Insensitive load balancing in data networks

In order to ensure the optimal load balancing in telecommunications network, the multicast routing flow-based model has been improved in the direction of implementing the multipath packet transmission strategy. It is suggested to split the original multicast flow into a set of subflows, for each of which an optimal tree is built as a multicast route. The problem of multipath multicast routing is presented in an optimization form, contributing to the most effective load balancing in the network. The application of the proposed model of multipath multicast routing allowed to improve the upper threshold of traffic congestion on the average from 17 to 25% compared to a singlepath solution.

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.

Network architectures with overlapping cells from more high altitude platforms may provide more reliable services with broader bandwidths than architectures with a single high altitude platform if an adequate load balancing mechanism is in place. Some network architectures and load balancing mechanisms have been proposed recently, but still there is an open space for further improvements in this area. This paper is built on the results provided by the former researchers and presents a contribution in a form of requirements for an optimal packet-based load balancing mechanism for such network architectures, from the point of utilization of the available bandwidth for the given sites.

This paper considers the problem of paging under the assumption that the sequence of pages accessed is generated by a Markov chain. We use this model to study the fault-rate of paging algorithms. We first draw on the theory of Markov decision processes to characterize the paging algorithm that achieves optimal fault-rate on any Markov chain. Next, we address the problem of devising a paging strategy with low fault-rate for a given Markov chain. We show that a number of intuitive approaches fail. Our main result is a polynomial-time procedure that, on any Markov chain, will give a paging algorithm with fault-rate at most a constant times optimal. Our techniques show also that some algorithms that do poorly in practice fail in the Markov setting, despite known (good) performance guarantees when the requests are generated independently from a probability distribution.

In this paper, we propose an optimal video streaming framework using retransmissions based on the theory of Markov decision processes (MDP). Using MDPs, we calculate average reward optimal policies so as to maximize the on time delivery probability of video frames in the transmission window while conforming to network-imposed available bandwidth constraints. The effectiveness of the proposed approach is validated by comparing the results with theoretically obtained rate-distortion curves corresponding to the optimal policies by using the distortion model in K. Stuhlmuller et al., (2000)

Planning under time constraints in stochastic domains

From the Publisher: Dynamic Routing in Broadband Networks focuses on routing in broadband networks based on MPLS (Multiprotocol Label Switching) and ATM (Asynchronous Transfer Mode). The routing methods are based on the theory of Markov decision processes which forms a very accurate framework for on-line route optimization. The author shows the issue of performance optimization and scalability with respect to dynamic routing of logical connections in broadband networks. The methods used are applicable to routing virtual path connections (VPC) and virtual channel connections (VCC) in ATM networks as well as label switched paths (LSP) in MPLS networks. Simulation results and a performance comparison with reference routing are given for the different schemes.

Queueing models are frequently helpful in the analysis and control of communication, manufacturing, and transportation systems. The theory of Markov decision processes and the inductive techniques of dynamic programming have been used to develop normative models for optimal control of admission, servicing, routing, and scheduling of jobs in queues and networks of queues. We review some of these models, beginning with single-facility models and then progressing to models for networks of queues. We emphasize the use of induction on a sequence of successive approximations of the optimal value function (value iteration) to establish the form of optimal control policies.

Nowadays, web servers have to respond to the huge amount of requests; therefore, balancing the load between server replicas becomes vital and critical. Conventional load balancers have been dedicated hardware that is susceptible being a single point of failure, vendor-dependent and not flexible. In this work, we propose a flexible and programmable scheduling algorithm that ponders different type of requests, various server processing capacity based upon their CPU, memory usage, I/O rate, and a number of connections. This algorithm uses optimization algorithm, considering not only load of servers but also network congestion. The proposed algorithm includes four main phases. The first two phases have the responsibility of creating sub-topology that causes to increase performance and scalability of the proposed algorithm by reduction of overall solution space. In the third phase, the joint optimal network and server-side load balancing approach are performed, and in the last phase, the post-processing is performed in order to select the server, creates a path, and injects appropriate flow table entries on the OpenFlow switches. The evaluation results show that the dynamic load balancing algorithm significantly decreases response-time whereas increases throughput, in comparison with the existing load balancing algorithms.

