nils-herrmann/clean_scirepeval huggingface dataset:
Optimal Data Allocation for Scratch-Pad Memory on Embedded Multi-core Systems

Multi-core systems have been a popular design for high-performance embedded systems. Scratch Pad Memory (SPM), a software-controlled on-chip memory, has been widely adopted in many embedded systems due to its small area and low energy consumption. Existing data allocation algorithms either cannot achieve optimal results or take exponential time to complete. In this paper, we propose one polynomial-time algorithms to solve the data allocation problem on multi-core system with exclusive data copy. According to the experimental results, the proposed optimal data allocation method alone reduces time cost of memory accesses by 16.45\% on average compared with greedy algorithm. The proposed data allocation algorithm also can reduce the energy cost significantly.
On services pushing and caching in high-speed train by using converged broadcasting and cellular networks

This paper proposes a services pushing and caching algorithm in high-speed train (HST) by using converged wireless broadcasting and cellular networks (CWBCN). Services pushing and caching on the HST is an efficient way to improve the capacity of the network; and it can also lead to better user experience. In the proposed services pushing and caching model, the most popular services are delivered and cached on the vehicle relay station (VRS) of the train ahead the departure time. Then, the most popular services are broadcasted and cached on the User Equipments (UEs) after all the passengers are on the train; the less popular services are transmitted to the user by p2p mode by the relayed cellular network on the train. In order to maximize the network capacity in limited time slots, we transform the issue into the 0–1 Knapsack problem. Dynamic programming algorithm is adopted to solve it in polynomial time. As the passengers may get on or get off the train when pushing the most popular services, an information retransfer algorithm is also proposed when more intermediate stations are considered. Simulations show that the proposed algorithms can efficiently improve the capacity of the converged network.
Real-Time Constrained Task Scheduling in 3D Chip Multiprocessor to Reduce Peak Temperature

Chip multiprocessor technique has been implemented in embedded systems due to the tremendous computation requirements. Three dimension chip multiprocessor architecture has been studied recently for integrating more functionalities and providing higher performance. The high temperature on chip is a critical issue for the 3D architecture. In this paper, we propose an online thermal prediction model for 3D chip. Using this model, we present a task scheduling algorithm based on rotation scheduling to reduce the peak temperature on chip. We consider the data dependencies, especially the inter-iteration dependencies which are not well considered in most of the current thermal-aware task scheduling algorithms. Our simulation result shows that our algorithm can efficiently reduce the peak temperature up to 10C.
Topology Virtualization for Throughput Maximization on Many-Core Platforms

As transistor's feature size continues to scale down into the deep sub-micron domain, IC chip performance variation caused by manufacturing process becomes un-negligible and can cause significant discrepancies between an application's nominal design and its actual realization on individual manycore platforms. In this paper, we study the problem on how to reduce the total schedule length of a task graph when realizing its nominal design on individual Network-on-Chip(NoC) based many-core platform with faulty cores. Different from traditional approaches to re-define the mapping/scheduling decisions in the nominal design, our methods judiciously mirror the physical architecture of each individual platform to the logical platform, based on which the nominal design is conducted. To facilitate the phyical/logic architecture virtualization, we develop a performance metric based on the opportunity cost, a concept borrowed from the economics field. Three virtualization heuristics are presented in this paper. Our experimental results show that the proposed approach can achieve up to 30% with an average 15% performance improvement by taking advantage of the heterogeneity of each individual platform.
Multicore-Enabled Smart Storage for Clusters

