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Program control flow reflects the algorithm of that program and may reveal implementation vulnerabilities. Thus its confidentiality needs to be protected, especially in a cloud setting. However, most existing control flow obfuscation methods are software-based, which cannot offer high confidentiality while maintaining low performance overhead. In this paper, we propose CFHider, a hardware-assisted solution. By performing program transformation and leveraging Trusted Execution Environments (Intel SGX), CFHider moves branch statement conditions to an opaque and trusted memory space during the program execution. We proved that by generating Obfuscation Invariants, CFHider is able to provide provable control flow confidentiality protection. Based on the design of CFHider, we also developed a prototype system for Java applications. Our security analysis and experimental results indicate that CFHider is effective in protecting control flow confidentiality and incurs a much reduced performance overhead than existing software-based solutions (by a factor of 18.1). [ABSTRACT FROM AUTHOR]

Program slicing, as a technique of program decomposition, is widely used in the field of program testing, model checking, software verification, symbolic execution, and other fields. However, the traditional approaches of program slicing tend to produce too large slices and the static program analyses are hard to be precise enough. Scenario-oriented program slicing, which considers the actual usage of software, gives a more precise perspective of program slicing. In this paper, we propose an approach of scenario-oriented program slicing, which combines constraint logic programming and program transformation. According to the observation that the output of a program transformation is a semantically equivalent program where the properties of interest are preserved, we can apply a sequence of transformations, more powerful than those needed for program specialization, refining the slicing to the desired degree of precision. And constraint logic programming has been shown to be a powerful, flexible formalism to reason about the correctness of programs. The novel contributions of this paper are as follows: 1) converting the problem of program slicing into program transformation and retrieval; 2) presenting a set of constraint handling rules for scenario-oriented program slicing in constraint logical programming programs; and 3) deriving a scenario-oriented program slicing algorithm. The method of scenario-oriented program slicing has been implemented and we have demonstrated its effectiveness and efficiency on three open source software projects in GitHub.

A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative importance among them. It is of critical importance to adjust these parameters, as quality of the solution depends on their values. Tuning parameters are a relatively straight forward task for a human, as one can intuitively determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but also becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative computed tomography (CT) reconstruction with a pixel-wise total-variation regularization term. We set up a parameter-tuning policy network (PTPN), which maps a CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN, reconstructed CT images attain quality similar or better than those reconstructed with manually tuned parameters. [ABSTRACT FROM AUTHOR]

The application of hybrid precoding in millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems has been proven effective for reducing the number of radio frequency (RF) chains. However, the maximum number of independent data streams is conventionally restricted by the number of RF chains, which limits the spatial multiplexing gain. To further improve the achievable spectral efficiency (SE), in this paper we propose a generalized spatial modulation aided mm-wave MIMO system to convey an extra data stream via the index of the active antennas group, while no extra RF chain is required. Moreover, we propose a hybrid analog and digital precoding scheme for SE maximization. Based on a narrowband mm-wave-MIMO channel assumption, a closed-form lower bound is derived to quantify the achievable SE of the proposed system. By utilizing this lower bound as the cost function, a two-step algorithm is proposed to optimize the hybrid precoder. The proposed algorithm not only utilizes the concavity of the cost function over the digital power allocation vector but also invokes the convex $\ell _\infty$ relaxation to handle the non-convex constraint imposed by analog precoding. Finally, the proposed scheme is shown via simulations to outperform state-of-the-art mm-wave MIMO schemes in terms of achievable SE. [ABSTRACT FROM PUBLISHER]

