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We consider the problem of preserving a large amount of data generated inside base station-less sensor networks, when sensor nodes are controlled by different authorities and behave selfishly. We modify the VCG mechanism to guarantee that each node, including the source nodes with overflow data packets, will voluntarily participate in data preservation. The mechanism ensures that each node truthfully reports its private type and network achieves efficiency for all the preserved data packets. Extensive simulations are conducted to further validate our results.

Positive psychological attributes are associated with better health outcomes, yet few studies have identified their underlying constructs and none have examined their temporal trajectories in clinical vs. non-clinical samples. From data collected over 4 years from people with HIV (PWH) and HIV-uninfected (HIV−) participants, we identified two latent factors (internal strengths; socioemotional support) based on responses to seven positive psychological attributes. Internal strengths increased over 4 years for PWH, but not for HIV− comparisons. Socioemotional support did not change significantly in either group. Lower internal strengths and worse socioemotional support were related to greater depressive symptoms. We speculate that improvement in internal strengths in PWH could reflect their being in care, but this requires further study to include PWH not in care. Given the apparent malleability of internal strengths and their association with improved health outcomes, these attributes can serve as promising intervention targets for PWH.

We propose a new algorithmic framework for traffic-optimal virtual network function (VNF) placement and migration for policy-preserving data centers (PPDCs). As dy- namic virtual machine (VM) traffic must traverse a sequence of VNFs in PPDCs, it generates more network traffic, consumes higher bandwidth, and causes additional traffic delays than a traditional data center. We design optimal, approximation, and heuristic traffic-aware VNF placement and migration algorithms to minimize the total network traffic in the PPDC. In particular, we propose the first traffic-aware constant-factor approximation algorithm for VNF placement, a Pareto-optimal solution for VNF migration, and a suite of efficient dynamic-programming (DP)-based heuristics that further improves the approximation solution. At the core of our framework are two new graph- theoretical problems that have not been studied. Using flow characteristics found in production data centers and realistic traffic patterns, we show that a) our VNF migration techniques are effective in mitigating dynamic traffic in PPDCs, reducing the total traffic cost by up to 73%, b) our VNF placement algorithms yield traffic costs 56% to 64% smaller than those by existing techniques, and c) our VNF migration algorithms outperform the state-of-the-art VM migration algorithms by up to 63% in reducing dynamic network traffic.

Fault-tolerant virtual machine (VM) placement refers to the process of placing multiple copies of the same VM cloud application inside cloud data centers. The challenge is how to place required number of VM replicas while minimizing the number of physical machines (PMs) that store them, in order to save energy consumption of cloud data centers. We refer to it as fault-tolerant VM placement problem. In our previous work, we have proposed a greedy algorithm to solve this problem. In this paper, we compare it with an existing research that is based on well-known Welsh Powell Graph-Coloring Algorithm to place items into bins while considering the conflicts between items and items and items and bins. Via extensive simulations, we show that our greedy algorithm can turn off 40-50% more PMs than existing work and can place upto four times as many VM replicas as existing work, achieving much stronger fault-tolerance with less energy consumption. We also compare both algorithms with the optimal integer lin- ear programming (ILP)-based algorithm, which serves as the benchmark of the comparison.

Virtual Network Functions (VNFs) are software implementation of middleboxes (MBs) (e.g., firewalls) that provide performance and security guarantees for virtual machine (VM) cloud applications. In this paper we study a new flow migration problem in VNF-enabled cloud data centers where the traffic rates of VM flows are constantly changing. Our goal is to minimize the total network traffic (therefore optimizing the network resources such as bandwidth and energy) while considering that VNFs have limited processing capability. We formulate the flow migration problem and design two efficient benefit-based greedy algorithms. The simulations show that our algorithms are effective in reducing the network traffic as well as in achieving load balance among VNFs. In particular, our flow migration algorithms can reduce upto 15% network traffic compared to the case without flow migration.