This paper quantitatively analyses the potential of vertical scaling MicroVMs in serverless computing. Our analysis shows that under real-world serverless workloads, vertical scaling can significantly improve execution performance and resource utilization. However, we also find that the memory scaling of MicroVMs is the bottleneck that hinders vertical scaling from reaching the performance ceiling. We propose Faascale, a novel mechanism that efficiently scales the memory of MicroVMs for serverless applications. Faascale employs a series of techniques to tackle this bottleneck: 1) it sizes up/down the memory for a MicroVM by blocks that bind with a function instance instead of general pages; and 2) it pre-populates physical memory for function instances to reduce the delays introduced by the lazy-population. Compared with existing memory scaling mechanisms, Faascale improves the memory scaling efficiency by 2 to 3 orders of magnitude. Based on Faascale, we realize a serverless platform, named Puffer. Experiments conducted on eight serverless benchmark functions demonstrate that compared with horizontal scaling strategies, Puffer reduces time for cold-starting MicroVMs by 89.01%, improves memory utilization by 17.66%, and decreases functions execution time by 23.93% on average.

Knowledge distillation has been widely used to improve the performance of small compact models for face recognition. However, selecting key knowledge and effectively transferring it from teacher to student remains a challenging problem. In this work, we propose an efficient Cluster-based Knowledge Distillation (CKD) dedicated to aligning the student model with the teacher model in terms of both sample relations and class centers. Specifically, CKD first determines the key sample relations based on the similarities between the sample features extracted by the teacher and their cluster centers generated by existing clustering algorithms. Then, CKD effectively transfers the knowledge of the above relations from the teacher to the student by designing a cluster-based relation distillation loss. Finally, CKD further improves the quality of the student's class centers by constructing a center loss between the above representative cluster centers and the student's class centers. We validate the proposed CKD on multiple face benchmarks. For example, CKD improves the baseline student performance from 91.95% to 94.20% on MegaFace and consistently outperforms recent competitive distillation methods on multiple benchmarks. These results demonstrate the effectiveness and superiority of CKD.

In the erasure-coded storage systems, multiple related blocks have to be retrieved from other surviving nodes to repair a failed block. This incurs significant communication overhead with the surging scale of distributed storage systems. To mitigate the bandwidth bottleneck, in-network repair (INR) has emerged as a promising transport paradigm, which migrates the aggregation operations from the repair node to the programmable hardware, such as Intel Tofino switches. However, due to the limited on-chip memory size of these switches, the INR can degrade to the most primitive incast-type transmission, leading to massive traffic volume and hindered repair throughput. While we notice that, there are spare CPU cores in the storage servers that can be leveraged as alternative computing resources. With this intuition, we propose SINA, a Server-assisted In-Network repair Acceleration framework in this paper, which leverages the spare servers to assist aggregation operations when the programming switches' memory size is scarce for failure repair. We formulate this problem by adjusting the aggregation modes across the involved racks and solve this NP-hard problem using the Gurobi optimization solver. For all we know, this is the first work exploring spare servers for assisting the memory-scarce INR in erasure-coded storage systems. We have implemented SINA on an FPGA-based prototype system, and the experimental results show that SINA can ensure fault tolerance and accelerate failure repair by$5.0 \times$compared to the conventional methods.

The advent of cloud computing offers a novel mode of managing computing resources, enabling users to flexibly utilize required computing and storage resources through the network. Simultaneously, it allows large-scale computing centers and data centers to more effectively utilize their computing resources. With the rapid development of cloud computing technology, the exponential growth of massive data storage needs from numerous users has brought challenges in storage cost, performance, reliability, and security. Conventional single-objective optimization approaches, which concentrate exclusively on a singular performance metric, are increasingly inadequate to address the intricate requirements of cloud storage systems. In contrast, multi-objective optimization methods can simultaneously optimize multiple aspects of concern to users or operators, providing more comprehensive solutions for cloud computing services. In this paper, we first introduce the basic concepts of multi-objective optimization problems. Subsequently, we present a comprehensive review of multi-objective optimization applications in cloud storage systems, including the setting of optimization objectives, algorithm selection, and comparison methods of experimental results. Finally, this paper summarizes and discusses the current implementation of multi-objective evolutionary algorithms in cloud storage optimization, and provides an outlook on future research directions.