Optimal Overhead Research Articles

Secure partial encryption with adversarial functional dependency constraints in the database-as-a-service model

Cloud computing enables end-users to outsource their dataset and data management needs to a third-party service provider. One of the major security concerns of the outsourcing paradigm is how to protect sensitive information in the outsourced dataset. In some applications, only partial values are considered sensitive. In general, the sensitive information can be protected by encryption. However, data dependency constraints (together with the unencrypted data) in the outsourced data may serve as adversary knowledge and bring security vulnerabilities to the encrypted data. In this paper, we focus on functional dependency (FD), an important type of data dependency constraints, and study the security threats by the adversarial FDs. We design a practical scheme that can defend against the FD attack by encrypting a small amount of non-sensitive data (encryption overhead). We prove that finding the scheme that leads to the optimal encryption overhead is NP-complete, and design efficient heuristic algorithms, under the presence of one or multiple FDs. We design a secure query rewriting scheme that enables the service provider to answer various types of queries on the encrypted data with provable security guarantee. We extend our study to enforce security when there are conditional functional dependencies (CFDs) and data updates. We conduct an extensive set of experiments on two real-world datasets. The experiment results show that our heuristic approach brings small amounts of encryption overhead (at most 1% more than the optimal overhead), and enjoys a 10-time speedup compared with the optimal solution. Besides, our approach can reduce up to 90% of the encryption overhead of state-of-the-art solution.

Packet Loss Minimizing Approach Based on Traffic Prediction for Multi-streaming Communication over MANET

Wireless networks are extensively used for communication. Most of the devices that we use for communication are equipped with the wireless network interface and are capable of streaming data efficiently to the device within the communication range. Stream Control Transmission Protocol (SCTP) is designed for multi-streaming service. In this paper, a Packet Loss Minimising Approach (PLMA-SCTP) based on traffic prediction for multi-streaming application is proposed for the mobile ad hoc networks (MANETs) environment. The objective is to reduce data loss and delay in multi-streaming communication. The PLMA-SCTP method aims to provide an efficient route with optimal overhead and delay. Our simulation studies prove that PLMA-SCTP improves the various performance metrics of multi-streaming applications.

Leakage Models and Inference Attacks on Searchable Encryption for Cyber-Physical Social Systems

Searchable encryption (SE) schemes, such as those deployed for cyber-physical social systems, may be vulnerable to inference attacks. In such attacks, attackers seek to learn sensitive information about the queries and data stored on the (cyber-physical social) systems. However, these attacks are often based on strong (impractical) assumptions (e.g., the complete knowledge of documents or known document injection) using access-pattern leakage. In this paper, we first identify different leakage models based on the leakage profiles of common SE schemes, and then design inference methods accordingly. In particular, based on the leakage models, we show that some information leakage allows a very powerful attack with little prior knowledge. We then propose new inference attacks in which an adversary only needs to have a partial knowledge of target documents. Unlike previous attacks, the proposed inference algorithms perform effective document-mapping attacks before query recovery attacks, in the sense that they are more efficient and scalable without requiring optimization overheads. We then use experiments to demonstrate their effectiveness.

QoS-data rate optimization for fast fading interference alignment X network

Quality of service (QoS) and high data rate are two main parameters that drive the research in wireless communication. Perfect Channel State Information (CSI) at both transmitter and receiver sides is an important condition for achieving QoS and high data rate. But, achieving perfect CSI in mobile transmitters/receivers under fast fading is challenging. That is, frequent CSI updating is required to satisfy QoS requirements under fast fading scenario.In this paper, we consider a multiple transmitter-receiver system, called X network, that has data flow in all the channels. Interference alignment (IA) is a practical solution to achieve maximum capacity in an X network. The present contribution explores the performance of the system with a mobile receiver (or transmitter) that employs CSI estimation using pilot symbols, and further optimize the performance so as to cater both bit error rate (BER) and data rate requirements. Using Monte-Carlo simulations, a novel algorithm is proposed to calculate optimal pilot overhead in M×2 X network or 2×M X network by setting an upper bound on BER. With simulation results we show that the optimal overhead varies with Doppler frequency and Signal-to-Noise Ratio (SNR).

