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Parallel and Distributed Systems, IEEE Transactions on

Issue 1 • Date Jan. 2014

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Displaying Results 1 - 25 of 29
  • State of the Journal

    Publication Year: 2014 , Page(s): 1
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    Freely Available from IEEE
  • A Framework for Amazon EC2 Bidding Strategy under SLA Constraints

    Publication Year: 2014 , Page(s): 2 - 11
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    With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and, thus, control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability tradeoffs under this scheme are of great value for users seeking to reduce their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies under several service-level agreement (SLA) constraints. In particular, we aim to minimize the monetary cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance price traces and workload models, we compare several adaptive checkpointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence. View full abstract»

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  • A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers

    Publication Year: 2014 , Page(s): 12 - 21
    Cited by:  Papers (3)
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    This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices. View full abstract»

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  • A Probabilistic Misbehavior Detection Scheme toward Efficient Trust Establishment in Delay-Tolerant Networks

    Publication Year: 2014 , Page(s): 22 - 32
    Cited by:  Papers (5)
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    Malicious and selfish behaviors represent a serious threat against routing in delay/disruption tolerant networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN is regarded as a great challenge. In this paper, we propose iTrust, a probabilistic misbehavior detection scheme, for secure DTN routing toward efficient trust establishment. The basic idea of iTrust is introducing a periodically available Trusted Authority (TA) to judge the node's behavior based on the collected routing evidences and probabilistically checking. We model iTrust as the inspection game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a node's reputation, which allows a dynamic detection probability determined by the trust of the users. The extensive analysis and simulation results demonstrate the effectiveness and efficiency of the proposed scheme. View full abstract»

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  • A Tag Encoding Scheme against Pollution Attack to Linear Network Coding

    Publication Year: 2014 , Page(s): 33 - 42
    Cited by:  Papers (1)
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    Network coding allows intermediate nodes to encode data packets to improve network throughput and robustness. However, it increases the propagation speed of polluted data packets if a malicious node injects fake data packets into the network, which degrades the bandwidth efficiency greatly and leads to incorrect decoding at sinks. In this paper, insights on new mathematical relations in linear network coding are presented and a key predistribution-based tag encoding scheme KEPTE is proposed, which enables all intermediate nodes and sinks to detect the correctness of the received data packets. Furthermore, the security of KEPTE with regard to pollution attack and tag pollution attack is quantitatively analyzed. The performance of KEPTE is competitive in terms of: low computational complexity; the ability that all intermediate nodes and sinks detect pollution attack; the ability that all intermediate nodes and sinks detect tag pollution attack; and high fault-tolerance ability. To the best of our knowledge, the existing key predistribution-based schemes aiming at pollution detection can only achieve at most three points as described above. Finally, discussions on the application of KEPTE to practical network coding are also presented. View full abstract»

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  • Advances in Multi-GPU Smoothed Particle Hydrodynamics Simulations

    Publication Year: 2014 , Page(s): 43 - 52
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    We present a multi-GPU version of GPUSPH, a CUDA implementation of fluid-dynamics models based on the smoothed particle hydrodynamics (SPH) numerical method. The SPH is a well-known Lagrangian model for the simulation of free-surface fluid flows; it exposes a high degree of parallelism and has already been successfully ported to GPU. We extend the GPU-based simulator to run simulations on multiple GPUs simultaneously, to obtain a gain in speed and overcome the memory limitations of using a single device. The computational domain is spatially split with minimal overlapping and shared volume slices are updated at every iteration of the simulation. Data transfers are asynchronous with computations, thus completely covering the overhead introduced by slice exchange. A simple yet effective load balancing policy preserves the performance in case of unbalanced simulations due to asymmetric fluid topologies. The obtained speedup factor (up to 4.5x for 6 GPUs) closely follows the expected one (5x for 6 GPUs) and it is possible to run simulations with a higher number of particles than would fit on a single device. We use the Karp-Flatt metric to formally estimate the overall efficiency of the parallelization. View full abstract»

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  • Behavioral Malware Detection in Delay Tolerant Networks

    Publication Year: 2014 , Page(s): 53 - 63
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    The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs ("insufficient evidence versus evidence collection risk" and "filtering false evidence sequentially and distributedly"), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of "malicious nodes sharing false evidence." Real mobile network traces are used to verify the effectiveness of the proposed methods. View full abstract»

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  • Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

    Publication Year: 2014 , Page(s): 63 - 72
    Cited by:  Papers (2)
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    Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical codesign approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates 1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and 2) an energy-efficient, multilevel computing architecture specifically designed to leverage the multiresolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a simulated truss structure and a real full-scale truss structure demonstrate the system's efficacy in damage localization and energy efficiency. View full abstract»

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  • Enabling P2P One-View Multiparty Video Conferencing