We present a multicore-enabled smart storage for clusters in general and MapReduce clusters in particular. The goal of this research is to improve performance of data-intensive parallel applications on clusters by offloading data processing to multicore processors in storage nodes. Compared with traditional storage devices, next-generation disks will have computing capability to reduce computational load of host processors or CPUs. With the advance of processor and memory technologies, smart storage systems are promising devices to perform complex on-disk operations. The proposed smart storage system can avoid moving a huge amount of data back and forth between storage nodes and computing nodes in a cluster. To enhance the performance of data-intensive applications, we have designed a smart storage system called Multicore-enabled Smart Storage (McSD), in which a multicore processor is integrated in storage nodes. We have implemented a programming framework for data-intensive applications running on a computing system coupled with McSD. The programming framework aims at balancing load between computing nodes and multicore-enabled smart storage nodes. To fully utilize multicore processors in smart storage nodes, we have implemented the MapReduce model for McSDs to handle parallel computing on a cluster. A prototype of McSD has been implemented in a cluster connected by Gigabit Ethernet. Experimental results show that McSD can significantly reduce the execution times of three real-world applications - word count, string matching, and matrix multiplication. We demonstrate that the integration of multicore-enabled smart storage with MapReduce clusters is a promising approach to improving overall performance of data-intensive applications on clusters.
Authoring multimedia documents through grammatical specifications

The diversity of multimedia devices is an important feature on the Internet, which demands an executable mechanism to intelligently adjust the appearance of a document according to different viewing contexts. In order to satisfy this requirement, a graph-grammar-based approach for multimedia authoring is presented by extending context-sensitive graph grammar formalism, reserved graph grammar (RGG), with the capability of spatial specifications. Based on a parser for the RGG, a graph grammar functions as a mapping from the structure of a document to a desirable layout, and graph transformations arc automatically performed when contexts arc changed.
Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
Smart Energy-Aware Data Allocation for Heterogeneous Memory

Heterogeneous memory is an emerging concept that uses multiple kinds of memories to balance the contradictions between the performance and cost. Many prior researches have addressed the heterogeneous memory designs, which attracted both researchers and practitioners. However, the main challenge in memory design is that the moving costs between memories cannot be ignored, such that forming an effective data allocation plan is a critical part for reducing total costs. This paper focuses on this issue and proposes an adaptive energy-aware approach that is designed for efficiently generating adaptive data allocation plans in heterogeneous memories. Two crucial variables are considered the condition constraints, including time and energy consumptions. The proposed method is called Smart Data Allocation Model (SDAM), which is supported by our algorithm, Smart Switch Allocation Algorithm (SA2). The experimental results have depicted that our approach is superior to prior developed methods. The likelihood of generating optimal solutions is 80.4% - 90.1%.
An Optimal Fully Homomorphic Encryption Scheme

The expeditious expansion of the networking technologies have remarkably driven the usage of the distributedcomputing as well as services, such as task offloading to the cloud. However, security and privacy concerns are restricting the implementations of cloud computing because of the threats from both outsiders and insiders. The primary alternative of protecting users' data is developing a Fully Homomorphic Encryption (FHE) scheme, which can cover both data protections and data processing in the cloud. Despite many previous attempts addressing this approach, none of the proposed work can simultaneously satisfy two requirements that include the non-noise accuracy and an efficiency execution. This paper focuses on the issue of FHE design and proposes a novel FHE scheme, which is called Optimal Fully Homomorphic Encryption (O-FHE). Our approach utilizes the properties of the Kronecker Product (KP) and designs a mechanism of achieving FHE, which consider both accuracy and efficiency. We have assessed our scheme in both theoretical proofing and experimental evaluations with the confirmed and exceptional results.
SPHA: Smart Personal Health Advisor Based on Deep Analytics

According to a report by the World Health Organization, diseases caused by an unhealthy lifestyle represent the leading cause of death all over the world. Therefore, it is crucial to monitor and avoid users' unhealthy behaviors. Existing health monitoring approaches still face many challenges of limited intelligence due to insufficient healthcare data. Therefore, this article proposes a smart personal health advisor (SPHA) for comprehensive and intelligent health monitoring and guidance. The SPHA monitors both physiological and psychological states of the user. The SPHAScore model is proposed to evaluate the overall health status of the user. Finally, a testbed for verification of feasibility and applicability of the proposed system was developed. The experimental and simulation results have shown that the proposed approach is efficient for proper user state monitoring.
Design and Architecture of Dell Acceleration Appliances for Database (DAAD): A Practical Approach with High Availability Guaranteed