Downsampling scattering parameters for rendering anisotropic media

Volumetric micro-appearance models have provided remarkably high-quality renderings, but are highly data intensive and usually require tens of gigabytes in storage. When an object is viewed from a distance, the highest level of detail offered by these models is usually unnecessary, but traditional linear downsampling weakens the object's intrinsic shadowing structures and can yield poor accuracy. We introduce a joint optimization of single-scattering albedos and phase functions to accurately downsample heterogeneous and anisotropic media. Our method is built upon scaled phase functions , a new representation combining abledos and (standard) phase functions. We also show that modularity can be exploited to greatly reduce the amortized optimization overhead by allowing multiple synthesized models to share one set of down-sampled parameters. Our optimized parameters generalize well to novel lighting and viewing configurations, and the resulting data sets offer several orders of magnitude storage savings.

Optimization of Pilot Overhead in Communications With ARQ-Feedback

In this letter, we optimize the pilot overhead in communication with retransmission and investigate the dependence of this overhead on various system parameters, e.g., fading rate, target rate, and signal-to-noise ratio. We formalize the optimization and frame it in terms of throughput maximization; this leads to an accurate optimal pilot overhead closed-form expression. Results show that the optimal overhead expressions are roots of quartic polynomials. At a high-power regime, they decrease with the target rate and increase with the normalized Doppler frequency, and at low-power regime, they are only function of the target rate.

Efficient consolidation-aware VCPU scheduling on multicore virtualization platform

Multicore processors are widely used in today’s computer systems. Multicore virtualization technology provides an elastic solution to more efficiently utilize the multicore system. However, the Lock Holder Preemption (LHP) problem in the virtualized multicore systems causes significant CPU cycles wastes, which hurt virtual machine (VM) performance and reduces response latency. The system consolidates more VMs, the LHP problem becomes worse. In this paper, we propose an efficient consolidation-aware vCPU (CVS) scheduling scheme on multicore virtualization platform. Based on vCPU over-commitment rate, the CVS scheduling scheme adaptively selects one algorithm among three vCPU scheduling algorithms: co-scheduling, yield-to-head, and yield-to-tail based on the vCPU over-commitment rate because the actions of vCPU scheduling are split into many single steps such as scheduling vCPUs simultaneously or inserting one vCPU into the run-queue from the head or tail. The CVS scheme can effectively improve VM performance in the low, middle, and high VM consolidation scenarios. Using real-life parallel benchmarks, our experimental results show that the proposed CVS scheme improves the overall system performance while the optimization overhead remains low.

Initializing Bayesian Hyperparameter Optimization via Meta-Learning

Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a subcommunity of machine learning has focused on solving this problem with Sequential Model-based Bayesian Optimization (SMBO), demonstrating substantial successes in many applications. However, for computationally expensive algorithms the overhead of hyperparameter optimization can still be prohibitive. In this paper we mimic a strategy human domain experts use: speed up optimization by starting from promising configurations that performed well on similar datasets. The resulting initialization technique integrates naturally into the generic SMBO framework and can be trivially applied to any SMBO method. To validate our approach, we perform extensive experiments with two established SMBO frameworks (Spearmint and SMAC) with complementary strengths; optimizing two machine learning frameworks on 57 datasets. Our initialization procedure yields mild improvements for low-dimensional hyperparameter optimization and substantially improves the state of the art for the more complex combined algorithm selection and hyperparameter optimization problem.

Efficient and Retargetable Dynamic Binary Translation on Multicores

Dynamic binary translation (DBT) is a core technologyto many important applications such as system virtualization, dynamic binary instrumentation, and security. However, there are several factors that often impede its performance: 1) emulation overhead before translation; 2) translation and optimization overhead; and 3) translated code quality. The issues also include its retargetabilitythat supports guest applications from different instruction-set architectures (ISAs) to host machines also with different ISAs-an important feature to system virtualization. In this work, we take advantage of the ubiquitous multicore platforms, and use a multithreaded approach to implement DBT. By running the translator and the dynamic binary optimizer on different cores with different threads, it could off-load the overhead incurred by DBT on the target applications; thus, afford DBT of more sophisticated optimization techniques as well as its retargetability. Using QEMU (a popular retargetable DBT for system virtualization) and Low-Level Virtual Machine (LLVM) as our building blocks, we demonstrated in a multithreaded DBT prototype, called Hybrid-QEMU (HQEMU), that it could improve QEMU performance by a factor of 2.6x and 4.1x on the SPEC CPU2006 integer and floating point benchmarks, respectively, for dynamic translation of x86 code to run on x86-64 platforms. For ARM codes to x86-64 platforms, HQEMU can gain a factor of 2.5x speedup over QEMU for the SPEC CPU2006 integer benchmarks. We also address the performance scalability issue of multithreaded applications across ISAs. We identify two major impediments to performance scalability in QEMU: 1) coarse-grained locks used to protect shared data structures, and 2) inefficient emulation of atomic instructions across ISAs. We proposed two techniques to mitigate those problems: 1) using indirect branch translation caching (IBTC) to avoid frequent accesses to locks, and 2) using lightweight memory transactions to emulate atomic instructions across ISAs. Our experimental results show that for multithread applications, HQEMU achieves 25X speedups over QEMU for the PARSEC benchmarks.