    Publication Year: 2014 , Page(s): 73 - 82
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    Multiparty video conferencing (MPVC) facilitates real-time group interaction between users. While P2P is a natural delivery solution for MPVC, a peer often does not have enough bandwidth to deliver her video to all other peers in the conference. Recently, we have witnessed the popularity of one-view MPVC, where each user only watches full video of another user. One-view MPVC opens up the design space for P2P delivery. In this paper, we explore the feasibility of a pure P2P solution for one-view MPVC. We characterize the video source rate region achievable through video relays between peers. For both homogeneous and heterogeneous MPVC systems, we establish tight universal video rate lower bounds that are independent of the number of peers, the number of video sources, and the specific viewing relations between peers. We further propose, P2P video relay designs to approach the maximal video rate region. Through numerical simulations, we verified that the derived lower bounds are indeed tight bounds, and the proposed bandwidth allocation algorithm can achieve a close-to-optimal peer upload bandwidth utilization. Our results demonstrate that P2P is a promising solution for one-view MPVC. Insights obtained from our study can be used to guide the design of P2P MPVC systems. View full abstract»

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  • Extending the Performance and Energy-Efficiency of Shared Memory Multicores with Nanophotonic Technology

    Publication Year: 2014 , Page(s): 83 - 92
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    As the number of cores increases exponentially on a single chip, the design and integration of both the on-chip network facilitating intercore communication, and the cache coherence protocol for enabling shared memory programming have become critical for improved energy-efficiency and overall chip performance. With traditional metal interconnects facing stringent energy constraints, researchers are currently pursuing disruptive solutions such as nanophotonics for improved energy-efficiency. Cache coherence in multicores can be enforced effectively by snoopy protocols; however, broadcasting every cache miss can limit the scalability while consuming excess energy. In this paper, we propose PULSE, a nanophotonic broadcast tree-based network for snoopy cache coherent multicores. To limit the energy-penalty from broadcasting (and thereby splitting) optical signals, we direct the optical signal from the external laser such that only the subset of requesters can receive the optical signal. Furthermore, as cache blocks are shared by a few cores, we propose a multicast version of PULSE called multi-PULSE that predicts the sharers' for each L2 miss and morphing the broadcast to a multicast network. We evaluate the energy and performance using CACTI and SIMICS on 16-core and 64-core versions of PULSE and multi-PULSE for Splash-2, PARSEC, and SPEC CPU2006 benchmarks and compare to electrical networks, optical networks, and another cache filtering techniques. Our results indicate that PULSE outperforms competitive electrical/optical networks by 60 percent in terms of execution time, and multi-PULSE reduces average energy from 10 to 80 percent even with a few mispredictions. View full abstract»

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  • Fast Bloom Filters and Their Generalization

    Publication Year: 2014 , Page(s): 93 - 103
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    Bloom filters have been extensively applied in many network functions. Their performance is judged by three criteria: query overhead, space requirement, and false positive ratio. Due to wide applicability, any improvement to the performance of Bloom filters can potentially have a broad impact in many areas of networking research. In this paper, we study Bloom-1, a data structure that performs membership check in one memory access, which compares favorably with the k memory accesses of a standard Bloom filter. We also generalize Bloom-1 to Bloom-g and Bloom-Q, allowing performance tradeoff between membership query overhead and false positive ratio. We thoroughly examine the variants in this family of filters, and show that they can be configured to outperform the Bloom filters with a smaller number of memory accesses, a smaller or equal number of hash bits, and a smaller or comparable false positive ratio in practical scenarios. We also perform experiments based on a real traffic trace to support our filter design. View full abstract»

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  • GMRace: Detecting Data Races in GPU Programs via a Low-Overhead Scheme

    Publication Year: 2014 , Page(s): 104 - 115
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    In recent years, GPUs have emerged as an extremely cost-effective means for achieving high performance. While languages like CUDA and OpenCL have eased GPU programming for nongraphical applications, they are still explicitly parallel languages. All parallel programmers, particularly the novices, need tools that can help ensuring the correctness of their programs. Like any multithreaded environment, data races on GPUs can severely affect the program reliability. In this paper, we propose GMRace, a new mechanism for detecting races in GPU programs. GMRace combines static analysis with a carefully designed dynamic checker for logging and analyzing information at runtime. Our design utilizes GPUs memory hierarchy to log runtime data accesses efficiently. To improve the performance, GMRace leverages static analysis to reduce the number of statements that need to be instrumented. Additionally, by exploiting the knowledge of thread scheduling and the execution model in the underlying GPUs, GMRace can accurately detect data races with no false positives reported. Our experimental results show that comparing to previous approaches, GMRace is more effective in detecting races in the evaluated cases, and incurs much less runtime and space overhead. View full abstract»

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  • High-Level Strategies for Parallel Shared-Memory Sparse Matrix-Vector Multiplication