As IT organizations are pursuing database High Availability (HA) solutions to ensure and protest critical commercial data, the challenge is to leverage the three-fold key dimensions: cost, performance and availability. A successful solution needs to integrate Database Management System (DBMS) seamlessly with back-end storage and offer good performance and customer data protection. Through extensively investigating plenty of productions in the market, Oracle database is a leading solution for business critical application. As a cost effective solution, it could reduce business risk and achieve data availability, performance, and Return on Investment (ROI). On the other aspect, high-end storage solution, Storage Area Network (SAN) based on Fibre Channel protocol can be easy to deploy with Oracle database and match end-user's requirements. In this paper, we will present a design of high available database on a new reference architecture. The implementation of design is based on Oracle database with Dell Acceleration Appliances for Databases (DAAD), which introduces flash technology by Fusion-IO. To demonstrate practical performances, we built a proof-ofconcept platform and compared the platform with a traditional 96-SAS-drive platform. The results show that our approach can deliver more than 1-million random Input/output Operations per Second (IOPS) that is 27 times faster than the traditional platform and it can also achieve a 96% reduction in the latencies, thus showing the scalability of the approach with massive database nodes. Therefore, our approach provides not only an ultra-fast storage solution to boost database performances but it also offers a flexible and high available design to achieve zero-downtime database.
SwapBench: The Easy Way to Demystify Swapping in Mobile Systems

Mobile systems such as smartphones and tablets are re-adopting swapping -- a mature but by rarely used OS feature -- to extend memory capacity without adding more DRAM, especially low-end devices. This resurgence of swapping in mobile systems has inspired both traditional "off-the-rack" schemes and new approaches based on compression and new hardware. Their vastly different designs, however, make them difficult for system designers to measure, compare and revise. In this paper, we propose an evaluation framework, SwapBench, to appraise swap schemes and focus on two important but overlooked metrics: application launch and switch. Cross-validation with microbenchmarks shows that SwapBench is accurate. Based on the findings from SwapBench, we further discuss the impacts of different approaches to swapping in mobile systems.
H∞ State Estimation for Discrete-Time Chaotic Systems Based on a Unified Model.

This paper is concerned with the problem of state estimation for a class of discrete-time chaotic systems with or without time delays. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these systems, such as chaotic neural networks, Chua's circuits, Hénon map, etc. Based on the H∞ performance analysis of this unified model using the linear matrix inequality approach, H∞ state estimator are designed for this model with sensors to guarantee the asymptotic stability of the estimation error dynamic systems and to reduce the influence of noise on the estimation error. The parameters of these filters are obtained by solving the eigenvalue problem. As most discrete-time chaotic systems with or without time delays can be described with this unified model, H∞ state estimator design for these systems can be done in a unified way. Three numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes.
Dynamic and Leakage Power Minimization with Loop Voltage Scheduling and Assignment

This paper studies the scheduling and assignment problem that minimizes the total energy including both dynamic and leakage energy for applications with loops on multi-voltage, multi-processor DSP. An algorithm, LSAMP (Loop Scheduling and Assignment to Minimize Power), is proposed. The algorithm attempts to minimize the total energy while satisfying timing constraint with guaranteed probability. We will perform scheduling and assignment simultaneously. Our approach shows better performance than the approach that considers scheduling and assignment at separate phases. Compared with previous work, our algorithm shows a significant improvement in total energy reduction.
An SDN-Based Fabric for Flexible Data-Center Networks

