A Self-Adaptive Network for HPC Clouds: Architecture, Framework, and Implementation

Abstract

Clouds offer flexible and economically attractive compute and storage solutions for enterprises. However, the effectiveness of cloud computing for high-performance computing (HPC) systems still remains questionable. When clouds are deployed on lossless interconnection networks, like InfiniBand (IB), challenges related to load-balancing, low-overhead virtualization, and performance isolation hinder full potential utilization of the underlying interconnect. Moreover, cloud data centers incorporate a highly dynamic environment rendering static network reconfigurations, typically used in IB systems, infeasible. In this paper, we present a framework for a self-adaptive network architecture for HPC clouds based on lossless interconnection networks, demonstrated by means of our implemented IB prototype. Our solution, based on a feedback control and optimization loop, enables the lossless HPC network to dynamically adapt to the varying traffic patterns, current resource availability, workload distributions, and also in accordance with the service provider-defined policies. Furthermore, we present IBAdapt, a simplified ruled-based language for the service providers to specify adaptation strategies used by the framework. Our developed self-adaptive IB network prototype is demonstrated using state-of-the-art industry software. The results obtained on a test cluster demonstrate the feasibility and effectiveness of the framework when it comes to improving Quality-of-Service compliance in HPC clouds.

All-to-All Broadcast Algorithm in Galaxyfly Networks

“…This subsection validates the effectiveness of our proposed algorithms. We generate five network configurations, i.e., Galaxyfly (3,5,4), Galaxyfly (3,5,8), Galaxyfly(4, 5, 5), Galaxyfly (4,7,4), and Galaxyfly (4,7,5). The packet size, denoted by PS, varies among 160B, 320B, 640B, and 1280B.…”

“…In the case of exascale systems, for instance, a power envelope of 20-30 MW must be strictly adhered to in order to ensure low power consumption [7]. Third, the excellent network design should endow HPC systems with high flexibility so that HPC systems possess the ability to vary on different scales [8].…”

Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning

“…Resource allocation problem is omnipresent in cloud computing and many methods have been proposed to enhance resource utilization with rational resource provisioning and effective cost control. For example, Zahid et al [10] proposed a ruled-based language for CSPs, in order to improve the QoS compliance of high-performance computing (HPC) clouds. Through using probabilistic thresholds, a system model was designed in [23] for accomplishing the switching between different operating levels of cloud services.…”

“…Many classic solutions for cloud resource allocation are based on rules [10], heuristics [11], and control theory [12]. Although these solutions can solve the problem of cloud resource allocation to some extent, they commonly use the prior knowledge of cloud systems (e.g., state transitions, demand changes, and energy consumptions) to develop corresponding strategies of resource allocation.…”

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

“…Maurer et al [9] designed a resource-efficient decision-making method in response to workload fluctuations, where a rule-based knowledge management strategy was proposed to achieve the autonomous reconstruction of VMs. Zahid et al [10] designed a ruled-based language for CSPs with adaptation schemes, in order to enhance the QoS compliance in highperformance computing (HPC) clouds. However, these two works did not regard the minimization of resource costs as an optimization goal, which may lead to excessive resource costs.…”