Data Cache Research Articles

Online data caching in edge computing

SummaryData caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

DCSS Protocol for Data Caching and Sharing Security in a 5G Network

Fifth Generation mobile networks (5G) promise to make network services provided by various Service Providers (SP) such as Mobile Network Operators (MNOs) and third-party SPs accessible from anywhere by the end-users through their User Equipment (UE). These services will be pushed closer to the edge for quick, seamless, and secure access. After being granted access to a service, the end-user will be able to cache and share data with other users. However, security measures should be in place for SP not only to secure the provisioning and access of those services but also, should be able to restrict what the end-users can do with the accessed data in or out of coverage. This can be facilitated by federated service authorization and access control mechanisms that restrict the caching and sharing of data accessed by the UE in different security domains. In this paper, we propose a Data Caching and Sharing Security (DCSS) protocol that leverages federated authorization to provide secure caching and sharing of data from multiple SPs in multiple security domains. We formally verify the proposed DCSS protocol using ProVerif and applied pi-calculus. Furthermore, a comprehensive security analysis of the security properties of the proposed DCSS protocol is conducted.

Crystal

Cloud analytical databases employ a disaggregated storage model, where the elastic compute layer accesses data persisted on remote cloud storage in block-oriented columnar formats. Given the high latency and low bandwidth to remote storage and the limited size of fast local storage, caching data at the compute node is important and has resulted in a renewed interest in caching for analytics. Today, each DBMS builds its own caching solution, usually based on file-or block-level LRU. In this paper, we advocate a new architecture of a smart cache storage system called Crystal , that is co-located with compute. Crystal's clients are DBMS-specific "data sources" with push-down predicates. Similar in spirit to a DBMS, Crystal incorporates query processing and optimization components focusing on efficient caching and serving of single-table hyper-rectangles called regions. Results show that Crystal, with a small DBMS-specific data source connector, can significantly improve query latencies on unmodified Spark and Greenplum while also saving on bandwidth from remote storage.

A mobile sink–integrated framework for the collection of farmland wireless sensor network information based on a virtual potential field

To overcome the limitations of traditional data collection methods in large-scale farmland wireless sensor network, in this study, we introduce a mobile sink and propose a virtual potential field-based strategy for mobile sink path planning. Virtual potential field-based strategy constructs a virtual field based on the residual energy, data generation rate, location information and cache urgency of nodes in the monitoring area. The stronger the virtual field, the more attractive it will be to mobile sink, which consequently affects the mobile path of sink node. Rendezvous points are selected in accordance with the maximum-farthest criterion, and the shortest path connecting all rendezvous points is taken as the mobile path of sink. Furthermore, the monitoring nodes employ the distance probability transmission strategy to have the transmission moment selected and the energy consumption optimized with reference to the path control information sent by the sink. The virtual potential fields and the rendezvous points are recalculated periodically according to the dynamic changes of both the node residual energy and the real-time cache. The simulation results showed that excellent transmission efficiency and network lifetime, and the combination of virtual potential field-based strategy and distance probability transmission strategy can have the fairness and real time of nodes guaranteed, thus it may meet the needs of large-scale farmland data collection.

Joint Computation Offloading and Data Caching Based on Cooperation of Mobile-Edge-Computing-Enabled Base Stations

Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks’ data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching.

Availability and Promotion of Cannabidiol (CBD) Products in Online Vape Shops.

Vaping products containing cannabidiol (CBD), a cannabis-derived compound used in wellness products and available in all 50 US states, were recently implicated in outbreaks of poisonings. Little is known about the commercial availability of CBD products in vape shops (i.e., stores that sell e-cigarettes). To document the availability and marketing of CBD products in online vape shops, in June 2020, we used the Google Chrome browser without cached data to collect the first two pages of search results generated by five Google queries (n = 100 search results) indicative of shopping for vaping products (e.g., “order vapes”). We then determined whether and what type of CBD products could be mail-ordered from the returned websites, and whether any explicit health claims were made about CBD. Over a third of the search results (n = 37; 37.0%) directed to vape shops that allowed visitors to also mail-order CBD. These shops sold 12 distinct categories of CBD products–some with direct analogs of tobacco or cannabis products including CBD cigarettes, edibles, flowers, pre-rolled joints, and vapes. Two vape shops made explicit health claims of the therapeutic benefits of CBD use, including in the treatment of anxiety, inflammation, pain, and stress. The abundance and placement of CBD in online vape shops suggests a growing demand and appeal for CBD products among e-cigarette users. Additional surveillance on the epidemiology of CBD use and its co-use with tobacco is warranted.