Evolutionary Optimum Design for a Task Specified 6-Link Planar Robot

Optimal structure design for robots is fundamentally important, as it stands first in the order of robotic system design. The structure of industrial robots should be robust, versatile and strong enough – to withstand a variety of forces/loads, guarantee stability and security in operation. To achieve best solution, it is desirable to perform structure design optimization. Here, it is proposed to determine optimal geometric dimensions for a task specified 6-link planar robot considering pick and place operation. The aim is to minimize torque required for defined motion and to maximize manipulability measure of the robot subject to dynamic, kinematic, deflection and structural constraints. Links physical characteristics (length and cross sectional area parameters) are design variables. Five different cross-sections (hollow circle, hollow square, hollow rectangle, C-channel and I-channel) have been experimented for the link. Evolutionary algorithms viz. Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Differential Evolution (MODE), Multiobjective Genetic Algorithm (MOGA) are used. Normalized weighting objective functions and average fuzzy membership functions are used to select the best optimal solution. Multiobjective performance measures, namely, Solution Spread Measure (SSM) and Ratio of Non-dominated Individuals (RNI) are used to evaluate Pareto optimal fronts. Two other multiobjective performance measures, namely, Optimizer Overhead (OO) and Algorithm effort are used to find computational effort of optimization algorithms. Results obtained from various techniques are compared and analyzed.

Generating signatures with optimal overhead: practical paddings for signature schemes

ABSTRACTOptimal signatures (generating signatures as short as possible), which achieve the optimal bandwidth for communication, are extremely useful in bandwidth‐critical networks. Previous approaches use the random permutations with large block size as building blocks, which incurs less efficient implementations in the real world. Meanwhile, all the practical signature schemes are not optimal in bandwidth including PSS‐R (probabilistic signature scheme with message recovery ), FDH ( Full Domain Hash), and DSA (Digital Signature Algorithm). This paper presents three constructions for optimal signature schemes. All the proposals use both the random oracles and the ideal ciphers with smaller block sizes as building blocks to obtain optimal paddings for signature schemes. The ideal ciphers in our schemes can be implemented by real block ciphers (e.g., AES (Advanced Encryption Standard)‐256). Concrete implementations of these signature schemes can utilize the trapdoor permutations of Rabin and RSA, respectively. Surprisingly, RSA and Rabin (trapdoor permutations) lead to not only optimality in bandwidth but also a tight security. Therefore, besides yielding secure signatures with high efficiency, our proposals can also be flexibly applied to the bandwidth‐limited networks that reduces the communication cost as less as possible. Copyright © 2014 John Wiley & Sons, Ltd.

Design and evaluation of a decentralised information service architecture for IEEE 802.21 networks

The deployment of wireless technologies such as Wi-Fi, WIMAX and LTE brings to the mobile user the opportunity to switch from one network to another, in a seamless manner. To accomplish this, the user must be able to discover which types of network connectivity are available. The IEEE 802.21 standard specifies a media independent information service MIIS providing capabilities for discovering network information within a geographical area. The representation and distribution of mobility information in heterogeneous networks are a big challenge, especially in multi-operator environments. In this paper, we introduce a decentralised information service architecture that improves the user experience and ensures scalability for large system deployments. Additionally, an evaluation using the NS-2 network simulator is presented. The results demonstrate that our proposal outperforms the existing approaches in terms of query delay, users optimal handovers and communication overhead, contributing to a traffic decrease at the operators core network and internet.

