A load balancer (LB) is a vital network function for cloud services to balance the load amongst resources. Stateful software LBs that run on commodity servers provides flexibility, cost-efficiency, and packet consistency. However current designs have two main limitations: 1) states are stored as digests which may cause packet inconsistency due to digest collisions; 2) the data plane needs to update for every new connection, and frequent updates hurt throughput and packet consistency. In this work, we present a new software stateful LB called Concury, which is the first solution to solve these problems. The key innovation of Concury is an algorithmic approach to store and look up large network states with frequent connection arrivals, which is succinct in memory cost, consistent under network changes, and incurs infrequent data plane updates. The evaluation results show that the Concury algorithm provides 4x throughput and consumes less memory compared to other LB algorithms, while providing weighted load balancing and false-hit freedom, for both real and synthetic data center traffic. We implement Concury as a prototype system deployed in CloudLab and show that the throughput of Concury on a single thread can reach 62.5% of the maximum capacity of two 10GbE NICs and that on two threads can reach the maximum capacity.
The concepts of terminating or not terminating Secure Sockets Layer (SSL) at the load balancer have always generated intriguing conversations. In this paper, the author explains the pros and cons of such concepts in a simplistic manner and also provides suggested recommendations to help organizations understand the security implications associated with unencrypted traffic flowing from the Load Balancer to the App Servers. Keywords: Secure Sockets Layer (SSL), Load Balancer, App Server, Man-in-the-Middle Attack (MITM),End to End Encryption (E2EE)
We present an architectural design and a reference implementation for horizontal scaling of virtual network function chains. Our solution does not require any changes to network functions and is able to handle stateful network functions for which states may depend on both directions of the traffic. We use connection-aware traffic load balancers based on hashing function to maintain mappings between connections and the dynamically changing network function chains. Our references implementation uses OpenFlow switches to route traffic to the assigned network function instances according to the lo…
In order to scale web or other services, the load on single instances of the respective service has to be balanced. Many services are stateful such that packets belonging to the same connection must be delivered to the same instance. This requires stateful load balancers which are mostly implemented in software. On the one hand, modern packet processing frameworks supporting software load balancers, such as the Data Plane Development Kit (DPDK), deliver high performance compared to older approaches. On the other hand, common Network Interface Cards (NICs) provide additional matching capabiliti…
To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to data center traffic shaping / steering transport capability featuring data event-aware network shaping and forwarding. The keystone of this ESnet JLab FPGA Accelerated Transport (EJFAT) is the joint development of a dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB data p…
Software-defined networking (SDN) is an architecture that aims to make networks fast and flexible. SDN's goal is to improve network control by enabling service providers as well as enterprises to respond quickly to changing business needs. In SDN, the administrator can shape traffic from a centralized control console without having to modify any of the individual switches belonging to the network. The SDN controller which is centralized directs the switches to deliver network services wherever they are needed, irrespective of the specific connections between a server and devices. This methodol…
Load balancing at transport layer is an important function in data centers, content delivery networks, and mobile networks, where per-connection consistency (PCC) has to be met for optimal performance. Cloud-native L4 load balancers are commonly deployed as virtual network functions (VNFs) and are a critical forwarding element in modern cloud infrastructure. We identify load imbalance among service instances as the main cause of additional processing delay caused by transport-layer load balancers. Existing transport-layer load balancers rely on one of two methods: host-level traffic redirectio…
Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services. However, when load balancers operate in dynamic environments with limited monitoring of application server loads, they rely on heuristic algorithms that require manual configurations for fairness and performance. To alleviate that, this paper proposes a distributed asynchronous reinforcement learning mechanism to-with no active load balancer state monitoring and limited network observations-improve the fairness of the workload d…
With technological advancements and constant changes of Internet, cloud computing has been today's trend. With the lower cost and convenience of cloud computing services, users have increasingly put their Web resources and information in the cloud environment. The availability and reliability of the client systems will become increasingly important. Today cloud applications slightest interruption, the impact will be significant for users. It is an important issue that how to ensure reliability and stability of the cloud sites. Load balancing would be one good solution. This paper presents a fr…
The load-balancing system, built on the basis of a subsystem load balancer and subsystem control and monitoring that closely interact with each other was propose in work. This system is presented as a queuing system with priority service discipline. In the described queuing system parallel processing flow applications occur in the multiple serving devices and successive junction of them into unified stream is done. The method of multifractal load balancing is submited on the basis of the developed system of load balancing.
