nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Resource Allocation Optimization in Heterogeneous Mobile Cloud Systems

Recent growth of cloud computing has driven the development of Heterogeneous Cloud Federation (HCF) that has been considered an approach of improving the diversity of cloud systems by enable cloud users to select resources provided by different service providers. Using this mechanism can divide a complicate task into a group of sub-tasks in order to increase the efficiency of application executions, which can support simultaneously executing different cloud systems. One of the resource allocation mechanisms is to schedule tasks among cloud providers. However, current challenge of implementing this mechanism is to generate an efficient task scheduling. We consider mobile cloud computing an adaptive approach for increasing the utilizations of mobile devices. Addressing this dimension, we propose our solution named Mobile Cloud-based Heterogeneous Resource Allocation Model (MC-HRAM), which is designed to minimize the execution time. The main algorithm used in the proposed model is Dynamic Heterogeneous Task Assignments Algorithm (DHTA). Our experimental evaluation has proved that our proposed schema has an advantage of saving execution time.
Balance of security strength and energy for a PMU monitoring system in smart grid

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.
A Novel Energy-Aware Fault Tolerance Mechanism for Wireless Sensor Networks

Sensors in a Wireless Sensor Network (WSN) are prone to failure, due to energy depletion, hardware failures, etc. Fault tolerance is one of the critical issues in WSNs. The existing fault tolerant mechanisms either consume significant extra energy to detect and recover from the failures or need to use additional hardware and software resource. In this paper, we propose a novel energy-aware fault tolerance mechanism for WSN, called Informer Homed Routing (IHR). In our IHR, the non cluster head nodes limit and select the target of their data transmission. Therefore, it consumes less energy. Our experiments show that our proposed protocol can dramatically reduce energy consumption, compared to two existing protocols, LEACH and DHR.
Intelligent Search Agent for Internet Computing with Fuzzy Approach

The advent of Internet and WWW imposes great challenges to search information quickly and accurately. Intelligent search has become a hot topic in Internet computing, with rival search engines battling to become the de facto Web portal. Web search becomes more and more personalized and this requires that the search agent has some "intelligent" to satisfy specific requirements of different users. This paper reports a fuzzy intelligent search agent and shows its great advantages. A detail example has been given to illustrate the pros and cons of the fuzzy approach. Based on our fuzzy intelligent search agent, the efficiency of search has been greatly improved and it will be a practical solution for intelligent information retrieval.
Resource Optimization Strategy for CPU Intensive Applications in Cloud Computing Environment

Traditionally resource utilization on physical servers in cloud data center is uncertain. On one hand, resources will be wasted if the assignment of tasks are not enough. On the other hand it will cause overload if the assignment of tasks are too much. This is especially obvious when the applications are the same type. To solve this issue and considering CPU intensive application is one of the most common type of application in cloud, we have studied the optimization strategy for this kind of applications on the same server. According to resource preferences of different types of applications, we analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can make a prediction of execution time for this case. Extensive experiments show that the model is suitable for CPU intensive applications, and it can accurately predict their execution time. In order to improve the execution efficiency of applications, we propose a scheduling model for CPU intensive applications. Experiments show that the scheduling model can improve the execution efficiency of applications effectively and optimize the resource utilization.
Performance and Power Analysis of High-Density Multi-GPGPU Architectures: A Preliminary Case Study