    Publication Year: 2014 , Page(s): 116 - 125
    Cited by:  Papers (1)
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    The sparse matrix-vector multiplication is an important computational kernel, but is hard to efficiently execute even in the sequential case. The problems--namely low arithmetic intensity, inefficient cache use, and limited memory bandwidth--are magnified as the core count on shared-memory parallel architectures increases. Existing techniques are discussed in detail, and categorized chiefly based on their distribution types. Based on this, new parallelization techniques are proposed. The theoretical scalability and memory usage of the various strategies are analyzed, and experiments on multiple NUMA architectures confirm the validity of the results. One of the newly proposed methods attains the best average result in experiments on a large set of matrices. In one of the experiments it obtains a parallel efficiency of 90 percent, while on average it performs close to 60 percent. View full abstract»

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  • High-Performance Publish-Subscribe Matching Using Parallel Hardware

    Publication Year: 2014 , Page(s): 126 - 135
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    Matching incoming event notifications against received subscriptions are a fundamental part of every publish-subscribe infrastructure. In the case of content-based systems this is a fairly complex and time consuming task, whose performance impacts that of the entire system. In the past, several algorithms have been proposed for efficient content-based event matching. While they differ in most aspects, they have in common the fact of being conceived to run on conventional, sequential hardware. On the other hand, parallel hardware is becoming available off-the-shelf: the number of cores inside CPUs is constantly increasing, and CUDA makes it possible to access the power of GPU hardware for general purpose computing. In this paper, we describe a new publish-subscribe content-based matching algorithm designed to run efficiently both on multicore CPUs and CUDA GPUs. A detailed comparison with two state-of-the-art sequential matching algorithms demonstrates how the use of parallel hardware can bring impressive speedups in content-based matching. At the same time, our analysis identifies the characteristic aspects of multicore and CUDA programming that mostly impact performance. View full abstract»

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  • IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks

    Publication Year: 2014 , Page(s): 136 - 145
    Cited by:  Papers (1)
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    Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks. View full abstract»

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  • Localization-Oriented Network Adjustment in Wireless Ad Hoc and Sensor Networks

    Publication Year: 2014 , Page(s): 146 - 155
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    Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes. View full abstract»

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  • Modeling of Distributed File Systems for Practical Performance Analysis

    Publication Year: 2014 , Page(s): 156 - 166
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    Cloud computing has received significant attention recently. Delivering quality guaranteed services in clouds is highly desired. Distributed file systems (DFSs) are the key component of any cloud-scale data processing middleware. Evaluating the performance of DFSs is accordingly very important. To avoid cost for late life cycle performance fixes and architectural redesign, providing performance analysis before the deployment of DFSs is also particularly important. In this paper, we propose a systematic and practical performance analysis framework, driven by architecture and design models for defining the structure and behavior of typical master/slave DFSs. We put forward a configuration guideline for specifications of configuration alternatives of such DFSs, and a practical approach for both qualitatively and quantitatively performance analysis of DFSs with various configuration settings in a systematic way. What distinguish our approach from others is that 1) most of existing works rely on performance measurements under a variety of workloads/strategies, comparing with other DFSs or running application programs, but our approach is based on architecture and design level models and systematically derived performance models; 2) our approach is able to both qualitatively and quantitatively evaluate the performance of DFSs; and 3) our approach not only can evaluate the overall performance of a DFS but also its components and individual steps. We demonstrate the effectiveness of our approach by evaluating Hadoop distributed file system (HDFS). A series of real-world experiments on EC2 (Amazon Elastic Compute Cloud), Tansuo and Inspur Clusters, were conducted to qualitatively evaluate the effectiveness of our approach. We also performed a set of experiments of HDFS on EC2 to quantitatively analyze the performance and limitation of the metadata server of DFSs. Results show that our approach can achieve sufficient performance analysis. Similarly, the proposed approach cou- d be also applied to evaluate other DFSs such as MooseFS, GFS, and zFS. View full abstract»

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  • Network Coding Aware Cooperative MAC Protocol for Wireless Ad Hoc Networks

    Publication Year: 2014 , Page(s): 167 - 179
    Cited by:  Papers (1)
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    Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. Network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless ad hoc networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay. Simulation results reveal that NCAC-MAC can improve the network performance under general circumstances comparing with two benchmarks. View full abstract»

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  • On the Feasibility of Gradient-Based Data-Centric Routing Using Bloom Filters

