Yiu-Wing Leung’s research while affiliated with Hong Kong Baptist University and other places

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 × 50 $\rm m^2$ ), our algorithm gives the optimal solutions; when the area is larger (80 × 60 $\rm m^2$ ), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

Energy conservation of large data centers for high-performance computing workloads, such as deep learning with big data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

Erasure codes have been used extensively in large-scale storage systems to reduce the storage overhead of triplication-based storage systems. One key performance issue introduced by erasure codes is the long time needed to recover from a single failure, which occurs constantly in large-scale storage systems. We present ESetStore, a prototype erasure-coded storage system that aims to achieve fast recovery from failures. ESetStore is novel in the following aspects. We proposed a data placement algorithm named ESet for our ESetStore that can aggregate adequate I/O resources from available storage servers to recover from each single failure. We designed and implemented efficient read and write operations on our erasure-coded storage system via effective use of available I/O and computation resources. Our proposed fast data recovery solution can also enhance existing solutions. We evaluated the performance of ESetStore with extensive experiments on a cluster with 50 storage servers. The evaluation results demonstrate that our recovery performance can obtain linear performance growth by harvesting available I/O resources. With our defined parameter recovery I/O parallelism I under some mild conditions, we can achieve optimal recovery performance, in which ESet enables minimal recovery time. Rather than being an alternative to improve recovery performance, our work can be an enhancement for existing solutions, such as Partial-parallel-repair (PPR), to further improve recovery performance.