Load Balancing In Data Centers Research Articles

Intelligent load balancing in data center software‐defined networks

AbstractIn response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load‐balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software‐defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real‐time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat‐tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal‐cost multipath and Hedera.

Алгоритм динамического распределения и балансировки нагрузки в распределенных облачных вычислениях

A mathematical model and algorithm of a two-level load management system for virtual clusters of a data processing center (data center) have been developed. At the first management level, virtual machines (VMs) are assigned to physical servers. At the same time, a greedy algorithm is used with restrictions on the time of searching for acceptable load distribution alternatives. The second level of management is implemented taking into account the chaotic structure of network traffic between the data center and users. Checking for the randomness of a time series of information traffic is carried out using Lyapunov exponents. The predictive model of the load intensity is implemented using the method of phase space reconstruction based on a set of values of a one-dimensional time series. When constructing a reconstructed phase space attractor, the time delay value is selected from the condition of reaching the zero value of the autocorrelation function, and the dimension of the embedding is determined by the angle of inclination of the straight line approximating the dependence of the value of the correlation integral on the radius of a given threshold point. The Tayler window is used to exclude correlated points in the numerical series. The criterion for evaluating the effectiveness of the developed algorithm is an integral indicator of the deviation of the load of each server from a given level. The proposed model can be used to build a data center load balancing system in conditions of its nonlinear nature.

A novel approach for new architecture for green data centre

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.

RMC: Reordering Marking and Coding for Fine-Grained Load Balancing in Data Centers

Data center networks typically adopt multi-rooted tree topologies to provide high bisection bandwidth. Various fine-grained load balancing schemes have been proposed to split flows across multiple paths. However, data center networks suffer from many uncertainties such as highly dynamic traffic. These uncertainties easily make network become asymmetric, resulting in significant packet reordering. Unfortunately, existing solutions passively deal with packet reordering based on a threshold and hardly adapt to asymmetric networks because of lacking the explicit reordering feedback. These solutions either fail to quickly respond to packet loss or cause unnecessary fast retransmission, which reduces link utilization and increases flow completion time. In this paper, we propose a fine-grained load balancing scheme RMC to eliminate the impact of packet reordering and handle uncertainties in asymmetric networks. To avoid unnecessary fast retransmission, the switch proactively identifies reordered packet according to local queue length and global path latency. Furthermore, we employ a coding technique with redundancy optimization to reduce long-tailed flow completion time under network asymmetry. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that RMC effectively avoids unnecessary fast retransmission under different network scenarios and reduces flow completion time by up to 72% compared with state-of-the-art schemes.

Adjusting Switching Granularity of Load Balancing for Heterogeneous Datacenter Traffic

The state-of-the-art datacenter load balancing designs commonly optimize bisection bandwidth with homogeneous switching granularity. Their performances surprisingly degrade under mixed traffic containing both short and long flows. Specifically, the short flows suffer from long-tailed delay, while the throughputs of long flows also degrade dramatically due to low link utilization and packet reordering. To solve these problems, we design a traffic-aware load balancing (TLB) scheme to adaptively adjust the switching granularity of long flows according to the load strength of short ones. Under the heavy load of short flows, the long flows use large switching granularity to help short ones obtain more opportunities in choosing short queues to complete quickly. On the contrary, the long flows reroute flexibly with small switching granularity to achieve high throughput. Furthermore, under extremely bursty scenario, we utilize the packet slicing scheme for long flows to release bandwidth for short ones. The experimental results of NS2 simulation and testbed implementation show that TLB significantly reduces the average flow completion time of short flows by 16%-67% over the state-of-the-art load balancers and achieves the high throughput for long flows. Moreover, for extreme bursty case, at the acceptable throughput degradation of long flows, TLB with packet slicing reduces the deadline missing ratio of bursty short flows by up to 80%.

