Minimum-storage regenerating (MSR) codes are provably optimal erasure codes that minimize the repair bandwidth (i.e., the amount of traffic being transferred during a repair operation), while minimizing storage redundancy, in distributed storage systems. However, the practical repair performance of MSR codes still has significant room for improvements, as their mathematical structure makes repair operations difficult to parallelize. In this article, we present HyperParaRC, a parallel repair framework for MSR codes. HyperParaRC leverages the sub-packetization nature of MSR codes to parallelize the repair of sub-blocks and balance repair load (i.e., the amount of traffic sent or received by a node) across available nodes. We first demonstrate that there exists a trade-off between repair bandwidth and maximum repair load. We then propose an affinity-based heuristic for HyperParaRC, which approximately minimizes the maximum repair load by examining the bandwidth incurred during sub-block computations and significantly reduces the search time for large coding parameters compared with our earlier work, ParaRC. Based on our affinity-based heuristic, we further design a full-node recovery mechanism for HyperParaRC that combines both intra-stripe and inter-stripe parallel repair scheduling to repair multiple lost blocks in a failed node. We prototype HyperParaRC on Hadoop HDFS and evaluate it on Alibaba Cloud. Our evaluation results show that HyperParaRC reduces both single-block repair and full-node recovery times compared with state-of-the-art repair approaches.

We develop an exact value function-based approach to solve a class of bilevel integer programs with stochastic right-hand sides. We first study structural properties and design two methods to efficiently construct the value function of a bilevel integer program. Most notably, we generalize the integer complementary slackness theorem to bilevel integer programs. We also show that the value function of a bilevel integer program can be characterized by its values on a set of so-called bilevel minimal vectors. We then solve the value function reformulation of the original bilevel integer program with stochastic right-hand sides using a branch-and-bound algorithm. We demonstrate the performance of our solution methods on a set of randomly generated instances. We also apply the proposed approach to a bilevel facility interdiction problem. Our computational experiments show that the proposed solution methods can efficiently optimize large-scale instances. The performance of our value function-based approach is relatively insensitive to the number of scenarios, but it is sensitive to the number of constraints with stochastic right-hand sides. Summary of Contribution: Bilevel integer programs arise in many different application areas of operations research including supply chain, energy, defense, and revenue management. This paper derives structural properties of the value functions of bilevel integer programs. Furthermore, it proposes exact solution algorithms for a class of bilevel integer programs with stochastic right-hand sides. These algorithms extend the applicability of bilevel integer programs to a larger set of decision-making problems under uncertainty.

In this paper, a novel non-saturation analytical model for enhanced distributed channel access (EDCA) in IEEE 802.11e wireless local area network (WLAN) is proposed. Unlike previous work focused on saturation conditions, this model uses Markov and M/M/1/K theories to predict MAC delay, service time, throughput and loss. Moreover, the EDCA technique resets the default contention window (CW) of stations statically after each successful transmission. The static behavior which can not adapt to the network state results in bad performance. Consequently, we introduce an enhancement scheme based on quality of experience (QoE) perceived by users to adjust CW adaptively. Instead of solely relying on technical parameters such as loss and delay, our scheme is based on QoE which contains the parameters inherently. Simulations validate this model and demonstrate better performance of our scheme compared with traditional EDCA regarding QoS parameters and user satisfaction.