Cooperative multicast has been demonstrated to achieve significant performance gain over the classic source–destination transmission paradigm by exploiting spatial diversity through the participation of multiple relay nodes. As a major technical challenge, the selection of relays for a multicast session has significant impact on the multicast performance. The challenge is even more pronounced when the number of channels is limited as the relay selection is in this context coupled with channel allocation. The goal of this paper is to design a fair multicast relay selection scheme with limited channel resources. Specifically, we establish an analytical framework for this joint relay selection and channel allocation problem and develop a lexicographic max–min multicast relay selection scheme. Our design consists of two technical steps. First, we consider the maximization of the minimal data rate. By decoupling relay selection and channel allocation, the problem is transformed to a max–min–max problem, which is difficult to solve. To make this problem tractable, we reformulate it as a convex optimization problem via relaxation and smoothing, and prove the asymptotic equivalence from a geometrical perspective. Second, we propose an adjustment algorithm based on the initial max–min solution, and prove that the proposed scheme achieves lexicographic optimality. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes. [ABSTRACT FROM PUBLISHER]

This paper is focused on techniques for maximizing utility across all users within a total network transit cost budget. We present a new method for selecting between replicated servers distributed over the Internet. First, we introduce a novel utility framework that factors in quality of service metrics. Then we design an optimization algorithm, solvable in polynomial time, to allocate user requests to servers based on utility while satisfying network transit cost constraints, mapping service names to service instance locators. We then describe an efficient, low overhead distributed model which only requires knowledge of a fraction of the data required by the global optimization formulation. Next, a load-balancing variant of the algorithm is explored that substantially reduces blocking caused by congested servers. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica, while meeting network traffic cost constraints. We discuss several options for real-world deployment that require no changes to end-systems based on either the use of SDN controllers or extensions to the current DNS system. [ABSTRACT FROM AUTHOR]

The promise of strong physical unclonable functions (PUF) is to utilize the manufacturing variations of circuit elements to produce an independent and unpredictable response to any input challenge vector. Attacks on PUFs that predict the responses to input challenge vectors offer an interesting research problem. An attacking approach based on the optimization theory and side-channel information is proposed where we estimate the manufacturing variations of the circuit elements and predict the PUF’s responses to challenge vectors whose actual responses are not known. We apply this attacking approach on some popular PUF designs, including the Arbiter PUFs, the Memristor Crossbar PUFs, and the XOR Arbiter PUFs. Simulations show a substantial reduction in attack complexity compared with previously proposed machine-learning (ML)-based attacks: we achieve an average reduction of 66% in attack time compared with the ML approach. Despite some overhead, our approach is also applicable when the PUF responses are noisy. [ABSTRACT FROM PUBLISHER]

In the last decade, several GPU implementations of Support Vector Machine (SVM) training with nonlinear kernels were published. Some of them even with source codes. The most effective ones are based on Sequential Minimal Optimization (SMO). They decompose the restricted quadratic problem into a series of smallest possible subproblems, which are then solved analytically. For large datasets, the majority of elapsed time is spent by a large amount of matrix-vector multiplications that cannot be computed efficiently on current GPUs because of limited memory bandwidth. In this paper, we introduce a novel GPU approach to the SVM training that we call Optimized Hierarchical Decomposition SVM (OHD-SVM). It uses a hierarchical decomposition iterative algorithm that fits better to actual GPU architecture. The low decomposition level uses a single GPU multiprocessor to efficiently solve a local subproblem. Nowadays a single GPU multiprocessor can run thousand or more threads that are able to synchronize quickly. It is an ideal platform for a single kernel SMO-based local solver with fast local iterations. The high decomposition level updates gradients of entire training set and selects a new local working set. The gradient update requires many kernel values that are costly to compute. However, solving a large local subproblem offers an efficient kernel values computation via a matrix-matrix multiplication that is much more efficient than the matrix-vector multiplication used in already published implementations. Along with a description of our implementation, the paper includes an exact comparison of five publicly available C++ SVM training GPU implementations. In this paper, the binary classification task and RBF kernel function are taken into account as it is usual in most of the recent papers. According to the measured results on a wide set of publicly available datasets, our proposed approach excelled significantly over the other methods in all datasets. The biggest difference was on the largest dataset where we achieved speed-up up to 12 times in comparison with the fastest already published GPU implementation. Moreover, our OHD-SVM is the only one that can handle dense as well as sparse datasets. Along with this paper, we published the source-codes at https://github.com/OrcusCZ/OHD-SVM. [ABSTRACT FROM AUTHOR]