Load balancing with traffic isolation in data center networks

The topologies of current data center networks are typically multi-rooted trees (e.g. leaf–spine) with rich parallel paths between any pair of hosts. Recent progress has demonstrated that effective load balancing can fully utilize these parallel paths to speed up data transfer. Nonetheless, the existing load balancing designs are agnostic to the heterogeneous datacenter traffic, i.e., a mass of delay-sensitive short flows mix with a handful of throughput-oriented long flows, and casually reroute these flows regardless of path condition, thus resulting in frequent flow collisions. The short flows suffer from the problem of large queuing delay due to colliding with long flows, while the frequent collisions between long flows lead to low network utilization and serious throughput degradation. To address these inefficiencies, we propose an isolation-based load balancing scheme, namely ILB, which perceives flow collisions and dynamically isolates the long flows from short flows. Specifically, when a long flow collides with short flows in the same path, it immediately switches to another path unused by short flows to help the short ones in the previous path complete quickly. When short flows disappear, the long flow quickly occupies all its available paths to achieve high throughput. Moreover, ILB is only deployed on the leaf switch without modifying the end-host and spine switch. Experimental results of NS3 simulations show that ILB respectively reduces the average and tail flow completion time for short flows by up to 30% and 70%, as well as increases the throughput of long flows by about 1.68x over the state-of-the-art datacenter load balancing schemes.

AAC: Adaptively Adjusting Concurrency by Exploiting Path Diversity in Datacenter Networks

Recent datacenter load balancing designs make full use of all available multiple paths to achieve high bisection bandwidth and support the increasing traffic demands. However, a multitude of uncertainties, such as congestion and asymmetry, easily leads to long tailed latencies for unlucky flows on bad paths. In this paper, we aim at adjusting the maximum number of multiple paths used by existing load balancing designs to achieve good tradeoff between the tailed latency and link utilization. Specifically, we propose a packet-level load balancing called scheme Adaptively Adjusting Concurrency (AAC) to spread packets across the multiple paths, which are adaptively selected according to path diversity. AAC is deployed at switch, without any modifications on end-hosts. The experimental results of NS2 simulation and Mininet implementation show that AAC significantly reduces the flow completion time by $$\sim$$ 21–56% over the state-of-the-art datacenter load balancing designs including MPTCP, LetFlow and RPS.

SOA-MZI based 4 × 4 interconnected crossbar photonic wavelength switching for datacenter load balancing

Our paper is focused on an extremely efficient structure for photonic crossbar switch using a fundamental building element, a semiconductor optical amplifier (SOA) in a Mach–Zehnder interferometer configuration. The switch is designed with a 10 Gbps rate and a high Q-factor of 30; a bit error rate ranging between ∼e − 122 and ∼e − 184 is achieved. In addition, the designed photonic switch model is reliable in terms of having an extinction ratio of 16 dB on average. High optical signal-to-noise ratio values, ranging between 101 and 104 dB, provide the low interference of noise in the required signals. It is observed that the proposed configuration is fast enough to provide a switching time ranging between 0.146 and 0.227 ns and is feasible in scaling the ports with an increase in the number of users to maintain a good quality factor. Hence, the proposed architecture can be taken as a solution for challenges in future datacenter network switching.

Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.

Queue length‐based load balancing in data center networks

SummaryTraffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds.This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.

MaxPass: Credit-based multipath transmission for load balancing in data centers

Various applications require the data center networks to carry their traffic efficiently. The data center networks usually have a hierarchical topology and exhibit distinct traffic patterns, which is different from the traditional Internet. These features have driven the data center networks to reduce the flow completion time (FCT) and to achieve high throughput. One of the possible solutions is balancing network loads across multiple paths by leveraging transport mechanisms like equal-cost multipath (ECMP) routing. ECMP allows flows to exploit multiple paths by hashing the metadata of the flows. However, due to the random nature of hash functions, ECMP often distributes the traffic unevenly, which makes it hard to utilize the links' full capacity. Thus, we propose an adaptive load balancing mechanism for multiple paths in data centers, dubbed MaxPass, to complement ECMP. A sender adaptively selects and dynamically changes multiple paths depending on the current network status like congestion. To monitor the network status, the corresponding receiver transmits a probe packet periodically to the sender; its loss indicates a traffic congestion. We carry out the quantitative analysis on the ns-2 simulator to show that MaxPass can improve the FCT and the throughput.

