Showing papers in "IEEE Transactions on Computers in 2015"

Design and Analysis of Approximate Compressors for Multiplication

Abstract: Inexact (or approximate) computing is an attractive paradigm for digital processing at nanometric scales. Inexact computing is particularly interesting for computer arithmetic designs. This paper deals with the analysis and design of two new approximate 4-2 compressors for utilization in a multiplier. These designs rely on different features of compression, such that imprecision in computation (as measured by the error rate and the so-called normalized error distance) can meet with respect to circuit-based figures of merit of a design (number of transistors, delay and power consumption). Four different schemes for utilizing the proposed approximate compressors are proposed and analyzed for a Dadda multiplier. Extensive simulation results are provided and an application of the approximate multipliers to image processing is presented. The results show that the proposed designs accomplish significant reductions in power dissipation, delay and transistor count compared to an exact design; moreover, two of the proposed multiplier designs provide excellent capabilities for image multiplication with respect to average normalized error distance and peak signal-to-noise ratio (more than 50 dB for the considered image examples).

Fault Analysis-Based Logic Encryption

Abstract: Globalization of the integrated circuit (IC) design industry is making it easy for rogue elements in the supply chain to pirate ICs, overbuild ICs, and insert hardware Trojans. Due to supply chain attacks, the IC industry is losing approximately $4 billion annually. One way to protect ICs from these attacks is to encrypt the design by inserting additional gates such that correct outputs are produced only when specific inputs are applied to these gates. The state-of-the-art logic encryption technique inserts gates randomly into the design, but does not necessarily ensure that wrong keys corrupt the outputs. Our technique ensures that wrong keys corrupt the outputs. We relate logic encryption to fault propagation analysis in IC testing and develop a fault analysis-based logic encryption technique. This technique enables a designer to controllably corrupt the outputs. Specifically, to maximize the ambiguity for an attacker, this technique targets 50% Hamming distance between the correct and wrong outputs (ideal case) when a wrong key is applied. Furthermore, this 50% Hamming distance target is achieved using a smaller number of additional gates when compared to random logic encryption.

Identity-Based Encryption with Outsourced Revocation in Cloud Computing

Abstract: Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm

Abstract: Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell (SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.

Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications

Abstract: Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust , to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based on SearchAdjust , we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces.

Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks

Abstract: Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate our proposed models and algorithm, we further apply the physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

RRAM Defect Modeling and Failure Analysis Based on March Test and a Novel Squeeze-Search Scheme

Abstract: The Resistive Random Access Memory (RRAM) is a new type of non-volatile memory based on the resistive memory device. Researchers are currently moving from resistive device development to memory circuit design and implementation, hoping to fabricate memory chips that can be deployed in the market in the near future. However, so far the low manufacturing yield is still a major issue. In this paper, we propose defect and fault models specific to RRAM, i.e., the Over-Forming (OF) defect and the Read-One-Disturb (R1D) fault. We then propose a March algorithm to cover these defects and faults in addition to the conventional RAM faults, which is called March C*. We also develop a novel squeeze-search scheme to identify the OF defect, which leads to the Stuck-At Fault (SAF). The proposed test algorithm is applied to a first-cut 4-Mb HfO 2 -based RRAM test chip. Results show that OF defects and R1D faults do exist in the RRAM chip. We also identify specific failure patterns from the test results, which are shown to be induced by multiple short defects between bit-lines. By identifying the defects and faults, designers and process engineers can improve the RRAM yield in a more cost-effective way.

MuR-DPA: Top-Down Levelled Multi-Replica Merkle Hash Tree Based Secure Public Auditing for Dynamic Big Data Storage on Cloud

Abstract: Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users’ direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.

Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems

Abstract: Generally, a parallel application consists of precedence constrained stochastic tasks, where task processing times and intertask communication times are random variables following certain probability distributions. Scheduling such precedence constrained stochastic tasks with communication times on a heterogeneous cluster system with processors of different computing capabilities to minimize a parallel application's expected completion time is an important but very difficult problem in parallel and distributed computing. In this paper, we present a model of scheduling stochastic parallel applications on heterogeneous cluster systems. We discuss stochastic scheduling attributes and methods to deal with various random variables in scheduling stochastic tasks. We prove that the expected makespan of scheduling stochastic tasks is greater than or equal to the makespan of scheduling deterministic tasks, where all processing times and communication times are replaced by their expected values. To solve the problem of scheduling precedence constrained stochastic tasks efficiently and effectively, we propose a stochastic dynamic level scheduling (SDLS) algorithm, which is based on stochastic bottom levels and stochastic dynamic levels. Our rigorous performance evaluation results clearly demonstrate that the proposed stochastic task scheduling algorithm significantly outperforms existing algorithms in terms of makespan, speedup, and makespan standard deviation.

Deploying Wireless Sensor Networks with Fault-Tolerance for Structural Health Monitoring

Abstract: Structural health monitoring (SHM) systems are implemented for structures (e.g., bridges, buildings) to monitor their operations and health status. Wireless sensor networks (WSNs) are becoming an enabling technology for SHM applications that are more prevalent and more easily deployable than traditional wired networks. However, SHM brings new challenges to WSNs: engineering-driven optimal deployment, a large volume of data, sophisticated computing, and so forth. In this paper, we address two important challenges: sensor deployment and decentralized computing. We propose a solution, to deploy wireless sensors at strategic locations to achieve the best estimates of structural health (e.g., damage) by following the widely used wired sensor system deployment approach from civil/structural engineering. We found that faults (caused by communication errors, unstable connectivity, sensor faults, etc.) in such a deployed WSN greatly affect the performance of SHM. To make the WSN resilient to the faults, we present an approach, called ${\tt FTSHM}$ (fault-tolerance in SHM), to repair the WSN and guarantee a specified degree of fault tolerance. ${\tt FTSHM}$ searches the repairing points in clusters in a distributed manner, and places a set of backup sensors at those points in such a way that still satisfies the engineering requirements. ${\tt FTSHM}$ also includes an SHM algorithm suitable for decentralized computing in the energy-constrained WSN, with the objective of guaranteeing that the WSN for SHM remains connected in the event of a fault, thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of ${\tt FTSHM}$ through extensive simulations and real experimental settings on a physical structure.

A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks

Abstract: Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people’s behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people’s preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Secure Distributed Deduplication Systems with Improved Reliability

Abstract: Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

Detection of Denial-of-Service Attacks Based on Computer Vision Techniques

Abstract: Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover’s Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance $L_{p}$ and $X^{2}$ statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second.

A Profit Maximization Scheme with Guaranteed Quality of Service in Cloud Computing

Abstract: As an effective and efficient way to provide computing resources and services to customers on demand, cloud computing has become more and more popular. From cloud service providers’ perspective, profit is one of the most important considerations, and it is mainly determined by the configuration of a cloud service platform under given market demand. However, a single long-term renting scheme is usually adopted to configure a cloud platform, which cannot guarantee the service quality but leads to serious resource waste. In this paper, a double resource renting scheme is designed firstly in which short-term renting and long-term renting are combined aiming at the existing issues. This double renting scheme can effectively guarantee the quality of service of all requests and reduce the resource waste greatly. Secondly, a service system is considered as an M/M/m+D queuing model and the performance indicators that affect the profit of our double renting scheme are analyzed, e.g., the average charge, the ratio of requests that need temporary servers, and so forth. Thirdly, a profit maximization problem is formulated for the double renting scheme and the optimized configuration of a cloud platform is obtained by solving the profit maximization problem. Finally, a series of calculations are conducted to compare the profit of our proposed scheme with that of the single renting scheme. The results show that our scheme can not only guarantee the service quality of all requests, but also obtain more profit than the latter.