Nonvolatile static random access memory (nvSRAM) has been widely investigated as a promising on-chip memory architecture in energy harvesting sensor nodes, due to zero standby power, resilience to power failures, and fast read/write operations. However, conventional approaches back up all data from static random access memory into nonvolatile memory when power failures happen. It leads to significant energy overhead and peak inrush current, which has a negative impact on the system performance and circuit reliability. This paper proposes a holistic data backup optimization to mitigate these problems in nvSRAM, consisting of a partial backup algorithm and a run-time adaptive write policy. A statistic dead-block predictor is employed to achieve dead block identification with trivial hardware overhead. An adaptive policy is used to switch between write-back and write-through strategy to reduce the rollback induced by backup failures. Experimental results show that the proposed scheme improves the performance by 4.6% on average while the backup power consumption and the inrush current are reduced by 38.1% and 54% on average compared to the full backup scheme. What is more, the backup capacitor size for energy buffer can be reduced by 40% on average under the same performance constraint. [ABSTRACT FROM PUBLISHER]

We present an information-theoretic approach to lower bound the oracle complexity of nonsmooth black box convex optimization, unifying previous lower bounding techniques by identifying a combinatorial problem, namely string guessing, as a single source of hardness. As a measure of complexity, we use distributional oracle complexity, which subsumes randomized oracle complexity as well as worst case oracle complexity. We obtain strong lower bounds on distributional oracle complexity for the box [-1,1]^n , as well as for the L^p -ball for $p \geq 1$ (for both low-scale and large-scale regimes), matching worst case upper bounds, and hence we close the gap between distributional complexity, and in particular, randomized complexity and worst case complexity. Furthermore, the bounds remain essentially the same for high-probability and bounded-error oracle complexity, and even for combination of the two, i.e., bounded-error high-probability oracle complexity. This considerably extends the applicability of known bounds. [ABSTRACT FROM PUBLISHER]

This paper presents an energy-efficient face detection and recognition processor aimed at mobile applications. The algorithmic optimizations including hybrid search scheme for face detection significantly reduce computational complexity and architecture modification such as feature memory segmentation and further reduce energy consumption. We utilize characteristics of the implemented algorithm and propose a 5T SRAM design heavily optimized for mostly-read operations. Systematic reset and write schemes allow for reliable data write operation. The 5T SRAM reduces the cell area by 7.2% compared to a conventional 6T bit cell in logic rule while significantly improving read margin and voltage scalability due to a decoupled read path. The fabricated processor consumes only 23 mW while processing both face detection and recognition in real time at 5.5 frames/s throughput. [ABSTRACT FROM PUBLISHER]

Most stencil computations allow tile-wise concurrent start, i.e., there always exists a face of the iteration space and a set of tiling directions such that all tiles along that face can be started concurrently. This provides load balance and maximizes parallelism. However, existing automatic tiling frameworks often choose hyperplanes that lead to pipelined start-up and load imbalance. We address this issue with a new tiling technique, called diamond tiling, that ensures concurrent start-up as well as perfect load-balance whenever possible. We first provide necessary and sufficient conditions for a set of tiling hyperplanes to allow concurrent start for programs with affine data accesses. We then provide an approach to automatically find such hyperplanes. Experimental evaluation on a 12-core Intel Westmere shows that diamond tiled code is able to outperform a tuned domain-specific stencil code generator by 10 to 40 percent, and previous compiler techniques by a factor of 1.3 $\times$ to 10.1$\times$ . [ABSTRACT FROM AUTHOR]