New approach for reducing energy consumption and load balancing in data centers of cloud computing

New approach for reducing energy consumption and load balancing in data centers of cloud computing

Adaptive load balancing based on accurate congestion feedback for asymmetric topologies

Datacenter load balancing schemes exist to facilitate parallel data transmission with multiple paths under various uncertainties such as traffic dynamics and topology asymmetries. Taking deployment challenges into account, several optimized schemes (e.g. CLOVE, Hermes) to ECMP balance load at end hosts. However, inaccurate congestion feedback exists in these solutions. They either detect congestion through Explicit Congestion Notification (ECN) and coarse-grained Round-Trip Time (RTT) measurements or are congestion-oblivious. These congestion feedbacks are not sufficient enough to indicate the accurate congestion status under asymmetry. And when rerouting events occur, outdated ACKs carrying congestion feedback of other paths can improperly influence the current sending rate. After our observations and analyses, these inaccurate congestion feedback can degrade performance.Therefore, we explore how to address above problems while ensuring good adaptation to existing switch hardware and network protocol stack. We propose ALB, an adaptive load balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry. ALB leverages a latency-based congestion detection to precisely reroute new flowlets to the paths with lighter load, and an ACK correction method to avoid inaccurate flow rate adjustment. In large-scale simulations, ALB achieves up to 13% and 48% better average flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry, respectively. And compared with other schemes ALB improves the average and the 99th percentile FCTs for small flows under high bursty traffic by 43–174% and 75–129%. Under the situation of dynamic network changes, ALB also provides competitive overall performance and maintains stable performance for small flows.

Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse?

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

Load Balancing in Data Center Networks: A Survey

Data center networks usually employ the scale-out model to provide high bisection bandwidth for applications. A large amount of data is required to be transferred frequently between servers across multiple paths. However, traditional load balancing algorithms like equal-cost multi-path routing are not suitable for rapidly varying traffic in data center networks. Based on the special data center topologies and traffic characteristics, researchers have recently proposed some novel traffic scheduling mechanisms to balance traffic. In this paper, we present a comprehensive survey of recent solutions for load balancing in data center networks. First, recently proposed data center network topologies and the studies of traffic characteristics are introduced. Second, the definition of the load-balancing problem is described. Third, we analyze the differences between data center load balancing mechanisms and traditional Internet traffic scheduling. Then, we present an in-depth overview of recent data center load balancing mechanisms. Finally, we analyze the performance of these solutions and discuss future research directions.

Water-Constrained Geographic Load Balancing in Data Centers

Spreading across many parts of the world and presently hard striking California, extended droughts could even potentially threaten reliable electricity production and local water supplies, both of which are critical for data center operation. While numerous efforts have been dedicated to reducing data centers’ energy consumption, the enormity of data centers’ water footprints is largely neglected and, if still left unchecked, may handicap service availability during droughts. In this paper, we propose a water-aware workload management algorithm, called WATCH (WATer-constrained workload sCHeduling in data centers), which caps data centers’ long-term water consumption by exploiting spatio-temporal diversities of water efficiency and dynamically dispatching workloads among distributed data centers. We demonstrate the effectiveness of WATCH both analytically and empirically using simulations: based on only online information, WATCH can result in a provably-low operational cost while successfully capping water consumption under a desired level. Our results also show that WATCH can cut water consumption by 20 percent while only incurring a negligible cost increase even compared to state-of-the-art cost-minimizing but water-oblivious solution. Sensitivity studies are conducted to validate WATCH under various settings.