ACORE: A Query Optimization Approach for Spark SQL Based on Cost Model and Markov Prediction Model

Spark SQL is Spark’s module for working with structured data. However, there exist two problems that may impact its query execution efficiency: 1) Users may attempt to cache datasets used multiple times in a query to speed up the execution, but due to the predicate pushdown optimization and the overhead of writing cached data, the cached plan may turn out to have worse performance. 2) When using Spark dynamic resource allocation strategy, the release of idle executors would lead to the loss of cached data, introducing recalculation cost. To address these, we present an approach, named ACORE, which adaptively cache datasets and optionally release executors to optimize query execution of Spark SQL. The approach includes two models: a cost model and a Markov chain prediction model. The cost model is applied to estimate the execution cost for cached and un-cached plans, and decide which one to be the actual execution plan. The Markov prediction model is used to predict the changing trend of intervals between queries, guiding to optionally release executors to save cached data. We test the efficiency of our approach with data generated by TPC-H tool and the experimental results show that the execution performance can be improved by up to 51%.

Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing

The satellite-borne SAR image intelligent processing system needs to process on-orbit real-time imaging and various tasks of applications, for which reason designing a dedicated high-efficient single-chip multi-processor is of prioritized necessity that can simultaneously satisfy requirements of real-time and low power consumption. Aiming at on-chip data organization and memory access structure, two typical models of SAR(synthetic aperture radar) imaging CSA (chirp scaling) and neural network VGG-11 are analyzed, and then a collaborative computing model for the intelligent processing on remote sensing is extracted. A strip Tile data processing scheme and a dedicated multi-processing architecture is not only proposed, but a data organization and a caching strategy of Tile space synchronization splicing is also presented. In addition, the designed data caching structure among the processing units greatly reduces off-chip access memory bandwidth while supporting parallel pipeline execution of multi-task model. The chip adopts 28 nm CMOS technology featuring with merely 1.83 W of the overall power consumption, whose throughput and energy efficiency reaches 9.89TOPS and 5.4 TOPS/W, respectively. The present architecture can improve real-time performance of the on-orbit remote sensing intelligent processing platform while reducing the complexity of system designing, which is highly adaptive to differentiated expansions according to different models of algorithm.

Mars On-Site Shared Analytics Information and Computing

We study the use of distributed computation in a representative multi-robot planetary exploration mission. We model a network of small rovers with access to computing resources from a static base station based on current design efforts and extrapolation from the Mars 2020 rover autonomy. The key algorithmic problem is simultaneous scheduling of computation, communication, and caching of data, as informed by an autonomous mission planner. We consider scheduling of a dependency chain of required and optional (but rewarding) tasks and present a consensus-backed scheduler for sharedworld, distributed scheduling based on an Integer Linear Program. We validate the pipeline with simulation and field results. Our results are intended to provide a baseline comparison and motivating application domain for future research into network-aware decentralized scheduling and resource allocation.

Decreasing the Miss Rate and Eliminating the Performance Penalty of a Data Filter Cache

While data filter caches (DFCs) have been shown to be effective at reducing data access energy, they have not been adopted in processors due to the associated performance penalty caused by high DFC miss rates. In this article, we present a design that both decreases the DFC miss rate and completely eliminates the DFC performance penalty even for a level-one data cache (L1 DC) with a single cycle access time. First, we show that a DFC that lazily fills each word in a DFC line from an L1 DC only when the word is referenced is more energy-efficient than eagerly filling the entire DFC line. For a 512B DFC, we are able to eliminate loads of words into the DFC that are never referenced before being evicted, which occurred for about 75% of the words in 32B lines. Second, we demonstrate that a lazily word filled DFC line can effectively share and pack data words from multiple L1 DC lines to lower the DFC miss rate. For a 512B DFC, we completely avoid accessing the L1 DC for loads about 23% of the time and avoid a fully associative L1 DC access for loads 50% of the time, where the DFC only requires about 2.5% of the size of the L1 DC. Finally, we present a method that completely eliminates the DFC performance penalty by speculatively performing DFC tag checks early and only accessing DFC data when a hit is guaranteed. For a 512B DFC, we improve data access energy usage for the DTLB and L1 DC by 33% with no performance degradation.

