Improving resource utilization in datacenters is vital for reducing costs for cloud service providers (CSPs). Increasing resource utilization must be balanced with maintaining quality of service (QoS) for latency-critical applications. In cloud environments, users often request excessive resources for applications to ensure QoS. To address this issue, CSPs use resource overcommitment - offering users resources that exceed the actual capacity of physical infrastructure. However, if not properly managed, such strategies may result in performance degradation or even request failure. Therefore, to achieve optimal resource utilization while maintaining QoS to applications, it is critical to implement a fine-grained overcommitment strategy.We propose HAPPIES, a History-aware management system with a precise prediction for machine resource demand. HAPPIES uses historical usage to extract resource characteristics and build application portraits that describe their resource demands. Compared to the existing strategy, this is a more aggressive overcommitment strategy that achieves higher resource utilization. We simulated experiments on 3,021 nodes and deployed over 14,000 applications on them. Results show that HAPPIES significantly outperforms Kubernetes Least Request and Peak Oracle in load balancing. Not only does it reduce the number of nodes experiencing high utilization, but it also decreases the peak usage of the most heavily utilized nodes. Therefore, HAPPIES scheduling reduces the risk of a machine being used beyond capacity.

Identifying and managing performance interference in clouds has long been a critical and challenging task for cloud providers. They keep seeking useful performance indicators from underlying systems to monitor cloud applications accurately. However, state-of-the-art indicators are either sensitive to limited applications and resource contention or are unrobust to the continually changing production environments. There still lacks a practical and efficient indicator for production environments.

Abstract The accurate representation of objects holds pivotal significance in the realm of panoptic segmentation. Presently, prevalent object representation methodologies, including box-based, keypoint-based and query-based techniques, encounter a challenge known as the ‘representation confusion’ issue in specific scenarios, often resulting in the mislabeling of instances. In response, this paper introduces Convex Object Representation (COR), a straightforward yet highly effective approach to address this problem. COR leverages a CNN-based Euclidean Distance Transform to convert the target instance into a convex heatmap. Simultaneously, it offers a parallel embedding method for encoding the object. Subsequently, COR characterizes objects based on the distinctive embedding vectors of their convex vertices. This paper seamlessly integrates COR into a state-of-the-art query-based panoptic segmentation framework. Experimental findings validate that COR successfully mitigates the representation confusion predicament, enhancing segmentation accuracy. The COR-augmented methods exhibit notable improvements of +1.3 and +0.7 points in PQ on the Cityscapes validation and MS COCO panoptic 2017 validation datasets, respectively.

DC/OS is a widely used distributed operating system that abstracts the resources of light-weighted virtualized datacenters, which is based on Mesos distributed systems kernel and user space services such as Marathon. It automates resource management and process scheduling, thus significantly impacts the performance of datacenters. In this paper, we propose Themis, a flexible, automatic and distributed framework, to evaluate the performance and scalability across both DC/OS and virtualization layer. We can integrate most emerging scale-out datacenter workloads into this framework, and get an easily understandable score that scales with underlying system capacity using a configurable controlled strategy.

This paper presents PARD, a programmable architecture for resourcing-on-demand that provides a new programming interface to convey an application's high-level information like quality-of-service requirements to the hardware. PARD enables new functionalities like fully hardware-supported virtualization and differentiated services in computers. PARD is inspired by the observation that a computer is inherently a network in which hardware components communicate via packets (e.g., over the NoC or PCIe). We apply principles of software-defined networking to this intra-computer network and address three major challenges. First, to deal with the semantic gap between high-level applications and underlying hardware packets, PARD attaches a high-level semantic tag (e.g., a virtual machine or thread ID) to each memory-access, I/O, or interrupt packet. Second, to make hardware components more manageable, PARD implements programmable control planes that can be integrated into various shared resources (e.g., cache, DRAM, and I/O devices) and can differentially process packets according to tag-based rules. Third, to facilitate programming, PARD abstracts all control planes as a device file tree to provide a uniform programming interface via which users create and apply tag-based rules.

Datacenters consolidate diverse applications to improve utilization. However when multiple applications are co-located on such platforms, contention for shared resources like Networks-on-Chip (NoCs) can degrade the performance of latency-critical online services (high-priority applications). Recently proposed bufferless NoCs have the advantages of requiring less area and power, but they pose challenges in quality-of-service (QoS) support, which usually relies on buffer-based virtual channels (VCs). We propose QBLESS, a QoS-aware bufferless NoC scheme for datacenters. QBLESS consists of two components: a routing mechanism (QBLESS-R) that can substantially reduce flit deflection for high-priority applications, and a congestion-control mechanism (QBLESS-CC) that guarantees performance for high-priority applications and improves overall system throughput. We use trace-driven simulation to model a 64-core system, finding that when compared to BLESS, a previous state-of-the-art bufferless NoC design, QBLESS improves performance of high-priority applications by an average of 33.2%.