A system architecture with high-density general purpose graphic processing unit (GPGPU) is emerging as a promising solution that can offer high compute performance and performance-per-watt for building cluster supercomputers. The raw compute power of these heterogeneous systems greatly exceeds the current prevailing homogenous systems, motivating their rapid adoption. These heterogeneous systems do however increase the complexity of developing parallel applications and there is a need to investigate the compute performances and associated power consumption of common benchmarks and scientific computing applications. In this paper, we present the performance and power studies through using the Dell C4130 server that integrates up to 4 GPGPU cards and NVIDIA GPGPU K80 is used. The high performance Linpack (HPL) and molecular dynamics (MD) simulators including NAMD, LAMMPS and GROMACS are tested. Through comparing 4-K80 and 2-Xeon E5-2690 v3 systems, we show that: (1) for HPL tests, the 4- GPU server delivers up to 7 TFLOPS that is 9 times faster than the 2-CPU system and its power efficiency is 4 GFLOPS per Watt, (2) for MD tests, NAMD on 4-GPU server achieves 7.8 times speedup and it uses 2.3 times power consumption compared to 2-CPU system, and LAMMPS achieves 16 times speedup and it uses 2.6 times power consumption, and GROMACS achieves 3.3 times speed up and it uses 2.6 times power consumption. These preliminary results demonstrated that the novel high-density multi-GPGPU architecture offers high performances for computing intensive applications and molecular simulators with superior power efficiencies in a space efficient design. In future, such heterogeneous architecture could be a powerful alternative solution for next generation supercomputer systems.
A Greedy Approach for Caching in Distributed Data Stores

The high latency of read and write makes disks no longer the popular place for data. To meet millions or billions of requests, mostly read, distributed data stores have to rely on memory systems. The cache system in data stores are different from CPU cache. For CPU cache, most workloads have short life span. But in distributed storage, workloads can last for days, months, and even years. How to classify storage workloads is critical to efficient in-memory data stores. Even though memory technologies have experienced dramatically improvement, their capacity are still not enough to hold all data. Cache eviction algorithms like LRU (last recently used) and LFU (least frequently used) are widely adopted by many in-memory systems. However, the uncertainty of storage workloads make these algorithms less efficient and accurate. Because they might ignore or distort important patterns based on their usage of history statistics. To address these issues, we propose a greedy approach for caching in distributed data stores. Our approach leverages the cache system by combining advantages of both LRU and LFU. Our approach requires only a temporary data structure to determine which data to evict. We compare our method with LRU approach. The evaluation shows that our greedy approach reduces the latency by 50% and doubles the throughput for reads. It also improves the performance of the data store for writes by a small fraction.
Research on Application Classification Method in Cloud Computing Environment

Energy consumption is very important to cloud data centers, as it takes a large quotient of the operation cost much related to the environment. To decrease the energy consumption in cloud data center, one possible solution is processing different types of applications in clouds with different strategies. However, to reach this goal, the prime question to be solved is how to effectively classify the type of cloud applications. To solve the problem, this paper uses the method to monitor the resource usage of different applications in clouds, and get the resource usage parameters of different applications. Through analysis, we find the main parameters, which can distinguish different types of applications. Using these parameters we draw out the features of the applications and establish a model to classify different cloud applications. Extensive experiments show that the model put forward can effectively and accurately classify CPU intensive application, I/O intensive application and network intensive application. It can be used as a basis on how to high efficiently use the cloud resources.
Blend Arithmetic Operations on Tensor-Based Fully Homomorphic Encryption Over Real Numbers

Recent booming growth of networking-based solutions have brought numerous challenges to security and privacy from both perspectives of insider and outsider threats. The encrypted data are relatively considered a safe storage status; however, the process of encrypting data is still facing adversarial actions and data process generally is inapplicable over ciphertexts. As a type of the encryption approach allowing computations over ciphertexts, a fully homomorphic encryption (FHE) can concurrently deal with the adversarial hazards and support computations on ciphertexts. This paper focuses on the issue of blend arithmetic operations over real numbers and proposes a novel tensor-based FHE solution. The proposed approach is called a FHE for blend operations model that uses tensor laws to carry the computations of blend arithmetic operations over real numbers. In our paper, we provide both theoretical proof and experimental evaluations in order to evince the adoptability of the proposed approach.
Revealing Feasibility of FMM on ASIC: Efficient Implementation of N-Body Problem on FPGA