As computational clouds offer increasingly sophisticated services, there is a dramatic increase in the variety and complexity of virtual machine (VM) placement problems. In this paper, we consider a VM placement problem with a special type of anti-colocation requirements—disk anti-colocation—which stipulate that, for every VM assigned to a PM (physical machine), its virtual disks should be spread out across the physical disks of the PM. Once such a requirement is met, the users of the VM can expect improved disk I/O performance. There will also be improvement in fault tolerance and availability. For scalable solutions, we propose a method that combines hierarchical decomposition with mixed integer programming (MIP), where the basic building blocks are independent, small MIP subproblems. We provide experimental results to demonstrate the effectiveness of the proposed method. We show that it is scalable and achieves high performance with respect to the optimization objective. [ABSTRACT FROM AUTHOR]

We consider a system, comprising a library of N files (e.g., movies) and a wireless network with a K_{T} transmitters, each equipped with a local cache of size of M_{T} files and a KR receivers, each equipped with a local cache of size of MR files. Each receiver will ask for one of the $N$ files in the library, which needs to be delivered. The objective is to design the cache placement (without prior knowledge of receivers’ future requests) and the communication scheme to maximize the throughput of the delivery. In this setting, we show that the sum degrees-of-freedom (sum-DoF) of $\min \left \{{\frac {K_{T} M_{T}+K_{R} M_{R}}{N},K_{R}}\right \}$ is achievable, and this is within a factor of 2 of the optimum, under uncoded prefetching and one-shot linear delivery schemes. This result shows that (i) the one-shot sum-DoF scales linearly with the aggregate cache size in the network (i.e., the cumulative memory available at all nodes), (ii) the transmitters’ caches and receivers’ caches contribute equally in the one-shot sum-DoF, and (iii) caching can offer a throughput gain that scales linearly with the size of the network. To prove the result, we propose an achievable scheme that exploits the redundancy of the content at transmitter’s caches to cooperatively zero-force some outgoing interference, and availability of the unintended content at the receiver’s caches to cancel (subtract) some of the incoming interference. We develop a particular pattern for cache placement that maximizes the overall gains of cache-aided transmit and receive interference cancellations. For the converse, we present an integer optimization problem which minimizes the number of communication blocks needed to deliver any set of requested files to the receivers. We then provide a lower bound on the value of this optimization problem, hence leading to an upper bound on the linear one-shot sum-DoF of the network, which is within a factor of 2 of the achievable sum-DoF. [ABSTRACT FROM AUTHOR]

A novel approach termed stochastic truncated amplitude flow (STAF) is developed to reconstruct an unknown n-dimensional real-/complex-valued signal \boldsymbol {x} from m “phaseless” quadratic equations of the form \psi _i=|\langle \boldsymbol a_i,\boldsymbol x\rangle | . This problem, also known as phase retrieval from magnitude-only information, is NP-hard in general. Adopting an amplitude-based nonconvex formulation, STAF leads to an iterative solver comprising two stages: s1) Orthogonality-promoting initialization through a stochastic variance reduced gradient algorithm; and, s2) a series of iterative refinements of the initialization using stochastic truncated gradient iterations. Both stages involve a single equation per iteration, thus rendering STAF a simple, scalable, and fast approach amenable to large-scale implementations that are useful when n is large. When \lbrace \boldsymbol a_i\rbrace i=1^m are independent Gaussian, STAF provably recovers exactly any \boldsymbol x\in \mathbbR^n exponentially fast based on order of n quadratic equations. STAF is also robust in the presence of additive noise of bounded support. Simulated tests involving real Gaussian \lbrace \boldsymbol {a}_i\rbrace vectors demonstrate that STAF empirically reconstructs any \boldsymbol x\in \mathbbR^n exactly from about 2.3n$ magnitude-only measurements, outperforming state-of-the-art approaches and narrowing the gap from the information-theoretic number of equations $m=2n-1$ . Extensive experiments using synthetic data and real images corroborate markedly improved performance of STAF over existing alternatives. [ABSTRACT FROM PUBLISHER]