The Effect of the Remplissage Procedure on Shoulder Range of Motion: A Cadaveric Study

The purpose of this in vitro biomechanical study was to assess the effects of the remplissage procedure for small- and large-sized Hill-Sachs lesions (HSLs) on shoulder range of motion (ROM) with a special interest in the apprehension position. HSLs of 50% and 100% of the glenoid width were simulated in 7 cadaveric shoulders as small and large lesions, respectively, and the postoperative condition was reproduced by placing suture anchors on the articular surface and tying down the infraspinatus at the medial edge of the would-be lesion site. ROMs were measured in abduction, internal rotation, and external rotation with the humerus in the adducted and abducted position. In addition, the ROM was measured in the anterior apprehension position, in which 2 torques of external rotation and extension were applied simultaneously, and external rotation and horizontal extension ROMs were measured with the humerus in different abduction angles (20°, 40°, and 60°). For standard ROMs, the procedure for the 50% HSL maintained complete ROMs, whereas the procedure for the 100% HSL significantly decreased external rotation ROM with the humerus in both the adducted and abducted positions, as well as abduction ROM. In the apprehension position, remplissage for the 50% HSL decreased extension ROM with the humerus abducted to 40° and 60°. Remplissage for the 100% HSL significantly decreased both external and extension ROMs regardless of the humeral abduction angle. In the cadaveric model with an intact humeral head and the simulated postoperative condition, the remplissage procedure for a large HSL caused significant restrictions in ROM of abduction in the scapular plane and external rotation with the humerus in both adduction and abduction. It also caused significant restrictions in both external rotation and extension ROMs in the apprehension position. The indication for the remplissage procedure for the larger HSL should be considered carefully, especially for the competitive throwing athlete who needs exceptional external rotation ROM for optimal overhead throwing performance.

Offline optimization for user-specific hybrid recommender systems

User-specific hybrid recommender systems aim at harnessing the power of multiple recommendation algorithms in a user-specific hybrid scenario. While research has previously focused on self-learning hybrid configurations, such systems are often too complex to take out of the lab and are seldom tested against real-world requirements. In this work, we describe a self-learning user-specific hybrid recommender system and assess its ability towards meeting a set of pre-defined requirements relevant to online recommendation scenarios: responsiveness, scalability, system transparency and user control. By integrating a client-server architectural design, the system was able to scale across multiple computing nodes in a very flexible way. A specific user-interface for a movie recommendation scenario is proposed to illustrate system transparency and user control possibilities, which integrate directly in the hybrid recommendation process. Finally, experiments were performed focusing both on weak and strong scaling scenarios on a high performance computing environment. Results showed performance to be limited only by the slowest integrated recommendation algorithm with very limited hybrid optimization overhead.

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Number of software applications demands various levels of security at the time of scheduling in Computational Grid. Grid may offer these securities but may result in the performance degradation due to overhead in offering the desired security. Scheduling performance in a Grid is affected by the heterogeneities of security and computational power of resources. Customized Genetic Algorithms have been effectively used for solving complex optimization problems (NP Hard) and various heuristics have been suggested for solving Multi-objective optimization problems. In this paper a security driven, elitist non-dominated sorting genetic algorithm, Optimal Security with Optimal Overhead Scheduling (OSO2S), based on NSGA-II, is proposed. The model considers dual objectives of minimizing the security overhead and maximizing the total security achieved. Simulation results exhibit that the proposed algorithm delivers improved makespan and lesser security overhead in comparison to other such algorithms viz. MinMin, MaxMin, SPMinMin, SPMaxMin and SDSG.

Методы оптимизации Cи/Cи++ - приложений распространяемых в биткоде LLVM с учетом специфики оборудования

This paper analyzes approaches for optimizing C/C++ applications used in twostage compilation system, allowing distributing such applications in the LLVM (low level virtual machine) intermediate representation. The on-stack replacement technique implemented in the LLVM just-in-time compiler is described. The paper presents a static instrumentation technique with incomplete control flow edge covering and its correction to the full control flow profile. The proposed approach allows the derived profile information to be comparable in quality to the classical approach while significantly reducing the profiling overhead. Also, the technique for converting dynamically collected profile to LLVM profile format for statically compiled applications is presented. This paper evaluates both existing optimizations modified for using the collected profile and the newly developed ones. Implemented profile based inlining, outlining, speculative devirtualization. Also implemented annotation of control flow graph by profile weights to make profile based optimizations easy for debugging. Proposed the technique which allows using the prefetch instructions to improve the effectiveness of array processing codes. This paper describes the approach for building a specific cloud application storage that allows solving program optimization and protection problems. Finally, we present the approach for reducing compilation and optimization overhead in the cloud infrastructure.