FPGAs have been improved significantly in terms of performance and capacity over the last 20 years. The scale of FPGA based design also sparked off the demands for high-level synthesis to handle complicated applications. A well known intricate application is the FMM (Fast Multipole Method) algorithm of N-body problem, which is so complicated that it was not implemented on FPGA as reported in literature. In this paper, we take high level modeling and design tools, i.e. Simulink and System Generator to implement major modules in FMM algorithm to solve the N-Body problem on FPGA. Besides the impressive performance speedup on FPGA, we improve the efficiency by merging the circuits for the common logic among modules in the algorithm. Our experience in efficiently implementing the FMM algorithm will be taken as a useful reference for researchers working on FPGA applications as well as high performance computing.
Rotation Scheduling and Voltage Assignment to Minimize Energy for SoC

Low energy consumption is a critical issue inembedded systems design. As the technology feature sizesof SoC (Systems on Chip) become smaller and smaller, thepercentage of leakage power consumption in total powerconsumption continues to grow. Traditional Dynamic VoltageScaling (DVS) fail to accurately address the impact of scaling on system power consumption as the leakage power increases exponentially. The combination of DVS and Adaptive Body Biasing (ABB) is an effective technique to jointly optimize dynamic and leakage energy dissipation. In this paper, we propose an optimal soft real-time Rotation scheduling and voltage assignment algorithm, RSVAME (Rotation Scheduling and Voltage Assignment to Minimize Energy), to minimize both dynamic and leakage energy via DVS and ABB. Voltage transition overhead has been considered in our approach. We conduct experiments on a set of DSP benchmarks based on the power model of 70 nm technology. The experimental results show that our approach achieves significant energy saving thanthat of ILP (Integer Linear Programming) approach.
Adaptive resource allocation for preemptable jobs in cloud systems

In cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.
Energy Minimization and Latency Hiding for Heterogeneous Parallel Memory

Many high-performance DSP processors employ multi-module on-chip memory to improve performance and power consumption. This paper studies the scheduling and assignment problem that minimizes the total energy while satisfying performance for applications with loops. An algorithm, LSAMEM (Loop Scheduling and Assignment to Minimize Energy for Memory), is proposed. The algorithm attempts to maximum energy saving while satisfying timing constraint with guaranteed probability. The experimental results show that the average improvement on energy-saving is significant by using LSAMEM.
Impact of Cyber-Attacks on Critical Infrastructure

In the contemporary perception, security has ceased to be a luxury in governments or businesses that are concerned with the safeguarding of their sensitive data and privacy. This is as a result of the unswerving emergence of the niggling security threats which are found in dynamic IT systems. The primary purpose of this project is to scrutinize the effect that cyber-attacks has on acute infrastructure. This research essentially relies on qualitative data principally from secondary sources to define the objectives. The study institutes those emergent threats on security including sky Wiper (Flame), the Moonlight maze Stuxnet, and logic bomb as being critical concerns that several governments still struggle to combat. Basing his facts on the above findings, the author does recommend that the owners of critical infrastructure must establish the basic measures to combat these worms. Additionally, appropriate strategies should be put in place so as to address the issue in case it occurs in their firms. Moreover, the author gives a recommendation to scholars to conduct much more in-depth studies in order to determine the extent of these problems.
Electronic Health Record Error Prevention Approach Using Ontology in Big Data

Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.
A Scalable and Quick-Response Software Defined Vehicular Network Assisted by Mobile Edge Computing

Connected vehicles provide advanced transformations and attractive business opportunities in the automotive industry. Presently,  802.11p and evolving 5G are the mainstream radio access technologies in the vehicular industry, but neither of them can meet all requirements of vehicle communication. In order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed. MEC technology with its on-premises feature can decrease data transmission time and enhance quality of user experience in latency-sensitive applications. Therefore, MEC plays as important a role in the proposed architecture as SDN technology. The proposed architecture was validated by a practical use case, and the obtained results have shown that it meets application- specific requirements and maintains good scalability and responsiveness.
A scalable embedded system for massive medical signal processing