This paper reports the development and application of a reconstruction method based on differential evolution (DE) to solve an underdetermined tactile sensing system with quasi-distributed fiber sensors. The reconstruction relies on the coupled responses from eight fiber Bragg grating-based transducers. The sensing system is capable of locating and quantifying up to three loads simultaneously applied to a metallic plate divided into 16 regions. The signal reconstruction is performed using compressive sensing methods to infer the spatial distribution of the applied forces. A comparison between the implemented method based on DE and traditional sparse signal recovery schemes (LASSO, OMP, Robust-SL0, and CoSaMP) showed the better performance of the proposed algorithm in the demonstrated application. [ABSTRACT FROM AUTHOR]

To foster the energy-efficiency in current and future multi-core processors, the benefits and trade-offs of a large set of optimization solutions must be evaluated. For this purpose, it is often crucial to consider how key micro-architecture aspects, such as accessing different memory levels and functional units, affect the attainable power and energy consumption. To ease this process, we propose a set of insightful cache-aware models to characterize the upper-bounds for power, energy and energy-efficiency of modern multi-cores in three different domains of the processor chip: cores, uncore and package. The practical importance of the proposed models is illustrated when optimizing matrix multiplication and deriving a set of power envelopes and energy-efficiency ranges of the micro-architecture for different operating frequencies. The proposed models are experimentally validated on a computing platform with a quad-core Intel 3770K processor by using hardware counters, on-chip power monitoring facilities and assembly micro-benchmarks. [ABSTRACT FROM PUBLISHER]

Driven by the development of the mobile Internet and the emergence of heterogeneous mobile devices, context-aware applications are attracting growing interest. Based on the idea of transparent computing, the authors propose a novel context-aware service provision (CASP) architecture to transparently and actively provide suitable services to clients. In this article, they present the system model and describe a series of key technologies in CASP, including the client parameter acquisition scheme, the user behavior analysis approach, the service choice algorithm, and the transmission optimization method. Based on the established architecture, they developed software and ran a performance evaluation, proving that CASP offers a good user experience and outstanding generality. [ABSTRACT FROM PUBLISHER]

In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user’s motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% ~ 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency. [ABSTRACT FROM PUBLISHER]

Efficient runtime resource allocation is critical to the overall performance and energy consumption of many-core systems. A region of free cores is allocated for each newly launched application. The cores are deallocated when the corresponding applications finish execution. The frequent allocations and deallocations of the cores might leave free cores scattered (not forming a contiguous region). This situation is referred to as fragmentation. Fragmentation could cause the inefficient mapping of the incoming applications, i.e., long communication distance between communicating cores. This further leads to poor performance and high energy consumption. In this paper, we propose a runtime defragmentation scheme that collects and reallocates the scattered cores in close proximity. We first define a fragmentation metric that is able to evaluate the scatteredness level of the free cores. Based on this, the proposed algorithm is executed to bring the scattered free cores together when the fragmentation metric is over a certain predefined threshold. In this way, the contiguous free core region is formed to facilitate the efficient mapping of the incoming applications. Moreover, the proposed algorithm also aims to minimize the negative impact on the performance of existing applications. Experimental results show that the proposed defragmentation scheme reduces the overall execution time and the energy consumption by 42% and 41%, respectively, when it is augmented to existing runtime mapping algorithms. Moreover, a negligible overhead, accounting for only less than 2.6% of the overall execution time, is required for the proposed defragmentation process. The proposed defragmentation scheme is an effective resource management enhancement to existing runtime mapping algorithms for many-core systems. [ABSTRACT FROM AUTHOR]

