REQUIREMENT- AWARE STRATEGIES FOR SCHEDULING MULTIPLE DIVISIBLE LOADS IN CLUSTER ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism

Ductile fracture and microstructure of a bearing steel in hot tension

Ductile fracture, such as micro-cavities and micro-voids, inevitably exist and evolve under tensile stress state in metal forming. Ductile fracture sways the mechanical performance of 52100 bearing steel. It is necessary to investigate the influences of strain rate and deformation temperature on both ductile fracture and microstructure evolution. Uniaxial hot tension tests were performed, in which specimens were stretched to failure in the temperatures range from 950 C to 1160 C and in the strain rates range from 0.01 /s to 1.0 /s. Specimens metallographies have been explored after hot tension. Experimental results show that the peak stress decreases when deformation temperature increases and strain rate decreases. The critical strain of stress–strain relationships increases when strain rate increases. Fracture morphology is severe at higher deformation temperatures and lower strain rates. Hot tension deformation capacity is worst at 1160 C and a strain rate of 0.01 /s, has been caused by a larger and coarser grain structure

Ductile fracture and microstructure of a bearing steel in hot tension

382-388Ductile fracture, such as micro-cavities and micro-voids, inevitably exist and evolve under tensile stress state in metal forming. Ductile fracture sways the mechanical performance of 52100 bearing steel. It is necessary to investigate the influences of strain rate and deformation temperature on both ductile fracture and microstructure evolution. Uniaxial hot tension tests were performed, in which specimens were stretched to failure in the temperatures range from 950 °C to 1160 °C and in the strain rates range from 0.01 /s to 1.0 /s. Specimens metallographies have been explored after hot tension. Experimental results show that the peak stress decreases when deformation temperature increases and strain rate decreases. The critical strain of stress–strain relationships increases when strain rate increases. Fracture morphology is severe at higher deformation temperatures and lower strain rates. Hot tension deformation capacity is worst at 1160 °C and a strain rate of 0.01 /s, has been caused by a larger and coarser grain structure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Adaptive Scheduling of Task Graphs with Dynamic Resilience

This paper studies a scheduling problem of task graphs on a nondedicated networked computing platform. The networked platform is characterized by a set of fully connected processors such as a multiprocessor system that can be shared by multiple tasks. Therefore, the computation and communication capacities of the computing platform dynamically fluctuate. To deal with this fluctuations for high performance task graph computing, we propose an online dynamic resilience scheduling algorithm called Adaptive Scheduling Algorithm (ASA) that bears certain distinct features compared to existing algorithms. First, the proposed algorithm deliberately assigns tasks to idle processors in multiple rounds to prevent any unfavorable decisions and also to avoid inefficient assignments of certain key tasks to slow processors. Second, the algorithm adopts task duplication as an attempt to minimize serious increase of schedule length due to unexpected processor slowdown. Finally, a look-ahead message transmission policy is applied to save communication time and further improve the overall performance. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared with the existing algorithms.Accepted versio

Iterative Dynamic Critical Path Scheduling: An Efficient Technique for Offloading Task Graphs in Mobile Edge Computing

Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm

HackMan: hacking commodity millimeter-wave hardware for a measurement study

The extremely high frequency of Millimeter-Wave technology warrants Gbps throughput for the next-generation wireless communication systems, but mmWave signals also suffer from severe path loss due to high attenuation. To compensate for this loss, mmWave radios establish communication links via directional beams so as to increase channel gains and communication range. Until recently, the measurement studies on mmWave technology were mainly based on prototypes built from band-limited Software Defined Radio, which could not characterize performance in realistic settings. Latest studies using commodity hardware reports straightforward measurements on the impact of environment settings but did not present deep analysis on the correlations of low-layer information. Meanwhile, those studies lack the ability to configure commodity devices under controlled settings, for instance, a single beam pattern for experimentation, thus failing to perform deeper analysis on low-layer protocol parameters. In this paper, we conduct extensive measurements in typical indoor settings, utilizing 802.11ad-compliant commodity hardware. Different from earlier studies, we hack the firmware and gain the privilege to modify physical layer settings online, enabling us to gain more insights under controlled settings.Essentially, we have demonstrated that (1) Signal-to-Noise Ratio, the criteria for beam control, may not be positively correlated with throughput, (2) sticking to a single beam pattern during data transmission can lead to both channel gains and throughput improvement, and (3) only independent cross-links could interfere with each other while multi-links coordinated by one AP experience no interference. These insights lead us to rethink the existing beam control policy

Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources

Towards robust multiple blind source localization using source separation and beamforming

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.Published versio