divide the proposed method into three main steps. First of all, a spatio-temporal multimedia content prediction based on long short-term memory is proposed for accurate prediction on multimedia data request. After that, we make use of Zipf fitting for caching model. At last, the caching optimization considering security is put forward in this paper. Experimental results show the satisfied performance of our proposed algorithm and it has obvious potential application value in cyber-physical industrial networks with cache resource allocation technology.

Optimizing Finite Volume Method Solvers on Nvidia GPUs

As scientific applications are increasingly ported to GPUs to benefit from both the powerful computing capacity and high throughput, accelerating explicit solvers for GPU-based finite volume methods is gaining more and more attention. In this paper, based on the detailed analysis of the FVM algorithm, we present a set of novel optimization methods, including the explicit data cache mechanism, optimal global memory loading strategy, as well as the inner-thread rescheduling method, which derives a suitable mapping from the solver algorithm to the underlying GPU hardware architecture, so as to remarkably improve the solving performance of structured mesh based FVM. We demonstrate the impact of our tuning techniques on two widely-used atmospheric dynamic kernels (3-D Euler and 2-D SWE) on five kinds of mainstream GPU platforms, and make a detailed analysis of the different tuning methodologies so as to demonstrate how to select the proper tuning strategy to different applications on various GPU platforms. Specifically, 93.9x speedup is achieved for the 3D Euler solver on Nvidia V100 over one 12-core Intel E5-2697 (v2) CPU, which is a 77 percent improvement compared with the original speedup without adopting the tuning techniques presented in this work.

A Multi-Level Cache Management Approach of Scientific and Technical Information Data for Electric Power

Scientific and technical information database for electric power becomes indispensable with the development of big data and think tank in electric industry. Querying information database is time-consuming and cache management is an efficient solution. In the paper we focus on a multi-level cache management in order to improve search speed of sci-tech information. The proposed computation method of cached data value considers visiting volume, data size and user experience, which is accurate for choosing key words and caching device. Our caching management framework consists of four modules, and the used machine learning method could effectively predict the number of visits next period for key words. The experiments demonstrate that our proposed MLCM approach performs better in prediction accuracy rate, query time and user experience than traditional statistic, LRU, LFU algorithms.

Aging-mitigation of cache memories by Intra-Word Bit Swapping

Aging is one of the most crucial reliability issues in recent chip fabrication technologies. Among different causes for aging, ``Bias Temperature Instability'' (BTI) imposes the highest semiconductor degradation rate to processors. Studies show that the stored values in the processors’ cache memories follow specific bit-patterns. The pattern in the data cache is due to the stored narrow-width values, however, in the instruction cache, it goes after the predefined instructions’ opcodes patterns. Hence, data and instruction cache memory cells experience different aging rates due to different stress conditions, which can lead to a decrease in ``Mean Time To Failure'' (MTTF) of the cache structures. This work proposes a new scheme that balances the stress condition over all cache memory cells by swapping bit values within each word of the cache memory. By utilizing this scheme, the aging rate of data and instruction cache memories is mitigated by up to 69% and 83%, respectively, with negligible area and performance overheads.