Adaptive tiling for parallel N-body simulations on many core

The N-body simulations consist of computing mutual gravitational forces exerted on each body in O ( N ) . The Barnes–Hut approximation allows processing a group of bodies in O ( 1 ) if they are far enough from a given body, which drops the complexity of the whole simulation to O ( N L o g N ) . The octree is used to ease the pruning process but at the cost of some irregularity in the access pattern. In a parallel N-body implementation the bodies are partitioned among threads that are executed on multiple cores. The depth-first traversal of the octree is used for processing each body, which causes repeated cache misses during traversal. This paper proposes different types of tiling methods to improve the performance of N-body simulations. It presents an experimental analysis of octree traversal by using these tiling methods to identify the potential of cache data reuse. It then evaluates these tiling methods for varying tile sizes with different galaxy sizes and a varying number of threads on several machine architectures. The efficiency of tiling approaches depends on the chosen tile size. It is shown that a speedup of 8 times can be achieved by choosing the appropriate tile size on a 60-core Intel accelerator. In order to determine appropriate tile size, the paper proposes an adaptive tiling approach to implicitly adapt the tile size to the distribution of threads, the cache capacity, cache latency, problem size and dynamic changes in the access pattern over the iterations. The proposed adaptive tiling approach can be used as an optimization option in parallel compilers. • Proposes tiling methods to improve cache data reuse for Barnes-Hut simulation. • Provides experimental analysis of octree traversal with proposed tiling approaches. • Evaluates different galaxy sizes and number of threads on several architectures. • A hybrid tiling approach with a suitable tile size provides better performance. • Proposes an adaptive tiling approach to identify an optimal tile size.

Sound Source Acquisition and Location System Based on DSP and FPGA

According to the requirements of audio signal acquisition and conversion, sound source location in complex environment, a sound source collention and positioning system which based on DSP and FPGA is designed. The system adopts low noise MEMS microphone sensor to form microphone array, DSP chip as the core controller, beam forming algorithm as the core algorithm of the system. Through the application of high precision ADC chip to carry out synchronous sampling and conversion of 64 channels of audio signals, simultaneously, the use of low-cost, high-speed FPGA chip to achieve batch data acquisition and caching. The real-time data upload is realized by fast Ethernet controller to ensure the non-blocking transmission of large traffic data. Through the laboratory test, the analysis and verification of the experimental data, the system works stably and meets the design requirements.

Cache-aware scheduling of scientific workflows in a multisite cloud

Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution.

Task Priority-Based Cached-Data Prefetching and Eviction Mechanisms for Performance Optimization of Edge Computing Clusters

The rapid evolution of the Internet of Things (IoT) and the development of cloud computing have endorsed a new computing paradigm called edge computing, which brings the computing resources to the edge of the network. Due to low computing power and small data storage at the edge nodes, the task must be assigned to the computing nodes, where their associated data is available, to reduce overheads caused by data transmissions in the network. The proposed scheme named task priority-based data-prefetching scheduler (TPDS) tries to improve the data locality through available cached and prefetching data for offloading tasks to the edge computing nodes. The proposed TPDS prioritizes the tasks in the queue based on the available cached data in the edge computing nodes. Consequently, it increases the utilization of cached data and reduces the overhead caused by data eviction. The simulation results show that the proposed TPDS can be effective in terms of task scheduling and data locality.

Electromagnetic fault injection against a complex CPU, toward new micro-architectural fault models

The last years have seen the emergence of fault attacks targeting modern central processing units (CPUs). These attacks are analyzed at a very high abstraction level and, due to the modern CPUs complexity, the underlying fault effect is usually unknown. Recently, a few articles have focused on characterizing faults on modern CPUs. In this article, we focus on the electromagnetic fault injection (EMFI) characterization on a bare-metal implementation. With this approach, we discover and understand new effects on micro-architectural subsystems. We target the BCM2837 where we successfully demonstrate persistent faults on L1 instruction cache, L1 data cache and L2 cache. We also show that faults can corrupt the memory management unit (MMU). To validate our fault model, we realize a persistent fault analysis to retrieve an AES key.