Data-centers play a fundamental role in cloud computing. As the market of cloud computing continues to scale, it becomes increasingly difficult to utilize resources in large data-center networks. There is a growing interest to seek for efficient data-center architectures among academia and industry. Software-defined Networking (SDN) is a promising technology to develop scalable network architectures for massive data processing. SDN decouples control planes from physical switches and centralizes network management. However, most of the previous works merging SDN with data-center networks use tree or fat-tree topologies. These topologies have fixed architectures and are difficult to make full use of the features supported by SDN. In this paper, we proposed a fabric topology to provide a flexible data-center network. With SDN controllers, our work has the potential to address critical issues such as bandwidth utilization and network capacity in datacenter networks.
Thermal-aware rotation scheduling for 3D multi-core with timing constraint

Multi-core technique has been implemented in DSP systems due to the tremendous computation requirements. In order to integrate more functionalities and provide higher performance, 3D multi-core architecture has been studied recently. The thermal issue is a critical problem for 3D architecture. In this paper, we propose an online thermal prediction model for 3D chip. Using this model, we present a task scheduling algorithm based on rotation scheduling to reduce the peak temperature on chip. While existing works do not consider the inter-iteration data dependencies in the application, we consider these dependencies in our proposed algorithm. Our simulation result shows that our algorithm can efficiently reduce the peak temperature up to 8 °C.
TOPS: Two-phase scheduling for distributed real-time systems

In this work we propose a two-phase scheduling technique (TOPS) for distributed real-time systems. The first phase of TOPS is in charge of producing a scheduling sequence, whereas the second phase aims to dispatch tasks to computing nodes of a distributed system. The two phases are independent of one another and; therefore, one can change a policy in one phase without configuring another phase. TOPS makes it possible to observe the impacts of sorting policies on the performance of scheduling policies. We implement a TOPS prototype, in which the first phase is comprised of three sorting policies and the second phase consists of two scheduling policies in the second phase. TOPS enables us to discover that combining the EDF (Earliest-Deadline-First) and AEAP (As-Early-As-Possible) policies leads to an optimized performance among all the six candidate algorithms.
Who Moved My Data? Privacy Protection in Smartphones

While enjoying the convenience brought by various kinds of IoT devices, our private data are leaked. Smartphones, as the typical pivot of IoT devices, use various kinds of apps, which collect our private data. In fact, private data leakage, as a potential hazard, is caused by the current design trend in industry, which is bigger and bigger. However, few people notice the side-effect, while we seem never bored by pursuing “smart” devices. In this article, we introduce the behaviors of data collection, and illustrate the motivations and reasons behind them. Thankfully, cloud computing with sufficient resources and exquisite services is a perfect way out. We introduce a mobile–cloud framework to provide fine-grained permission authorization service for IoT devices, and show its performance by experimental results. Nevertheless, there are still lots of issues to be solved for the perfect IoT-cloud architecture. We list and analyze some obstacles and trends in the field of IoT–cloud.
Maintainable Mobile Model Using Pre-Cache Technology for High Performance Android System

As a mobile operating system framework, Android plays a significant role in supporting mobile apps. Using cloud-based approaches have further enabled the implementations of mobile apps. However, current Android application model is not efficient by using current common approaches. In this paper, we proposed a new mobile app model using precache technology to overcome the current existing obstacles. The results of experiments show that our model can reduce network traffic of Android apps efficiently. At the same time, using our proposed model can reduce the data redundancy and improve maintainability of Android apps.
Loop Parallelism Maximization for Multimedia Data Processing in Mobile Vehicular Clouds

Mobile vehicular cloud has become popular with the rapid development of cloud computing and mobile computing. Nested loops are usually the most critical part in multimedia and high performance Digital Signal Processing (DSP) systems which are widely used in vehicular applications and systems. In order to further explore the parallelism in nested loops, we study how to maximize the system performance with considering the energy reduction for applications on Chip Multiprocessor (CMP) architectures. We propose an algorithm Energy-Aware Loop Parallelism Maximization (EALPM) to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops. Our experiment shows that using the EALPM algorithm significantly improves both performance and energy consumption on average in comparision to other algorithms.
Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing

Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.