    Publication Year: 2014 , Page(s): 180 - 190
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    Gradient-based routing using Bloom filters is an effective mechanism to enable data-centric queries in multihop networks. A node compressively describes its data items as a Bloom filter, which is then diffused away to the other nodes with information decay. The Bloom filters form an information potential that eventually navigates queries to the source node by ascending the potential field. The existing designs of Bloom filters, however, have critical limitations with respect to the feasibility of gradient-based routing. The compressed routing entries appear to be noisy. Noise in unrelated routing entries is very likely to equal to even outweigh information in right routing entries, thus blinding a query to its desired destination. This work addresses the root cause of the mismatch between the ideal and the practical performance of gradient-based routing using Bloom filters. We first investigate the impact of decaying model on the effectiveness of routing entries, and then evaluate the negative impact of noise on routing decisions. Based on such analytical results, we derive the necessary and sufficient condition of feasible gradient-based routing using Bloom filters. Accordingly, we propose a receiver-oriented design of Bloom filters, called Wader, which satisfies the necessary and sufficient condition. The evaluation results demonstrate that Wader guarantees the correctness and efficiency of gradient-based routing with high probability. View full abstract»

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  • On the Throughput of Two-Way Relay Networks Using Network Coding

    Publication Year: 2014 , Page(s): 191 - 199
    Cited by:  Papers (2)
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    Network coding has shown the promise of significant throughput improvement. In this paper, we study the network throughput using network coding and explore how the maximum throughput can be achieved in a two-way relay wireless network. Unlike previous studies, we consider a more general network with arbitrary structure of overhearing status between receivers and transmitters. To efficiently utilize the coding opportunities, we invent the concept of network coding cliques (NCCs), upon which a formal analysis on the network throughput using network coding is elaborated. In particular, we derive the closed-form expression of the network throughput under certain traffic load in a slotted ALOHA network with basic medium access control. Furthermore, the maximum throughput as well as optimal medium access probability at each node is studied under various network settings. Our theoretical findings have been validated by simulation as well. View full abstract»

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  • Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads

    Publication Year: 2014 , Page(s): 200 - 211
    Cited by:  Papers (4)
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    This paper considers a stochastic optimization approach for job scheduling and server management in large-scale, geographically distributed data centers. Randomly arriving jobs are routed to a choice of servers. The number of active servers depends on server activation decisions that are updated at a slow time scale, and the service rates of the servers are controlled by power scaling decisions that are made at a faster time scale. We develop a two-time-scale decision strategy that offers provable power cost and delay guarantees. The performance and robustness of the approach is illustrated through simulations. View full abstract»

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  • Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing

    Publication Year: 2014 , Page(s): 212 - 221
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    To improve the accuracy of learning result, in practice multiple parties may collaborate through conducting joint Back-Propagation neural network learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN "doubly homomorphic" encryption algorithm for the multiparty setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient, and accurate. View full abstract»

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  • Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data

    Publication Year: 2014 , Page(s): 222 - 233
    Cited by:  Papers (8)
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    With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of "coordinate matching," i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication. View full abstract»

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  • Surface Coverage in Sensor Networks

    Publication Year: 2014 , Page(s): 234 - 243
    Cited by:  Papers (4)
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    Coverage is a fundamental problem in wireless sensor networks (WSNs). Conventional studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a field of interest (FoI) is complex in many real-world applications. However, existing coverage studies do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the field of interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. Thus, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, what is the expected coverage ratio when a number of sensors are adopted? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct extensive simulations to evaluate the performance of the proposed algorithms. View full abstract»

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  • Time Synchronization Based on Slow-Flooding in Wireless Sensor Networks

    Publication Year: 2014 , Page(s): 244 - 253
    Cited by:  Papers (2)
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    The accurate and efficient operation of many applications and protocols in wireless sensor networks require synchronized notion of time. To achieve network-wide time synchronization, a common strategy is to flood current time information of a reference node into the network, which is utilized by the de facto time-synchronization protocol Flooding Time-Synchronization Protocol (FTSP). In FTSP, the propagation speed of the flood is slow because each node waits for a given period of time to propagate its time information about the reference node. It has been shown that slow-flooding decreases the synchronization accuracy and scalability of FTSP drastically. Alternatively, rapid-flooding approach is proposed in the literature, which allows nodes to propagate time information as quickly as possible. However, rapid flooding is difficult and has several drawbacks in wireless sensor networks. In this paper, our aim is to reduce the undesired effect of slow-flooding on the synchronization accuracy without changing the propagation speed of the flood. Within this context, we realize that the smaller the difference between the speeds of the clocks, the smaller the undesired effect of waiting times on the synchronization accuracy. In the light of this realization, our main contribution is to show that the synchronization accuracy and scalability of slow-flooding can drastically be improved by employing a clock speed agreement algorithm among the sensor nodes. We present an evaluation of this strategy on a testbed setup including 20 MICAz sensor nodes. Our theoretical findings and experimental results show that employing a clock speed agreement algorithm among the sensor nodes drastically improves the synchronization accuracy and scalability of slow-flooding. View full abstract»

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IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers.

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David Bader
College of Computing
Georgia Institute of Technology