Optimal Overhead Cost Allocation in an Agency Model with Moral Hazard and Private Information

This paper develops a model of moral hazard and private information to explore the properties of efficient cost allocations. The production function uses two complement inputs: the agent’s effort and a resource supplied by the principal. After contracting, the agent receives non-contractible (private) information related to the productivity of the resource that is relevant to his effort level decision. The optimal cost allocation rate reflects the trade-off between the need to recover overhead costs and the need to provide incentives to the agent. It is shown that the optimal cost allocation charge is lower than the resource cost as a result of the need to compensate the agent for the uncontrollable risk. Further, the allocation rate might be negative or positive, depending on whether or not the effect of recovering overhead costs prevails over the incentive effect. When the agent uses two resources whose productivities are positively and strongly correlated, the optimal resource charge rate is higher than the resource cost. Interestingly, the reason for this is to expose the agent to less controllable risk. The results in this paper explain some empirical evidence related to cost allocation practice and to the relationship between risk and incentives.

A New Scalable Hybrid Routing Protocol for VANETs

Vehicular ad hoc networks (VANETs) are highly mobile wireless networks that are designed to support vehicular safety, traffic monitoring, and other commercial applications. Within VANETs, vehicle mobility will cause the communication links between vehicles to frequently be broken. Such link failures require a direct response from the routing protocols, leading to a potentially excessive increase in the routing overhead and degradation in network scalability. In this paper, we propose a new hybrid location-based routing protocol that is particularly designed to address this issue. Our new protocol combines features of reactive routing with location-based geographic routing in a manner that efficiently uses all the location information available. The protocol is designed to gracefully exit to reactive routing as the location information degrades. We show through analysis and simulation that our protocol is scalable and has an optimal overhead, even in the presence of high location errors. Our protocol provides an enhanced yet pragmatic location-enabled solution that can be deployed in all VANET-type environments.

Background optimization in full system binary translation

Binary translation and dynamic optimization are widely used to provide compatibility between legacy and promising upcoming architectures on the level of executable binary codes. Dynamic optimization is one of the key contributors to dynamic binary translation system performance. At the same time it can be a major source of overhead, both in terms of CPU cycles and whole system latency, as long as optimization time is included in the execution time of the application under translation. One of the solutions that allow to eliminate dynamic optimization overhead is to perform optimization simultaneously with the execution, in a separate thread. In the paper we present implementation of this technique in full system dynamic binary translator. For this purpose, an infrastructure for multithreaded execution was implemented in binary translation system. This allowed running dynamic optimization in a separate thread independently of and concurrently with the main thread of execution of binary codes under translation. Depending on the computational resources available, this is achieved whether by interleaving the two threads on a single processor core or by moving optimization thread to an underutilized processor core. In the first case the latency introduced to the system by a computational intensive dynamic optimization is reduced. In the second case overlapping of execution and optimization threads also results in elimination of optimization time from the total execution time of original binary codes.

A Selective-Relay Based Cooperative Spectrum Sensing Scheme without Dedicated Reporting Channels in Cognitive Radio Networks

Typically, each cooperative spectrum sensing process requires two phases: the primary user's signal detection phase, in which all cognitive users attempt to detect the presence of the primary user within a certain observation window (called signal detection overhead); and the initial detection result reporting phase, in which the cognitive users forward their detection results to a fusion center. To avoid interfering with the primary user in the reporting phase, previous research assumed that there is a common control channel (also known as dedicated reporting channel) between the cognitive users and fusion center, which, however, requires extra channel resources and introduces an additional complexity due to the dedicated channel resource management. In this paper, we propose a selective-relay based cooperative spectrum sensing scheme, which is able to control and reduce the interference from cognitive reporting users to primary user without the dedicated channel. We analyze the interference impact on the primary user and show that the interference induced by the reporting users is controllable and can be reduced to satisfy a given outage probability requirement of the primary transmissions. In addition, we investigate the receiver operating characteristics (ROC) of the traditional cooperative sensing scheme (with dedicated reporting channel) and the proposed scheme (without dedicated reporting channel) by jointly considering the signal detection and reporting phases. It is proven that, given a target detection probability, a unique optimal signal detection overhead exists to minimize an asymptotic overall false alarm probability in high SNR regions. We illustrate that, compared to the traditional scheme, the selective-relay based cooperative sensing scheme can save the dedicated channel resources without sacrificing ROC performance. Numerical results also show that, under a guaranteed overall detection probability, an overall false alarm probability can be minimized through an optimization of the signal detection overhead.