Embedded Systems based on FPGA have been adopted as a solution to energy hungry data centers that have to meet real time requirement of massive data processing applications. Classic memory centric design approach was usually favored since most of designers were trained to rely on memory for calculation. However, scalability issue arose from this approach given massive signals from a large number of channels of remote clients. To solve the scalability issue, an embedded system design containing a streaming microarchitecture, embedded scheduler, and communication middleware for massive biomedical signal processing is proposed with a scalable case study of processing massive biomedical signals from multiple channels in this paper. Evaluation results show that our streaming system design has good performance in terms of scalability and latency.
Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems

Energy-saving is extremely important in real-time embedded systems. Dynamic Voltage Scaling (DVS) is one of the prime techniques used to achieve energy-saving. Due to the uncertainties in execution times of some tasks of systems, this paper models each varied execution time as a random variable. By using probabilistic approach, we propose two optimal algorithms, one for uniprocessor and one for multiprocessor to explore soft real-time embedded systems and avoid over-designing them. Our goal is to minimize the expected total energy consumption while satisfying the timing constraint with a guaranteed confidence probability. The solutions can be applied to both hard and soft real-time systems. The experimental results show that our approach achieves significant energy-saving than previous work.
Understanding Taxonomy of Cyber Risks for Cybersecurity Insurance of Financial Industry in Cloud Computing

The dramatical development of Web-based technology has been empowering enormous change in various domains. Cloud-based solutions have remarkably widened business models with multiple value creation channels. The financial industry is a major beneficiary of leveraging these emerging new technologies, such as big data and cloud-related services. This great changing trend has also led to a great concern in cybersecurity. Under this background, cybersecurity insurance is a growing domain in the financial industry. However, cybersecurity insurance industry also encounters a variety of cyber concerns while the Web-based approaches are applied. This paper focuses on this issue and review a broad scope of materials to gain a deep understanding of taxonomy of cyber security risks for cybersecurity insurance. The findings of this work can guide the cybersecurity insurance practitioners to avoid as much risk as possible as well as create potential solutions to the possible risks.
Ontology-Based Knowledge Representation for Secure Self-Diagnosis in Patient-Centered Teleheath with Cloud Systems

The implementation of cloud computing in tele-health has enabled enormous benefits of improving health-care services as well as active health approaches, such as Patient-Centered Telehealth (PCT). Applying cloud systems enables healthcare users to obtain medical information from multiple cloud-based platforms or sources. Various service deployments meet different customers' needs. As one of the popular information sharing manners on clouds, Online Self-Diagnosis (OSD) has been widely implemented for recommending medicines or treatment plans in the varied medical fields. However, there is a great risk for users when executing OSD since lack of professional pharmacological knowledge. This paper concentrates on this issue and proposes an ontology-based approach representing the medical knowledge for alarming potential risks. The proposed mechanism is named Secure Ontology-based Self-diagnosis (SOS) Model that is designed to generate knowledge of medical treatments or incompatibility of medicines to avoid improper behaviors of diagnoses. Based on the proposed model, our algorithm uses structure-level technique applying semantic schemas, which is Propositional Satisfiability Matching Algorithm (PSMA). Our experiment has proved the feasibility of our proposed mechanism.
A Design for Scalable and Secure Key-Value Stores

Reliable and scalable storage systems are key to cloud-based applications. In cloud storage, users store their data on remote servers rather than their local computers. Secure storage is used to ensure the safety of data in clouds. As more and more users rely on third-party cloud vendors to store their data, concerns have arisen among users and cloud providers. Encryption-based approaches are commonly used in secure storage systems. Data are encrypted and stored on persistent storage like disks and flash memories. When data are needed by the users, they are decrypted and accessed by the users. This way of managing data hurts the scalability and throughput of cloud systems. In the meantime, cloud systems have to perform fault-tolerance strategies on data, which also brings performance deduction. The combination of these issues cause a high price for data security in cloud systems. Aware of such issues. we propose methods to reduce the overhead of secure storage while guaranteeing the safeness of data.