Locality-aware request distribution in cluster-based network servers

We consider cluster-based network servers in which a front-end directs incoming requests to one of a number of back-ends. Specifically, we consider content-based request distribution: the front-end uses the content requested, in addition to information about the load on the back-end nodes, to choose which back-end will handle this request. Content-based request distribution can improve locality in the back-ends’ main memory caches, increase secondary storage scalability by partitioning the server’s database, and provide the ability to employ back-end nodes that are specialized for certain types of requests. As a specific policy for content-based request distribution, we introduce a simple, practical strategy for locality-aware request distribution (LARD). With LARD, the front-end distributes incoming requests in a manner that achieves high locality in the back-ends’ main memory caches as well as load balancing. Locality is increased by dynamically subdividing the server’s working set over the back-ends. Trace-based simulation results and measurements on a prototype implementation demonstrate substantial performance improvements over state-of-the-art approaches that use only load information to distribute requests. On workloads with working sets that do not fit in a single server node’s main memory cache, the achieved throughput exceeds that of the state-of-the-art approach by a factor of two to four. With content-based distribution, incoming requests must be handed off to a back-end in a manner transparent to the client, after the front-end has inspected the content of the request. To this end, we introduce an efficient TCP handoflprotocol that can hand off an established TCP connection in a client-transparent manner.

Cloud computing is an emerging paradigm in the computer industry where the computing is moved to a cloud of computers. Cloud computing is a way to increase the capacity or add capabilities dynamically without investing in new infrastructure, training new personnel, or licensing new software. This paper is focused on the load balancing issues of cloud computing and techniques to overcome the waiting time and turnaround time. Load Balancing is done with the help of load balancers where each incoming request is redirected and is transparent to client who makes the request. Based on predetermined parameters, such as availability or current load, the load balancer uses various scheduling algorithm to determine which server should handle and forwards the request on to the selected server.

This study proposes a unified, Python-based simulation framework to enhance the performance and security of cloud-integrated computer networks. The framework concurrently addresses two critical aspects, load balancing and intrusion detection, within a single reproducible environment. Using the CICIDS2017 dataset, a Random Forest classifier was used to detect a wide range of network attacks with high accuracy. To simulate realistic traffic behavior, synthetic data were generated for performance metrics such as latency, throughput, and packet loss. Load balancing is evaluated using round-robin and random assignment strategies across virtual servers, illustrating the trade-offs between uniformity and randomness in the request distribution. The experimental results demonstrated a classification accuracy of 99.79%, with precision and recall metrics supporting the robustness of the selected model. Feature importance analysis highlights the key indicators of anomalous traffic, and confusion matrices and precision-recall curves validate the detection performance. Additionally, the simulated network KPIs provide a scalable approximation of the Quality of Service under varying load scenarios. The proposed research is the only one that suggests an integrated and data-driven approach to fill the gap documented in previous studies, where network security and resource optimization are frequently studied independently. The proposed framework provides a convenient basis for additional scholarly and industrial investigations in the field of secure cloud networking.

Grids are an emerging infrastructure providing distributed access to computational and storage resources. Handling many incoming requests at the same time and distributing the workload efficiently is a challenge which load balancing algorithms address. Current load balancing implementations for the Grid are central in nature and therefore prone to the single point of failure problem. This paper introduces two distributed artificial life-inspired load balancing algorithms using Ant Colony Optimization and Particle Swarm Optimization. Distributed load balancing stands out as a robust algorithm in regard to any topology changes in the network. The implementation details are given and evaluation results show the efficiency of the two distributed load balancing algorithms.

To improve response time of a Web site, one replicates the site on multiple servers. The effectiveness of a replicated server system will depend on how the incoming requests are distributed among replicas. A large number of load‐balancing strategies for Web server systems have been proposed.In this paper we describe a testbed that can be used to evaluate the performance of different load‐balancing strategies. The testbed uses a general architecture which allows different load‐balancing approaches to be supported easily. It emulates a typical World Wide Web scenario and allows variable load generation and performance measurement. We have performed some preliminary experiments to measure the performance of a few policies for load balancing using this testbed. Copyright © 2003 John Wiley & Sons, Ltd.

The rapid growth of e-commerce has brought about challenges in managing traffic loads and ensuring the availability of database servers. One critical issue is what happens when the main database server fails, potentially leading to downtime or data loss. This research aims to address these problems by optimizing server resource distribution and implementing data replication across multiple database servers. The goal is to enhance system efficiency, prevent server overloads, ensure data availability, and minimize downtime or data loss when server failures occur.This research utilizes a multi-stage methodology. First, data collection was conducted to gather relevant information. In the second stage, a needs analysis was performed to identify system requirements. The third stage involved designing the system architecture, including the creation of entity-relationship diagrams (ERD), flowcharts, and network topologies. In the final stage, the system was implemented through database replication and the application of failover methods.The results demonstrate that the failover mechanism operates effectively, ensuring that data is successfully replicated across all database servers. This confirms that the system achieves high availability and minimizes the risk of data loss or service downtime. The implications of this research provide a reliable solution for improving the resilience and performance of e-commerce systems, particularly in managing database server failures.

A number of technology and workload trends motivate us to consider a new request distribution and load balancing strategy for streaming media clusters. First, in emerging media workloads, a significant portion of the content is short and encoded at low bit rates. Additionally, media workloads display a strong temporal and spatial locality. This makes modern servers with gigabytes of main memory well suited to deliver a large fraction of accesses to popular files from memory. Second, a specific characteristic of streaming media workloads is that many clients do not finish playing an entire media file which results from the browsing nature of a large fraction of client accesses. In this paper, we propose and evaluate two new load-balancing strategies for media server clusters. The proposed strategies, FlexSplit and FlexSplitLard aim to efficiently utilize the combined cluster memory by exploiting specific media workload properties by "tuning" their behavior to media file popularity changes. The ability of the proposed policies to self-adapt to changing workloads across time while maintaining high performance makes these strategies an attractive choice for load balancing in media server clusters.

Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining physical data centers and servers, you can access technology services, such as computing power, storage, and databases, on an as-needed basis from a cloud provider like Amazon Web Services (AWS), Microsoft Azure, Google cloud. Cloud computing is one of the most growing technologies. The fundamental idea behind cloud computing is to distribute an array of computing services by unifying and scheduling a pool of computing resources, thereby minimizing the burden on the users and helping them focus on their core businesses. These computing resources are hosted on virtual hosts and distributed on-demand to the users by cloud service providers. For efficient resource utilization, systematic load balancing of incoming user traffic across virtual hosts is imperative. Aim of this paper is to design and implement Network load balancer with cross zone feature which balance incoming user requests and avoid the problem of server down in real scenario. Elastic Load Balancer automatically equally distribute incoming traffic to the available EC2, targets, like Amazon EC2 instances, IP addresses containersand Lambda functions. Load balancer effectively distribute load equally on available server and AZ to improve fault tolerance and availability. It can manage the differing load of your application traffic in one Availability Zone or over different Availability Zones.

To deliver high performance and reliability to the mobile users in accessing mobile cloud services, the major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures. In such a heterogeneous network ecosystem, multiple cloudlets from different service providers coexist. However, to meet the stringent latency requirements of computation-intensive and mission-critical applications, overloaded cloudlets can offload some of the incoming job requests to their relatively under-loaded neighboring cloudlets. In this paper, we propose a novel economic and non-cooperative game-theoretic model for load balancing among competitive cloudlets. This model aims to maximize the utilities of all the competing cloudlets while meeting the end-to-end latency of the users. We characterize the problem as a generalized Nash equilibrium problem and investigate the existence and uniqueness of a pure-strategy Nash equilibrium. We design a variational inequality based algorithm to compute the pure-strategy Nash equilibrium. We show that all the competing cloudlets are able to maximize their utilities by employing our proposed Nash equilibrium computation offload strategy in both under- and overloaded conditions. We also show through numerical evaluations that our load balancing model outperforms some of the existing game-theoretic load balancing frameworks, especially in a highly overloaded condition.

