A comprehensive survey of load balancing techniques in software-defined network

Continuous integration enables the development of microservices-based applications using container virtualization technology. Container orchestration systems such as Kubernetes, which has become the de facto standard, simplify the deployment of container-based applications. However, developing efficient and well-defined orchestration systems is a challenge. This article focuses specifically on the scheduler, a key orchestrator task that assigns physical resources to containers. Scheduling approaches are designed based on different Quality of Service (QoS) parameters to provide limited response time, efficient energy consumption, better resource utilization, and other things. This article aims to establish insight knowledge into Kubernetes scheduling, find the main gaps, and thus guide future research in the area. Therefore, we conduct a study of empirical research on Kubernetes scheduling techniques and present a new taxonomy for Kubernetes scheduling. The challenges, future direction, and research opportunities are also discussed.

Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G . A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10 $\%$ higher SFC acceptance ratio than the previous counterparts and the typical heuristics.

Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization

Network function virtualization paradigm enables us to implement network functions provided in middleboxes as softwares that run on commodity servers. This paper proposes a backup resource allocation model for middleboxes with considering both failure probabilities of network functions and backup servers. A backup server can protect several functions; a function can have multiple backup servers. We take the importance of functions into account by defining a weighted unavailability for each function. We aim to find an assignment of backup servers to functions, where the worst weighted unavailability is minimized. We formulate the proposed backup resource allocation model as a mixed integer linear programming problem. We prove that the backup resource allocation problem for middlebox with importance is NP-complete. We develop three heuristic algorithms with polynomial time complexity to solve the problem. We analyze the approximation performances of different heuristic algorithms with providing several lower and upper bounds. We present the competitive evaluation in terms of deviation and computation time among the results obtained by running the heuristic algorithms and by solving the mixed integer linear programming problem. The results show the pros and cons of different approaches. With our analyses, a network operator can choose an appropriate approach according to the requirements in specific application scenarios.

Optimization Model for Backup Resource Allocation in Middleboxes With Importance

Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research. 

/pdf/a-survey-of-kubernetes-scheduling-algorithms-4bpdjhy6.pdf

A survey of Kubernetes scheduling algorithms

This paper proposes a backup network design scheme that can determine backup link capacity in practical time. The proposed scheme suppresses the required backup link capacity while providing a guaranteed level of recovery against multiple independent link failures. The conventional scheme is based on robust optimization and suffers from the problem of overestimating the backup link capacity. The proposed scheme addresses the overestimation problem by computing the probabilistic distribution function of required backup link capacity in polynomial time. We formulate the backup network design problem with the proposed scheme as a mixed integer linear programming problem to minimize the total required backup link capacity. We prove that the decision version of backup network design problem is NP-complete. Given that network size will continue to increase, we introduce a heuristic approach of simulated annealing to solve the same problem. Numerical results show that the proposed scheme requires less total backup link capacity than the conventional scheme based on robust optimization.

Backup Network Design Against Multiple Link Failures to Avoid Link Capacity Overestimation

This paper proposes a backup resource allocation model that provides a probabilistic protection for primary physical machines in a cloud provider to minimize the required total capacity. When any random failure occurs, workloads are transferred to preplanned and dedicated backup physical machines for prompt recovery. In the proposed model, a probabilistic protection guarantee is introduced to prevent the cloud provider from capacity overbooking. We apply robust optimization in our model to formulate the backup resource allocation problem as an integer linear programming problem. A simulated annealing heuristic is adopted to solve the same optimization problem when the cloud provider is large. Finally, the results reveal that the required backup capacity depends on the reliability of primary physical machines. Specifically, the more the resources in primary physical machines share backup capacity when the failure probabilities of primary physical machines are sufficiently small, the less capacity is required for backup resource allocation.

Robust Optimization Model for Backup Resource Allocation in Cloud Provider

This paper proposes an optimization model to derive the virtual network function (VNF) allocation of time slots in sequence aiming to maximize the continuous available time of service function chains (SFCs) in a network. The proposed model suppresses service interruptions otherwise created by the unavailability of virtual machines (VMs) and the reallocation of VNFs. The proposed model computes VNF allocation in a series of time slots based on a VM availability schedule, which provides information on the availability of each VM in each time slot. We formulate the proposed model as an integer linear programming (ILP) problem with the goal of maximizing the minimum number of longest continuous available time slots in each SFC. We prove that the decision version of the VNF allocation problem (VNFA) is NP-complete. As the size of ILP problem increases, the problem is difficult to solve in a practical time. We develop a heuristic algorithm to solve the VNFA problem. Numerical results show that the proposed model improves the continuous available time of SFCs compared with existing models, which partially consider VM unavailability or VNF reallocation. We observe that the proposed model together with a consideration of routing reduces the path length of requests. The developed heuristic algorithm is faster than the ILP approach with a limited performance penalty.

Virtual Network Function Allocation to Maximize Continuous Available Time of Service Function Chains With Availability Schedule

This paper proposes a backup resource allocation model for virtual networks with providing a probabilistic protection against multiple facility node failures by jointly considering backup computing and bandwidth resource allocation. Previous work only considers the backup computing resource allocation for virtual nodes regardless of the constraints from network aspects. In this paper, we firstly analyze the types of backup paths required for each virtual link in cases of multiple facility node failures. We describe the substrate link capacity constraints and flow constraints for backup paths, which show the interactions between backup computing and bandwidth resource allocation. In the proposed model, the routings of backup paths are determined when the backup computing resource allocation is determined. By adopting robust optimization with several extensive mathematical operations, we formulate the proposed model as a mixed integer linear programming problem to minimize the total required backup computing capacity. Finally, we show the optimal backup resource allocations for an example cloud provider with and without considering the constraints from network aspects, respectively.

Backup Resource Allocation Model for Virtual Networks with Probabilistic Protection against Multiple Facility Node Failures

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