Pro NDN : MCDM-Based Interest Forwarding and Cooperative Data Caching for Named Data Networking

Named data networking (NDN), as a specific architecture design of information-centric networking (ICN), has quickly became a promising candidate for future Internet architecture, where communications are driven by data names instead of IP addresses. To realize the NDN communication paradigm in the future Internet, two important features, stateful forwarding and in-network caching, have been proposed to cope with drawbacks of host-based communication protocols. The stateful forwarding is designed to maintain the state of pending Interest packets to guide Data packets back to requesting consumers, while the in-network caching is used to reduce both network traffic and data access delay to improve the overall performance of data access. However, the conventional stateful forwarding approach is not adaptive and responsive to diverse network conditions because it fails to consider multiple network metrics to make Interest forwarding decision. In addition, the default in-network caching strategy relies on storing each received Data packet regardless of various caching constraints and criteria, which causes the routers in the vicinity of data producers to suffer from excessive caching overhead. In this paper, we propose the Pro NDN , a novel stateful forwarding and in-network caching strategy for NDN networks. The Pro NDN consists of multicriteria decision-making (MCDM) based interest forwarding and cooperative data caching. The basic idea of the MCDM-based interest forwarding is to employ Technique for Order Performance by Similarity to Idea Solution (TOPSIS) to dynamically evaluate outgoing interface alternatives based on multiple network metrics and objectively select an optimal outgoing interface to forward the Interest packet. In addition, the cooperative data caching consists of two schemes: CacheData, which caches the data, and CacheFace, which caches the outgoing interface. We conduct extensive simulation experiments for performance evaluation and comparison with prior schemes. The simulation results show that the Pro NDN can improve Interest satisfaction ratio and Interest satisfaction latency as well as reduce hop count and Content Store utilization ratio.

Inspecting Edge Data Integrity With Aggregate Signature in Distributed Edge Computing Environment

In the edge computing environment, app vendors can cache their data on a large number of geographically distributed edge servers to serve their users. However, those cached data are particularly vulnerable to both intentional and accidental corruption, which makes data security a major concern in the EC environment. Given limited computing resources of edge servers, how to effectively and efficiently inspect those data over tremendous edge servers is a critical and open problem. To tackle this edge data integrity (EDI) problem, we first study the entities, threats, system objectives, and the inspection mechanism, then propose a novel approach named EDI-S for inspecting the integrity of edge data and localizing the corrupted ones. Based on the elliptic curve cryptography, EDI-S generates one digital signature as the integrity proof for each replica. Then, multiple integrity proofs can be inspected altogether via an aggregate verification. This allows the integrity of tremendous cache data on multiple edge servers can be inspected more efficiently. EDI-S also provides two methods for localizing the corrupted data on edge servers, one for small-scale scenarios and the other for large-scale scenarios. Both theoretical analysis and experimentally evaluation demonstrate that EDI-S can solve the EDI problem effectively and efficiently.

New Two-Level L1 Data Cache Bypassing Technique for High Performance GPUs

On-chip caches of graphics processing units (GPUs) have contributed to improved GPU performance by reducing long memory access latency. However, cache efficiency remains low despite the facts that recent GPUs have considerably mitigated the bottleneck problem of L1 data cache. Although the cache miss rate is a reasonable metric for cache efficiency, it is not necessarily proportional to GPU performance. In this study, we introduce a second key determinant to overcome the problem of predicting the performance gains from L1 data cache based on the assumption that miss rate only is not accurate. The proposed technique estimates the benefits of the cache by measuring the balance between cache efficiency and throughput. The throughput of the cache is predicted based on the warp occupancy information in the warp pool. Then, the warp occupancy is used for a second bypass phase when workloads show an ambiguous miss rate. In our proposed architecture, the L1 data cache is turned off for a long period when the warp occupancy is not high. Our two-level bypassing technique can be applied to recent GPU models and improves the performance by 6% on average compared to the architecture without bypassing. Moreover, it outperforms the conventional bottleneck-based bypassing techniques.

A Heterogeneous Distributed Computing Model Based on Web Service

Based on the in-depth study of the existing database synchronization model, in order to improve the cross platform ability of the system and facilitate the construction of small and medium-sized enterprise information platform, this paper proposes a heterogeneous distributed computing scheme based on Web service. The scheme uses JMS to realize the message transmission between systems, and uses web service technology to realize cross platform data reading and writing. In the aspect of distributed transaction processing, the two-phase commit protocol is improved to reduce the probability of system deadlock and effectively ensure the consistency of distributed database data. In order to improve the performance of distributed database system, cache technology is introduced, and the way of integrating cache and database transaction processing is proposed, which effectively ensures the validity of cache data. The architecture is oriented to program developers, who can develop efficient and convenient distributed database system on the basis of this architecture. Finally, this architecture is applied to the background management system of mobile express service. The running results show that the architecture can well meet the business requirements of distributed heterogeneous database system synchronization.

Constrained App Data Caching Over Edge Server Graphs in Edge Computing Environment

In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider’s perspective, caching app data on edge servers can ensure low latency in its users’ data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this article, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider’s perspective and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set. The results demonstrate that they significantly outperform four representative approaches.