<![CDATA[ Parallel and Distributed Systems, IEEE Transactions on - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 71 2014July 24<![CDATA[A Decision Procedure for Deadlock-Free Routing in Wormhole Networks]]>$times$ 13 torus deadlock-free within seconds. Finding minimal deadlocks is more difficult. Our tool needs four minutes to find a minimal deadlock in a 11 $times$ 11 torus while it needs nine hours for a 12 $times$ 12 network.]]>25819351944327<![CDATA[A New Parity-Based Migration Method to Expand RAID-5]]>$m$ disks to a RAID-5 with $n$ disks, PBM achieves the minimal data migration which only needs to move $m/(n+m)$ of all data blocks. Furthermore, no parity blocks are recalculated during the expansion. After expansion, although the RAID is not a standard RAID-5 distribution, the parity blocks are distributed evenly. Experimental results based on extensive trace-driven show that, on average, PBM can reduce the time of expansion by 73.6 percent while only reduces the performance of the expanded RAID by 1.83 percent when compared with Multiple-Device (MD), a toolkit provided in Linux kernel.]]>25819451954831<![CDATA[A Study of Localization Accuracy Using Multiple Frequencies and Powers]]>258195519651117<![CDATA[Accelerating ODE-Based Simulation of General and Heterogeneous Biophysical Models Using a GPU]]>25819661975642<![CDATA[An Approximation Algorithm for Constructing Degree-Dependent Node-Weighted Multicast Trees]]>$(2ln {kover2} +1)(W_{T^{ast}}+B)$, where $k$ is the size of the set of multicast members, $W_{T^{ast}}$ is the cost of a minimum-cost Steiner tree $T^{ast}$, and $B$ is related to the degree-dependent node costs. Simulations are carried out to study the performance of the proposed algorithm. A distributed implementation of the proposed algorithm is presented. In addition, the proposed algorithm is generalized to solve the degree-dependent node-weighted constrained forest problem.]]>25819761985710<![CDATA[Bloom Filter Based Associative Deletion]]>258198619981143<![CDATA[Coding Opportunity Aware Backbone Metrics for Broadcast in Wireless Networks]]>258199920091038<![CDATA[CONDESA: A Framework for Controlling Data Distribution on Elastic Server Architectures]]>25820102019810<![CDATA[Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration]]>25820202029487<![CDATA[Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage]]>25820302042443<![CDATA[Energy Efficient Task Assignment with Guaranteed Probability Satisfying Timing Constraints for Embedded Systems]]>25820432052536<![CDATA[EPPDR: An Efficient Privacy-Preserving Demand Response Scheme with Adaptive Key Evolution in Smart Grid]]>25820532064431<![CDATA[Fault-Tolerant RFID Reader Localization Based on Passive RFID Tags]]>258206520761387<![CDATA[Lightweight Privacy-Preserving and Secure Communication Protocol for Hybrid Ad Hoc Wireless Networks]]>25820772090782<![CDATA[Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation]]>$times$ speedup over Hadoop and outperforms other state-of-the-art frameworks.]]>25820912100527<![CDATA[Minimizing System Cost with Efficient Task Assignment on Heterogeneous Multicore Processors Considering Time Constraint]]>25821012113635<![CDATA[OTrack: Towards Order Tracking for Tags in Mobile RFID Systems]]>258211421251121<![CDATA[Task-Tree Based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling]]>258212621371024<![CDATA[Parallel Workload Modeling with Realistic Characteristics]]>258213821481001<![CDATA[Performance Analysis of EDF Scheduling in a Multi-Priority Preemptive M/G/1 Queue]]>$M/G/1/./EDF$ system. Existing models on EDF scheduling consider them to be $M/M/1$ queues or nonpreemptive $M/G/1$ queues. The proposed model approximates the mean waiting time for a given class based on the higher and lower priority tasks receiving service prior to the target and the mean residual service time experienced. Additional time caused by preemptions is estimated as part of mean request completion time for a given class and as part of the mean delay experienced due to jobs in execution, on an arrival. The model is evaluated analytically and by simulation. Results confirm its accuracy, with the difference being a factor of two on average in high loads. Comparisons with other algorithms (such as First-Come-First-Served, Round-Robin and Nonpreemptive Priority Ordered) reveal that EDF achieves a better balance among priority classes where high priority requests are favored while preventing lower priority requests from overstarvation. EDF achieves best waiting times for higher priorities in lower to moderate loads (0.2-0.6) and while only being 6.5 times more than static priority algorithms in high loads (0.9). However, for the lowest priority classes, it achieves comparable waiting times to Round-Robin and First-Come-First-Served in low to moderate loads and achieves waiting times only twice the amount of Round-Robin in high system loads.]]>25821492158679<![CDATA[Performance Estimation of Pipelined MultiProcessor System-on-Chips (MPSoCs)]]>$10^{12}$ to $10^{18}$ design points, and hence simulation of all design points will take years and is infeasible. Compared to PS method, the PSP method reduced simulation time from days to several hour-
.]]>25821592168591<![CDATA[Practical Resource Provisioning and Caching with Dynamic Resilience for Cloud-Based Content Distribution Networks]]>25821692179908<![CDATA[Reliable Bulk-Data Dissemination in Delay Tolerant Networks]]>258218021891005<![CDATA[Robust Collaborative Spectrum Sensing Schemes for Cognitive Radio Networks]]>258219022001111<![CDATA[Securely Outsourcing Attribute-Based Encryption with Checkability]]>25822012210495