This paper studies robust resource-allocation algorithm design for a multiuser multiple-input–single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system that provides wireless unicast secure layered video information to multiple single-antenna secondary receivers. The resource-allocation algorithm design is formulated as a nonconvex optimization problem for the minimization of the total transmit power at the secondary transmitter. The proposed framework takes into account a quality-of-service (QoS) requirement regarding video communication secrecy in the secondary system, the imperfection of the channel state information (CSI) of potential eavesdroppers (primary receivers) at the secondary transmitter, and a limit for the maximum tolerable received interference power at the primary receivers. Thereby, the proposed problem formulation exploits the self-protecting architecture of layered transmission and artificial noise generation to ensure communication secrecy. The considered nonconvex optimization problem is recast as a convex optimization problem via semi-definite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. In addition, two suboptimal resource-allocation schemes are proposed for the case when the solution of the dual problem is unavailable for constructing the optimal solution. Simulation results demonstrate significant transmit power savings and robustness against CSI imperfection for the proposed optimal and suboptimal resource-allocation algorithms employing layered transmission compared to baseline schemes employing traditional single-layer transmission. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of dynamic spectrum access for small-cell networks using a graphical game approach. Compared with existing studies, we take the features of different cell loads and local interference relationship into account. It is proven that the formulated spectrum access game is an exact potential game (EPG) with the aggregate interference level as the potential function, and the Nash equilibrium (NE) of the game corresponds to the global or local optima of the original optimization problem. A lower bound of the achievable aggregate interference level is rigorously derived. Finally, we propose an autonomous best response (BR) learning algorithm to converge toward its NE. It is shown that the proposed game-theoretic solution converges rapidly, and its achievable performance is close to the optimum solution. [ABSTRACT FROM PUBLISHER]

Background completion for consumer videos captured by free-moving cameras is a challenging problem. In this paper, we present a new approach to complete the holes left by removing objects with motion-guided pixels assignment optimization. We first estimate the motion field in the holes by applying a two-step motion propagation method. Then, using estimated motion field as guidance, the missing parts of the video are completed by performing pixels assignment optimization based on the Markov random field, which optimally assigns available pixels from other neighboring video frames to the missing regions. Finally, we present an illumination-adjusting approach to eliminate the illumination inconsistency in the completed holes. We validate our method on a variety of videos captured by free-moving cameras. Compared with previous methods, our method works better to keep the completed background spatiotemporally coherent, to complete video background with much depth discontinuity and to make the illumination consistent in the completed region. [ABSTRACT FROM PUBLISHER]

We consider the problem of recovering a high-resolution image from a pair consisting of a complete low-resolution image and a high-resolution but incomplete one. We refer to this task as the image zoom completion problem. After discussing possible contexts in which this setting may arise, we introduce a nonlocal regularization strategy, giving full details concerning the numerical optimization of the corresponding energy and discussing its benefits and shortcomings. We also derive two total variation-based algorithms and evaluate the performance of the proposed methods on a set of natural and textured images. We compare the results and get with those obtained with two recent state-of-the-art single-image super-resolution algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents the extension of framework for automatic design space exploration (FADSE) tool using a meta-optimization approach, which is used to improve the performance of design space exploration algorithms, by driving two different multiobjective meta-heuristics concurrently. More precisely, we selected two genetic multiobjective algorithms: 1) non-dominated sorting genetic algorithm-II and 2) strength Pareto evolutionary algorithm 2, that work together in order to improve both the solutions’ quality and the convergence speed. With the proposed improvements, we ran FADSE in order to optimize the hardware parameters’ values of the grid ALU processor (GAP) micro-architecture from a bi-objective point of view (performance and hardware complexity). Using our developed approach we obtained better GAP instances (a configuration has for almost the same cycles per instruction – 1.00, the hardware complexity 38% smaller/better – 35.81 versus 58.61) in half of the time compared to a classical sequential optimization approach (5 days versus 10 days). [ABSTRACT FROM PUBLISHER]

Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers’ requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50 percent access latency reduction, compared with the model in $\mu$ LibCloud. For complex requirements, Triones can improve fault-tolerance level by 2 $\times$ and reduce access latency and vendor lock-in level by 30$\sim$ 70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia. [ABSTRACT FROM PUBLISHER]

