Control Of Data Centers Research Articles

FastCredit: Expediting credit-based congestion control in datacenters

Receiver-driven transports that leverage hop-by-hop credits have the merits of fast convergence, short queueing, and strong congestion avoidability, making them high-efficiency in managing congestion in current high-speed data centers. However, these transport protocols treat long and latency-sensitive short flows equally, increasing flow completion times and affecting user experiences. Although flow scheduling mechanisms have been studied extensively, they are hard to be deployed into hop-by-hop credit-based transports. The root cause is that most traditional flow scheduling mechanisms mainly work in the long queue containing flows of various sizes, while credit-based proactive transports maintain the extremely short bounded queue.Based on this observation, this paper presents FastCredit, making the first attempt to expedite short-flow scheduling in hop-by-hop credit-based congestion control. In FastCredit, we schedule credit transmission at both receivers and switches to indirectly perform data flow scheduling, and we develop an ECN-based credit rate control mechanism to mitigate the problems caused by credit loss. Compared to the state-of-the-art credit-based transport protocol, FastCredit reduces average flow completion time to 0.71x and greatly improves the short flow transmission latency to 0.45x in realistic workloads. Especially, FastCredit reduces average flow completion time to 0.72x under incast circumstances and 0.56x in many-to-one traffic mode. Moreover, FastCredit still maintains the advantages of the short queue and high throughput and essentially reduces credit loss by employing credit rate control.

Reinforcement Learning for Datacenter Congestion Control

We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that these challenges prevent standard RL algorithms from operating within this domain. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, show that our method exhibits improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

Reinforcement Learning for Datacenter Congestion Control

We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, nonstationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

Harmonia: Explicit Congestion Notification and Credit-Reservation Transport Converged Congestion Control in Datacenters

Harmonia: Explicit Congestion Notification and Credit-Reservation Transport Converged Congestion Control in Datacenters

Dual-Level Cooperative Game Approach for Energy-Aware Resource Allocation in Data Centers

Cloud computing is the next major paradigm shift in information and communication technology. Today, data center (DC) is pivotal to the effective implementation of the cloud computing paradigm. To deliver the necessary cloud services, the DC faces major reliability and robustness challenges. In this study, we concentrate upon the efficient DC resource allocation technique by considering the synergy of the performance and energy efficiency. To achieve the maximum system efficiency, thermal-aware control mechanism is developed based on the dual-level cooperative game. Proactively, we adaptively assign the workload among different servers using the concept of relative utilitarian bargaining solution. Reactively, we maintain a thermal equilibrium according to the characteristics of τ-value. The main advantage of our dual-level game model is to provide an axiom-based strategic solution for the DC resource allocation problem while dynamically responding to the current workload and thermal conditions. The effectiveness of our proposed protocol is verified through simulation evaluations under different workload scenarios. Results demonstrate that the proposed approach significantly outperforms the existing state-of-the-art DC control schemes from energy consumption and load balancing perspectives. Last, some open problems are identified and we summarize possible research issues for the future study.

Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.