Load balancing and control for distributed World Wide Web servers

An integrated bandwidth allocation and admission control framework for the support of heterogeneous real-time traffic in class-based IP networks

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

The emerging IEEE 802.11e standard for wireless local area networks (WLANs) has been proposed to support quality of service (QoS) by assigning different channel access parameters (CAPs) to different access categories (ACs). As an important part of QoS, an admission control scheme is required to maximally utilize the wireless medium resources and to efficiently admit the upcoming real time traffic while not compromising the QoS of existing traffic. In this paper, we propose a novel admission and congestion control scheme which obtains the admission control parameters through existing analytical model and traffic QoS requirements. It then dynamically updates the CAPs based on periodical monitoring of current channel conditions. Through numerical analysis and extensive simulation, results show that such a scheme could provide the guaranteed QoS for admitted real-time traffic in terms of guaranteed throughput achievement, bounded maximum delay and bounded maximum dropping rate while maintaining good channel utilization

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.

Traditional load balancer has no flexibility to manage growing network traffic in real time and is expensive too. Software-defined networking (SDN) is a promising solution for handling today’s growing network architecture. To address issues such as network congestion and overloading, service providers use multiple replicas in the server cluster to provide the same services where network virtualization and effective load balancing is very important. This work proposes the implementation of real-time traffic management strategy that distributes the network requests among multiple servers based on a fuzzy logic. The fuzzy membership characteristic that affects the performance parameters of server load has been analyzed, and load state of virtual servers in real-time is evaluated through fuzzy logic. Considering major parameters of Web server (CPU, RAM and bandwidth utilization) as an input of fuzzy system, the clients requests are forwarded to the server having minimum load at real time. The proposed load balance algorithm written in Python is simulated on Mininet, and performance of each Web servers is compared with existing round robbin (RR) and least connections (LC) load balancing schemes. The results demonstrate that the proposed scheme has improved response time and higher throughput as compared to the other load balance solutions.KeywordsSoftware-defined networkingOpenFlowLoad balancerServer cluster

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

Recent years have seen rapid growth of online services that rely on large-scale server clusters to handle high volume of requests. Such clusters must adaptively control the CPU utilizations of many processors in order to maintain desired soft real-time performance and prevent system overload in face of unpredictable workloads. This paper presents DUC-LB, a novel distributed utilization control algorithm for cluster-based soft real-time applications. Compared to earlier works on utilization control, a distinguishing feature of DUC-LB is its capability to handle system dynamics caused by load balancing, which is a common and essential component of most clusters today. Simulation results and control-theoretic analysis demonstrate that DUC-LB can provide robust utilization control and effective load balancing in large-scale clusters.

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker's idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.

We look at a number of policies for managing a file system cache in a Web server. Traces from NCSA World Wide Web (WWW) server are used in this study. We study several caching policies for improving the overall performance of such a server and show that request response time can be improved by some of the replacement policies that take size of the request into account. It is shown that least frequently used (LFU) performs well when cache sizes are small (<16 MB) and SpacexAge replacement policy performs consistently better than LRU. We show that previously proposed cache sharing policies improve performance significantly in clustered Web servers.

Websites that have high traffic rates will have a large load of access. If the web server uses a single server topology, the work of the web server is getting heavier in receiving the request, so that problems such as crashes or downs arise due to the many requests from clients.From the test results, it was found that the load balance response time was slower than thatsingle web server. The highest request loss on the load balance namely the number of clients 500 with 10000 requests amounting to 4.058% while the single web server on the number 500 clients with 10 000 requests for 10.622%. The value of throughput load balance and a single web server have the same average value, but load balance is able to equalize the throughput on all clients anda single web server is incapable of doing that. Banana Pi device used as a web The server is able to handle simulated DoS attacks on the HTTP protocol with a maximum value of use The CPU is 52% while using a load balance that makes 2 web servers work together able to reduce CPU usage by 19%.

We propose a novel pricing incentive mechanism based on multi-satges traffic classification methodology supporting load balancing and allocating network resource efficiently for QoS-enabled networks in this paper. We integrate the pricing with QoS routing and present a novel pricing incentive mechanism meeting the QoS requirements of different applications. This mechanism provides an equitable pricing incentive for applications according to their service requests. A novel multi-stages traffic classification methodology that brings together the benefits of port mapping, signature matching and flow character classification techniques is motivated by variety of network activities and their QoS requirements of traffic. We study the pricing and different levels of services in detail and integrate admission control scheme and load balancing in our framework. By theoretical analysis and extensive simulations, we prove its effectiveness in making traffic load balance and providing an incentive for users to utilize network resources to users’ satisfaction.

Distributed multimedia applications (DMA), such as video-on-demand (VoD) require dynamic quality-of-service (QoS) guarantee from end-user machines for their continuous multimedia streams. The QoS requirements and improvement in job execution time can be achieved with the aid of proper load balancing among the machines. An effective way to achieve load balance is to link up these end-user machines with multiple potential heterogeneous idle or under-loaded clustered-servers and allow these servers with idle retrieval bandwidth to help out end-user machines that are low power or temporarily overloaded (bottleneck). The clustered VoD servers approach provides scalability and availability compared to a single video server. In this paper we design and implement a new clustered-server-client architecture for multimedia VoD applications along with a new REMS+VoD algorithm. Rigorous simulation for the algorithm has been performed by varying both the number of video-requests (sessions) and the number of potential servers. The simulation results show that our approach provides: (1) effective load balancing; (2) scalability; and (3) availability in a VoD system

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

Effective load balancing is critical in ensuring optimal resource utilization, reducing latency, and improving the overall performance of distributed systems. This report commences with a comprehensive literature review on existing load-balancing algorithms, examining their methodologies, strengths, and limitations within various computing environments, including cloud computing, data centers, and network traffic management. Despite significant advancements in this field, the dynamic nature of distributed systems, coupled with the ever-increasing demand for efficient data processing, poses ongoing challenges. In response, this study proposes a novel load-balancing algorithm to address these contemporary challenges. The approach leverages dynamic and hybrid load balancing, distinguishing it from existing methods. Detailed analysis show that this approach offers improved scalability, response time, and resource allocation efficiency, particularly in scenarios characterized by highly volatile workloads. This report details the conceptual framework of the proposed algorithm and offers a concise understanding of how cloud-based load balancers can be optimized to delegate compute and focus on just routing.