We consider in this study dynamic control policies for slotted Aloha random access systems. New performance bounds are derived when random access is combined with power control for system optimization, and we establish the existence of optimal control approaches for such systems. We analyze throughput and delay when the number of backlogged users is known, where we can explicitly obtain optimal policies and analyze their corresponding performance using Markov Decision Process (MDP) theory with average cost criterion. For the realistic unknown-backlog case, we establish the existence of optimal backlog-minimizing policies for the same range of arrival rates as the ideal known-backlog case by using the theory of MDPs with Borel state space and unbounded costs. We also propose suboptimal control policies with performance close to the optimal without sacrificing stability. These policies perform substantially better than existing Certainty Equivalence controllers.

The issue of optimal load balancing has been raised in several important engineering fields such as the file management in a data base, the job control in a multiple processor system or the flow assignment in a packet switched data network. By modeling the subsystems as multiple GI/G/1 queues and applying known approximations and decomposition methods, the system mean response time is expressed as a function of the load allocation. To find the optimal load balancing, the convexity property is first studied with respect to the type of the arrival process of the system. We give then an efficient algorithm which finds for the convex case the global and for the non‐convex case a local minimum. Some important properties of the multiple Gl/G/1 queueing system are revealed with several numerical examples.

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.

Sketches have been widely used to record traffic statistics using sub-linear space data structure. Most sketches focus on the traffic estimation of elephant flows (i.e., heavy hitters) due to their importance to many network optimization tasks, e.g., traffic engineering and load balancing. In fact, the information of aggregate mice flows (e.g., all the mice flows with the same source IP) is also crucial to many security-associated tasks, e.g., DDoS detection and network scan detection. However, the previous solutions, e.g., measuring each individual flow or using multiple sketches for independent measurement tasks, will result in worse estimation error or higher computational overhead. To conquer the above disadvantages, we propose an accurate traffic measurement framework with multiple filters, called Sketchtree, to efficiently measure both elephant flows and aggregate mice flows. These filters in Sketchtree are organized in a hierarchical manner, and help to alleviate the hash collision and improve the measurement accuracy, as the number of flows through hierarchical filters in turn will be decreased gradually. We also design some mechanisms to improve the resource utilization efficiency. To validate our proposal, we have implemented Sketchtree and conducted experimental evaluation using real campus traffic traces. The experimental results show that Sketchtree can increase the processing speed by 100 percent, and reduce the measurement error by over 30 percent compared with state-of-the-art sketches.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network

Online advertising has motivated interest in online selection problems. Displaying ads to the right users benefits both the platform (e.g., via pay-per-click) and the advertisers (by increasing their reach). In practice, not all users click on displayed ads, while the platform’s algorithm may miss the users most disposed to do so. This mismatch decreases the platform’s revenue and the advertiser’s chances to reach the right customers. With this motivation, we propose a secretary problem where a candidate may or may not accept an offer according to a known probability p. Because we do not know the top candidate willing to accept an offer, the goal is to maximize a robust objective defined as the minimum over integers k of the probability of choosing one of the top k candidates, given that one of these candidates will accept an offer. Using Markov decision process theory, we derive a linear program for this max-min objective whose solution encodes an optimal policy. The derivation may be of independent interest, as it is generalizable and can be used to obtain linear programs for many online selection models. We further relax this linear program into an infinite counterpart, which we use to provide bounds for the objective and closed-form policies. For [Formula: see text], an optimal policy is a simple threshold rule that observes the first [Formula: see text] fraction of candidates and subsequently makes offers to the best candidate observed so far. Funding: Financial support from the U.S. National Science Foundation [Grants CCF-2106444, CCF-1910423, and CMMI 1552479] is gratefully acknowledged.