Verifiable Auditing for Outsourced Database in Cloud Computing

Abstract: The notion of database outsourcing enables the data owner to delegate the database management to a cloud service provider (CSP) that provides various database services to different users. Recently, plenty of research work has been done on the primitive of outsourced database. However, it seems that no existing solutions can perfectly support the properties of both correctness and completeness for the query results, especially in the case when the dishonest CSP intentionally returns an empty set for the query request of the user. In this paper, we propose a new verifiable auditing scheme for outsourced database, which can simultaneously achieve the correctness and completeness of search results even if the dishonest CSP purposely returns an empty set. Furthermore, we can prove that our construction can achieve the desired security properties even in the encrypted outsourced database. Besides, the proposed scheme can be extended to support the dynamic database setting by incorporating the notion of verifiable database with updates.

Collaborative Mobile Charging

Abstract: The limited battery capacity of sensor nodes has become one of the most critical impediments that stunt the deployment of wireless sensor networks (WSNs). Recent breakthroughs in wireless energy transfer and rechargeable lithium batteries provide a promising alternative to power WSNs: mobile vehicles/robots carrying high volume batteries serve as mobile chargers to periodically deliver energy to sensor nodes. In this paper, we consider how to schedule multiple mobile chargers to optimize energy usage effectiveness, such that every sensor will not run out of energy. We introduce a novel charging paradigm, collaborative mobile charging, where mobile chargers are allowed to intentionally transfer energy between themselves. To provide some intuitive insights into the problem structure, we first consider a scenario that satisfies three conditions, and propose a scheduling algorithm, PushWait, which is proven to be optimal and can cover a one-dimensional WSN of infinite length. Then, we remove the conditions one by one, investigating chargers’ scheduling in a series of scenarios ranging from the most restricted one to a general 2D WSN. Through theoretical analysis and simulations, we demonstrate the advantages of the proposed algorithms in energy usage effectiveness and charging coverage.

End-to-End Communication Delay Analysis in Industrial Wireless Networks

Abstract: WirelessHART is a new standard specifically designed for real-time and reliable communication between sensor and actuator devices for industrial process monitoring and control applications. End-to-end communication delay analysis for WirelessHART networks is required to determine the schedulability of real-time data flows from sensors to actuators for the purpose of acceptance test or workload adjustment in response to network dynamics. In this paper, we consider a network model based on WirelessHART, and map the scheduling of real-time periodic data flows in the network to real-time multiprocessor scheduling. We then exploit the response time analysis for multiprocessor scheduling and propose a novel method for the delay analysis that establishes an upper bound of the end-to-end communication delay of each real-time flow in the network. Simulation studies based on both random topologies and real network topologies of a $74$ -node physical wireless sensor network testbed demonstrate that our analysis provides safe and reasonably tight upper bounds of the end-to-end delays of real-time flows, and hence enables effective schedulability tests for WirelessHART networks.

Energy Minimization in Multi-Task Software-Defined Sensor Networks

Abstract: After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks

Abstract: Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking , aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target’s movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.

Cost-Effective Authentic and Anonymous Data Sharing with Forward Security

Abstract: Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Exploring the Feasibility of Fully Homomorphic Encryption

Abstract: In 2010, Gentry and Halevi presented the first FHE implementation. FHE allows the evaluation of arbitrary functions directly on encrypted data on untrusted servers. However, even for the small setting with 2048 dimensions, the authors reported a performance of 1.8 s for a single bit encryption and 32 s for recryption on a high-end server. Much of the latency is due to computationally intensive multi-million-bit modular multiplications. In this paper, we introduce two optimizations coupled with a novel precomputation technique. In the first optimization called partial FFT, we adopt Strassen’s FFT-based multiplication algorithm along with Barret reduction to speedup modular multiplications. For the encrypt primitive, we employ a window-based evaluation technique along with a modest degree of precomputation. In the full FFT optimization, we delay modular reductions and change the window algorithm, which allows us to carry out the bulk of computations in the frequency domain. We manage to eliminate all FFT conversion except the final inverse transformation drastically reducing the computation latency for all FHE primitives. We implemented the GH FHE scheme on two GPUs to further speedup the operations. Our experimental results with small parameter setting show speedups of 174, 7.6, and 13.5 times for encryption, decryption, and recryption, respectively, when compared to the Gentry–Halevi implementation. The speedup is enhanced in the medium setting. However, in the large setting, memory becomes the bottleneck and the speedup is somewhat diminished.

Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs

Abstract: This paper presents a sparse matrix partitioning strategy to improve the performance of SpMV on GPUs and multicore CPUs. This method has wide adaptability for different types of sparse matrices, and is different from existing methods which only adapt to some particular sparse matrices. In addition, our partitioning method can obtain dense blocks by analyzing the probability distribution of non-zero elements in a sparse matrix, and result in very low proportion of zero padded. We make the following significant contributions. (1) We present a partitioning strategy of sparse matrices based on probabilistic modeling of non-zero elements in a row. (2) We prove that our method has the highest mean density compared with other strategies according to certain given ratios of partition obtained from the computing powers of heterogeneous processors. (3) We develop a CPU-GPU hybrid parallel computing model for SpMV on GPUs and multicore CPUs in a heterogeneous computing platform. Our partitioning strategy has balanced load distribution and the performance of SpMV is significantly improved when a sparse matrix is partitioned into dense blocks using our method. The average performance improvement of our solution for SpMV is about 15.75 percent on multicore CPUs, compared to that of the other solutions. By considering the rows of a matrix in a unique order based on the probability mass function of the number of non-zeros in a row, the average performance improvement of our solution for SpMV is about 33.52 percent on GPUs and multicore CPUs of a heterogeneous computing platform, compared to that of the partitioning methods based on the original row order of a matrix.

Accelerating Fully Homomorphic Encryption in Hardware

Abstract: We present a custom architecture for realizing the Gentry-Halevi fully homomorphic encryption (FHE) scheme. This contribution presents the first full realization of FHE in hardware. The architecture features an optimized multi-million bit multiplier based on the Schonhage Strassen multiplication algorithm. Moreover, a number of optimizations including spectral techniques as well as a precomputation strategy is used to significantly improve the performance of the overall design. When synthesized using 90 nm technology, the presented architecture achieves to realize the encryption, decryption, and recryption operations in 18.1 msec, 16.1 msec, and 3.1 sec, respectively, and occupies a footprint of less than 30 million gates.

AppATP : An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol

Abstract: Many mobile applications require frequent wireless transmissions between the content provider and mobile devices, consuming much energy in mobile devices. Motivated by the popularity of prefetch-friendly or delay-tolerant apps (e.g., social networking, app updates, cloud storage), we design and implement an application-layer transmission protocol, AppATP, which leverages cloud computing to manage data transmissions for mobile apps, transferring data to and from mobile devices in an energy-efficient manner. Measurements show that significantly amount of energy is consumed by mobile devices during poor connectivity. Based on this observation, AppATP adaptively seizes periods of good bandwidth condition to prefetch frequently used data with minimum energy consumption, while deferring delay-tolerant data during poor network connectivity. Using the stochastic control framework, AppATP only relies on the current network information and data queue sizes to make an online decision on transmission scheduling, and performs well under unpredictable wireless network conditions. We implement AppATP on Samsung Note 2 smartphones and Amazon EC2. Results from both trace-driven simulations and extensive real-world experiments show that AppATP can be applied to a variety of application scenarios while achieving $30$ - $50$ percent energy savings for mobile devices.

Testing Open Defects in Memristor-Based Memories

Abstract: Memristor-based memory technology, also referred to as resistive RAM (RRAM), is one of the emerging memory technologies potentially to replace conventional semiconductor memories such as SRAM, DRAM, and flash. Existing research on such novel circuits focuses mainly on the integration between CMOS and non-CMOS, fabrication techniques, and reliability improvement. However, research on (manufacturing) test for yield and quality improvement is still in its infancy stage. This paper presents fault analysis and modeling for open defects based on electrical simulation, introduces fault models, and proposes test approaches for RRAMs. The fault analysis reveals that unique faults occur in addition to some conventional memory faults, and the detection of such unique faults cannot be guaranteed with just the application of traditional march tests. The paper also presents a new Design-for-Testability (DfT) concept to facilitate the detection of the unique faults. Two DfT schemes are developed by exploiting the access time duration and supply voltage level of the RRAM cells, and their simulation results show that the fault coverage can be increased with minor circuit modification. As the fault behavior may vary due to process variations, the DfT schemes are extended to be programmable to track the changes and further improve the fault/defect coverage.

CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

Abstract: The maximum satisfiability (MAX-SAT) problem, especially the weighted version, has extensive applications. Weighted MAX-SAT instances encoded from real-world applications may be very large, which calls for efficient approximate methods, mainlystochastic local search (SLS) ones. However, few works exist on SLS algorithms for weighted MAX-SAT. In this paper, we propose a new heuristic called CCM for weighted MAX-SAT. The CCM heuristic prefers to select a CCMP variable. By combining CCM withrandom walk, we design a simple SLS algorithm dubbed CCLS for weighted MAX-SAT. The CCLS algorithm is evaluated against a state-of-the-art SLS solver IRoTS and two state-of-the-art complete solvers namely akmaxsat_ls and New WPM2, on a broad range of weighted MAX-SAT instances. Experimental results illustrate that the quality of solution found by CCLS is much better than that found by IRoTS, akmaxsat_ls and New WPM2 on most industrial, crafted and random instances, indicating the efficiency and the robustness of the CCLS algorithm. Furthermore, CCLS is evaluated in the weighted and unweighted MAX-SAT tracks of incomplete solvers in the Eighth Max-SAT Evaluation (Max-SAT 2013), and wins four tracks in this evaluation, illustrating that the performance of CCLS exceeds the current state-of-the-art performance of SLS algorithms on solving MAX-SAT instances.

A Novel En-Route Filtering Scheme Against False Data Injection Attacks in Cyber-Physical Networked Systems

Abstract: In Cyber-Physical Networked Systems (CPNS), the adversary can inject false measurements into the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromise-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. PCREF adopts polynomials instead of Message Authentication Codes (MACs) for endorsing measurement reports to achieve resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial, derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Through extensive theoretical analysis and experiments, our data shows that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Completely Pinpointing the Missing RFID Tags in a Time-Efficient Way

Abstract: Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Characterization of Cascading Failures in Interdependent Cyber-Physical Systems

Abstract: In this paper, we focus on the cyber-physical system consisting of interdependent physical-resource and computational-resource networks, e.g., smart power grids, automated traffic control system, and wireless sensor and actuator networks, where the physical-resource and computational-resource network are connected and mutually dependent. The failure in physical-resource network might cause failures in computational-resource network, and vice versa. A small failure in either of them could trigger cascade of failures within the entire system. We aim to investigate the issue of cascading failures occur in such system. We propose a typical and practical model by introducing the interdependent complex network. The interdependence between two networks is practically defined as follows: Each node in the computational-resource network has only one support link from the physical-resource network, while each node in physical-resource network is connected to multiple computational nodes. We study the effect of cascading failures using percolation theory and present detailed mathematical analysis of failure propagation in the system. We analyze the robustness of our model caused by random attacks or failures by calculating the size of functioning parts in both networks. Our mathematical analysis proves that there exists a threshold for the proportion of faulty nodes, above which the system collapses. Using extensive simulations, we determine the critical values for different system parameters. Our simulation also shows that, when the proportion of faulty nodes approaching critical value, the size of functioning parts meets a second-order transition. An important observation is that the size of physical-resource and computational-resource networks, and the ratio between their sizes do not affect the system robustness.

FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds

Abstract: As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role However, few researches on the fault-tolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently To address this issue, this paper presents a fault-tolerant mechanism which extends the primary-backup model to incorporate the features of clouds Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel f ault-tolerant e lastic s cheduling algorithms for real-time ta sks in c l ouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, ie, N on- M igration-FESTAL (NMFESTAL), N on- O verlapping-FESTAL (NOFESTAL), and E lastic F irst F it (EFF) The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds