Hao Dai

With the fast growth of mobile edge computing (MEC), the deep neural network (DNN) has gained more opportunities in application to various mobile services. Given the tremendous number of learning parameters and large model size, the DNN model is often trained in cloud center and then dispatched to end devices for inference via edge network. Therefore, maximizing the cost-efficiency of learned model dispatch in the edge network would be a critical problem for the model serving in various application contexts. To reach this goal, in this article we focus mainly on reducing the total model dispatch cost in the edge network while maintaining the efficiency of the model inference. We first study this problem in its off-line form as a baseline where a sequence of n requests can be pre-defined in advance and exploit dynamic programming techniques to obtain a fast optimal algorithm in time complexity of O(m2n) under a semi-homogeneous cost model in a m -sized network. Then, we design and implement a 2.5-competitive algorithm for its online case with a provable lower bound of 2 for any deterministic online algorithm. We verify our results through careful algorithmic analysis and validate their actual performance via a trace-based study based on a public open international mobile network dataset.

In this paper we study a data caching problem in edge-based content delivery network (CDN) where a data item is shared between service requests. Instead of improving the hit ratio with respect to limited capacity as in traditional case, we study the problem based on a semi-homogeneous (semi-homo) cost model in the edge-based CDN with monetary cost reduction as a goal. The cost model is semi-homo in the sense that all pairs of caching nodes have the same transfer cost, but each has its own caching cost rate. In particular, given a stream of requests R to a shared data item in the edge network, we present a shortest-path based optimal off-line caching algorithm that can minimize the total transfer and caching costs within O(mn) time ( m: the number of network nodes, n: the length of request stream) in a proactive fashion. While for the online case, by extending the anticipatory caching idea, we also propose a 2-competitive online reactive caching algorithm and show its tightness by giving a lower bound of the competitive ratio as 2−o(1) for any deterministic online algorithm. Finally, to combine the advantages of both algorithms and evaluate our findings, we also design a hybrid algorithm. Our trace-based empirical studies show that the proposed algorithms not only improve the previous results in both time complexity and competitive ratio, but also relax the cost model to semi-homogeneity, rendering the algorithms more practical in reality. We provably achieve these results with our deep insights into the problem and careful analysis of the solutions, together with a prototype framework.

Currently, most edge-based Deep Reinforcement Learning (Deep-RL) applications have been deployed in the edge network, however, their mainstream studies are still short of adequate considerations on its limited compute and bandwidth resources. In this paper, we investigate the near on-policy of actions taking in distributed Deep-RL architecture, and propose a “hybrid near on-policy” Deep-RL framework, called Coknight, by leveraging a game-theory based DNN partition approach. We first formulate the partition problem into a variant of knapsack problem in device-edge setting, and then transform it into a potential game with a formal proof. Finally, we show the problem is NP-complete whereby an efficient distributed algorithm based on the potential game theory is developed from device perspective to achieve fast and dynamic partitioning. Coknight not only significantly improves the resource efficiency of the Deep-RL but also allows the inference to enforce the scalability of the actor policy. We prototype the framework with extensive experiments to validate our findings. The experimental results show that with the premise of a rapid convergence guarantee, Coknight, compared with Seed-RL, can reduce GPU utilization by 30% while providing large-scale scalability.

File system metadata is the data in charge of maintaining namespace, permission semantics and location of file data blocks. Operations on the metadata can account for up to 80% of total file system operations. As such, the performance of metadata services significantly impacts the overall performance of file systems. A large-scale distributed file system (DFS) is a storage system that is composed of multiple storage devices spreading across different sites to accommodate data files, and in most cases, to provide users with location independent access interfaces. Large-scale DFSs have been widely deployed as a substrate to a plethora of computing systems, and thus their metadata management efficiency is crucial to a massive number of applications, especially with the advent of the Big Data age, which poses tremendous pressure on underlying storage systems. This paper reports the state-of-the-art research on metadata services in large-scale distributed file systems, which is conducted from three indicative perspectives that are always used to characterize DFSs: high-scalability, high-performance, and high-availability, with special focus on their respective major challenges as well as their developed mainstream technologies. Additionally, the paper also identifies and analyzes several existing problems in the research, which could be used as a reference for related studies.