Edge computing servers have been widely deployed in recent years to address the requirements of diverse tasks that are sensitive to delays and computationally intensive. However, due to their independent nature and uneven distribution of service requests, certain clusters may be relatively idle, while others may be overloaded. This situation can result in increased latency for certain tasks, and it prevents the full utilization of resources in the edge clusters. To mitigate this problem, we design and implement a prototype testbed for task offloading, aimed at achieving computing and network convergence. This testbed facilitates collaboration among multiple edge computing clusters. We construct multiple clusters using Intel NUC mini computers and incorporate key enabling technologies into the system. We assess the testbed's performance by employing multiple video processing services that require low latency and high computational capacity. In scenarios with uneven service requests, load balancing can be achieved across the edge computing clusters, resulting in reduced response latency for user tasks.

A complex repairable computer-based test (CBT) network system studied in this paper consists of three client computers, a load balancer, two database servers, with the centralized server structured in a series configuration. Subsystem 1 consists of three homogeneous clients arranged in parallel configuration, subsystem 2 comprises a load balancer, subsystem 3 is comprised of two distributed homogeneous database servers in parallel arrangement and subsystem 4 consists of a centralized database server. Through the transition diagram, the first-order differential equations are derived. The model has solved using supplementary variables, with implications of Laplace transforms. Reliability metrics of system effects such as availability, reliability, MTTF, MTTF sensitivity, and the cost function is estimated to see the impact of failure and repair patterns on reliability evaluations. The results of this study indicated that system performance could be improved when the copula repair is employed.

Any micro service-based programme, including alumni management software, must include load balancing. The effective use of resources is made possible by load balancing, which also ensures that application performance is unaffected by unexpected increases in requests or user usage. In an alumni management application, load balancing can be achieved with a combination of techniques such as round-robin scheduling, weighted load balancing, and dynamic scheduling. These techniques help to ensure that the application remains up and running, even during peak usage times. Additionally, load balancing can be used to ensure users get the best possible experience when using the application. By distributing traffic among multiple back-end services, users can expect faster response times and more reliable performance.Load balancing in a micro services-based alumni management application is a way of distributing incoming web traffic across multiple back end servers in order to efficiently manage traffic, reduce latency, and improve overall performance. Load balancing works by distributing incoming requests across a cluster of servers, and can be done either statically or dynamically. Static load balancing assigns each request to a specific server, while dynamic load balancing uses algorithms to decide which server should handle each request. In the alumni management application, load balancing is important for ensuring that requests are handled efficiently and that the system is not overwhelmed by too many requests. This is especially important when multiple users are accessing the application simultaneously. By distributing requests across multiple servers, the application can remain responsive and reliable even under heavy load.

New designs of the Web switches must incorporate a client-and-server-aware adaptive dispatching algorithm to be able to optimize multiple static and dynamic services providing quality of service and service differentiation. This paper presents such an algorithm called FNRD (Fuzzy-Neural Request Distribution) which operates at layer-7 of the OSI protocol stack. This algorithm assigns each incoming request to the server with the least expected response time estimated using the fuzzy approach. FNRD has ability for learning and adaptation by means of a neural network feedback loop. We demonstrate through the simulations that our dispatching policy is more effective than state-of-the-art layer-7 reference dispatching policies CAP (Client-Aware Policy) and LARD (Locality Aware Request Distribution).

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

This paper describes an overload control scheme for web servers which integrates admission control and load balancing. The admission control mechanism adaptively determines the client request acceptance rate to meet the web servers' performance requirements while the load balancing or client request distribution mechanism determines the fraction of requests to be assigned to each web server. The scheme requires no prior knowledge of the relative speeds of the web servers, nor the work required to process each incoming request. Copyright © 2002 John Wiley & Sons, Ltd.