Storage system efficiency can be significantly improved by determining the value of data. A key concept is cognitive storage, or optimizing storage systems by better comprehending the relevance of data to user needs and preferences. The Web extra at https://youtu.be/P-ZxlTLwzTI is a video of authors Giovanni Cherubini and Vinodh Venkatesan of IBM Research--Zurich discussing the concepts, applications, and benefits of cognitive storage for big data. [ABSTRACT FROM AUTHOR]

Load balancing of large distributed server systems is a complex optimization problem of critical importance in cloud systems and data centers. Existing schedulers often incur a high communication overhead when collecting the data required to make scheduling decisions, hence delaying job requests on their way to the executing servers. We propose a novel scheme that incurs no communication overhead between the users and the servers upon job arrival, thus removing any scheduling overhead from the job's critical path. Our approach is based on creating several replicas of each job and sending each replica to a different server. Upon the arrival of a replica to the head of the queue at its server, the latter signals the servers holding replicas of that job, so as to remove them from their queues. We show, through analysis and simulations, that this scheme significantly improves the expected queuing overhead over traditional schemes under various load conditions and different job length distributions. In addition, we show that our scheme remains efficient even when the inter-server signal propagation delay is significant (relative to the job's execution time). We provide a heuristic solution to the performance degradation that occurs in such cases and show, by simulations, that it efficiently mitigates the detrimental effect of propagation delays. [ABSTRACT FROM AUTHOR]

Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks. [ABSTRACT FROM AUTHOR]

This paper proposes a natural extension of conditional functional dependencies (cfds   [1]) and conditional inclusion dependencies (cinds   [2]), denoted by cfd^p s and cind $^p$ s, respectively, by specifying patterns of data values with $\ne , <, \le , >$, and $\ge$ predicates. As data quality rules, cfd^p s and cind $^p$ s are able to capture errors that commonly arise in practice but cannot be detected by cfds and cinds. We establish two sets of results for central technical problems associated with cfd ^p s and cind$^p$ s. (a) One concerns the satisfiability and implication problems for cfd^p s and cind $^p$ s, taken separately or together. These are important for, e.g. deciding whether data quality rules are dirty themselves, and for removing redundant rules. We show that despite the increased expressive power, the static analyses of cfd^p s and cinds retain the same complexity as their cfds and cinds counterparts. (b) The other concerns validation of cfd ^p s and cind$^p$ s. We show that given a set $\Sigma$ of cfd^p s and cind $^p$ s on a database $D$ , a set of sql queries can be automatically generated that, when evaluated against $D$ , return all tuples in $D$ that violate some dependencies in $\Sigma$ . We also experimentally verified the efficiency ... [ABSTRACT FROM PUBLISHER]

An effective and efficient successful-parent-selecting framework is proposed to improve the performance of differential evolution (DE) by providing an alternative for the selection of parents during mutation and crossover. The proposed method adapts the selection of parents by storing successful solutions into an archive, and the parents are selected from the archive when a solution is continuously not updated for an unacceptable amount of time. The proposed framework provides more promising solutions to guide the evolution and effectively helps DE escaping the situation of stagnation. The simulation results show that the proposed framework significantly improves the performance of two original DEs and six state-of-the-art algorithms in four real-world optimization problems and 30 benchmark functions. [ABSTRACT FROM PUBLISHER]

This paper presents a multicore and multichannel (MCMC) technology and a synchronous and forward-backward scheduling (SFBS) for the cost reduction of sequential minimal optimization trainable pattern classifier. The MCMC technology uses multiple processing cores that are self-reconfigurable and preconfigurable. For different functions, five self-configurable modes and four preconfigurable modes can be combined to achieve high flexibility. A multichannel hierarchical architecture enables different transfer rates. To minimize communication cost, the SFBS uses synchronous and forward-backward counting for data scheduling. For implementation in reconfigurable FPGAs, MCMC and SFBS are combined for use in synthesis, placement, and routing. Compared with the baseline design, the emulation results show that the proposed architecture has a low area and low power costs (5755 logic elements and 195 mW), respectively. The experimental results confirm the cost improvement achieved by the proposed architecture and methods. [ABSTRACT FROM PUBLISHER]