Load-Balancers play an important role in data centers as they distribute network flows across application servers and guarantee per-connection consistency. It is hard however to make fair load balancing decisions so that all resources are efficiently occupied yet not overloaded. Tracking connection states allows to infer server load states and make informed decisions, but at the cost of additional memory space consumption. This makes it hard to implement on programmable hardware, which has constrained memory but offers line-rate performance. This paper presents Charon, a stateless load-aware l…
Network load balancers are important components in data centers to provide scalable services. Workload distribution algorithms are based on heuristics, e.g., Equal-Cost Multi-Path (ECMP), Weighted-Cost Multi-Path (WCMP) or naive machine learning (ML) algorithms, e.g., ridge regression. Advanced ML-based approaches help achieve performance gain in different networking and system problems. However, it is challenging to apply ML algorithms on networking problems in real-life systems. It requires domain knowledge to collect features from low-latency, high-throughput, and scalable networking system…
Nowadays, centralized Path Computation Elements (PCE) integrate control plane algorithms to optimize routing and load-balancing continuously. When a link fails, the traffic load is automatically transferred to the remaining paths according to the configuration of load-balancers. In this context, we propose a load-balancing method that anticipates load transfers to ensure the protection of traffic against any Shared Risk Link Group (SRLG) failure. The main objective of this approach is to make better use of bandwidth compared to existing methods. It consists in reserving a minimum amount of ext…
Load Balancing plays a vital role in modern data centers to distribute traffic among instances of network functions or services. State-of-the-art load balancers such as Silkroad dispatch traffic obliviously without considering the real-time utilization of service instances and therefore can lead to uneven load distribution and suboptimal performance. In this paper, we design and implement Spotlight, a scalable and distributed load balancing architecture that maintains connection-to-instance mapping consistency at the edge of data center networks. Spotlight uses a new stateful flow dispatcher w…
Session Initiation Protocol (SIP) grows for VoIP applications, and faces challenges including security and overload. On the other hand, the new concept of Software-defined Networking (SDN) has made great changes in the networked world. SDN is the idea of separating the control plane from the network infrastructure that can bring several benefits. We used this idea to provide a new architecture for SIP networks. Moreover, for the load distribution challenge in these networks, a framework based on SDN was offered, in which the load balancing and network management can be easily done by a central…
This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Traditional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load balancers. The cooperative network load balancing task is formulated as a Dec-POMDP problem, which naturally induces the MARL methods. To bridge the reality gap for applying learning-based methods, all methods are directly trained and evaluate…
Content Delivery Network (CDN) has been emerged as a compelling technology to provide efficient and scalable web services even under high client request. However, this leads to a dilemma between minimum deployment cost and robust service under heavy loads. To solve this problem, we propose the Workload Automation Engine (WAE) which enables dynamic resource management, automated scaling and rapid service deployment with least cost for CDN providers. Our modular design uses an algorithm to calculate the optimal assignment of virtual CDN functions such as streaming, progressive delivering and loa…
Maintaining an up-to-date global network view is of crucial importance for intelligent SDN applications that need to act autonomously. In this paper, we focus on two key factors that can affect the controllers' global network view and subsequently impact the application performance. Particularly we examine: (1) network state collection, and (2) network state distribution. First, we compare the impact of active and passive OpenFlow network state collection methods on an SDN load-balancing application running at the controller using key performance indicators that we define. We do this compariso…
Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information-without incurring additional signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling system, and a load balancer-and demon…
An effective packet processing abstraction that leverages software or hardware acceleration techniques can simplify the implementation of high-performance virtual network functions. In this paper, we explore the suitability of SDN switches' stateful forwarding abstractions to model accelerated functions in both software and hardware accelerators, such as optimized software switches and FPGA-based NICs. In particular, we select an Extended Finite State Machine abstraction and demonstrate its suitability by implementing the Linux's iptables interface. By doing so, we provide the acceleration of …
State-of-the-art approaches to design, develop and optimize software packet-processing programs are based on static compilation: the compiler's input is a description of the forwarding plane semantics and the output is a binary that can accommodate any control plane configuration or input traffic. In this paper, we demonstrate that tracking control plane actions and packet-level traffic dynamics at run time opens up new opportunities for code specialization. We present Morpheus, a system working alongside static compilers that continuously optimizes the targeted networking code. We introduce a…
Software-defined networking (SDN) enables advanced operation and management of network deployments through (virtually) centralised, programmable controllers, which deploy network functionality by installing rules in the flow tables of network switches. Although this is a powerful abstraction, buggy controller functionality could lead to severe service disruption and security loopholes, motivating the need for (semi-)automated tools to find, or even verify absence of, bugs. Model checking SDNs has been proposed in the literature, but none of the existing approaches can support dynamic network d…
Network Function (NF) deployments on commodity servers have become ubiquitous in datacenters and enterprise settings. Many commonly used NFs such as firewalls, load balancers and NATs are shallow - i.e., they only examine the packet's header, despite the entire packet being transferred on and off the server. As a result, the gap between moved and inspected data when handling large packets exceeds 20x. At modern network rates, such excess data movement is detrimental to performance, hurting both the average and 90% tail latency of large packets by up to 1.7x. Our thorough performance analysis i…