As SOA gains more traction through various implementations, building reliable service compositions remains one of the principal research concerns. Widely researched reliability assurance methods, often rely on applying redundancy or complex optimization strategies that can make them less applicable when it comes to designing service compositions on a larger scale. To address this issue, we propose a design time reliability improvement method that enables selective service composition improvements by focusing on the most reliability-critical workflow components, named weak points. With the aim of detecting most significant weak points, we introduce a method based on a suite of recommendation algorithms that leverage a belief network reliability model. The method is made scalable by using heuristic algorithms that achieve better computational performance at the cost of recommendation accuracy. Although less accurate heuristic algorithms on average require more improvement steps, they can achieve better overall performance in cases when the additional step-wise overhead of applying improvements is low. We confirm the soundness of the proposed solution by performing experiments on data sets of randomly generated service compositions. [ABSTRACT FROM AUTHOR]

This paper presents a method for simultaneous segmentation and modeling of objects, detected in range data gathered by a laser scanner mounted onboard ground-robotic platforms. Superquadrics are used as model for both segmentation and object shape fitting. The proposed method, which we name Simultaneous Segmentation and Superquadrics Fitting, relies on a novel global objective function that accounts for the size of the object and the distance of range points, and for partial occlusions. Results on experimental 2-D range data, which are collected from indoor and outdoor environments, are qualitatively and quantitatively analyzed. Results are compared with those from popular and state-of-the-art segmentation methods. Moreover, we present results on 3-D data obtained from an in-house setup and also from a Velodyne LIDAR. This paper finds applications in areas of mobile robotics and autonomous vehicles, namely object detection, segmentation, and modeling. [ABSTRACT FROM PUBLISHER]

The performance of database/web-service backed applications can be significantly improved by asynchronous submission of queries/requests well ahead of the point where the results are needed, so that results are likely to have been fetched already when they are actually needed. However, manually writing applications to exploit asynchronous query submission is tedious and error-prone. In this paper, we address the issue of automatically transforming a program written assuming synchronous query submission, to one that exploits asynchronous query submission. Our program transformation method is based on data flow analysis and is framed as a set of transformation examples. Our examples can handle query executions within loops, unlike some of the earlier work in this area. We also present a novel approach that, at runtime, can combine multiple asynchronous requests into batches, thereby achieving the benefits of batching in addition to that of asynchronous submission. We have built a tool that implements our transformation techniques on Java programs that use JDBC calls; our tool can be extended to handle Web service calls. We have carried out a detailed experimental study on several real-life applications, which shows the effectiveness of the proposed rewrite techniques, both in terms of their applicability and the performance gains achieved. [ABSTRACT FROM AUTHOR]

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science. [ABSTRACT FROM PUBLISHER]

We present a new method for optimizing the source code of embedded control software with the objective of minimizing implementation errors in the linear fixed-point arithmetic computations caused by overflow, underflow, and truncation. Our method relies on the use of the satisfiability modulo theory (SMT) solver to search for alternative implementations that are mathematically equivalent but require a smaller bit-width, or implementations that use the same bit-width but have a larger error-free dynamic range. Our systematic search of the bounded implementation space is based on a new inductive synthesis procedure, which is guaranteed to find a valid solution as long as such solution exists. Furthermore, we propose an incremental optimization procedure, which applies the synthesis procedure only to small code regions—one at a time—as opposed to the entire program, which is crucial for scaling the method up to programs of realistic size and complexity. We have implemented our new method in a software tool based on the Clang/LLVM compiler frontend and the Yices SMT solver. Our experiments, conducted on a set of representative benchmarks from embedded control and digital signal processing applications, show that the method is both effective and efficient in optimizing arithmetic computations in embedded software code. [ABSTRACT FROM AUTHOR]