Load Balancing in a Cluster Computer

Werstein, P.; Situ, Hailing; Huang, Zhiyi

doi:10.1109/pdcat.2006.77

Cited by 49 publications

(40 citation statements)

References 4 publications

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“…In [15] author proposes an algorithm based on the least connection mechanism which itself is a part of dynamic scheduling algorithms.the algorithm runs dynamically i.e. on the run time and tries to count the number of connections for each server dynamically to estimate the load.Author used the concept of load balancer in his proposal which records the connection number of each server.when ever a new connection is allocated, the total number of existing connections increases which increases the load and decreases when the connection is terminated.…”

Section: Connection Mechanismmentioning

confidence: 99%

QOS Differentiation of Various Cloud Computing Load Balancing Techniques

Hans¹,

Singh²,

Kumar³

et al. 2015

CAE

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With an increase in the demands the Cloud computing has become one of the ongoing scalable approach to fulfill the cloud based neccesties. The biggest advantage of the cloud computing is the ability to overcome the infrastructural challenges those are earlier faced by other technologies. Since the technology is new, therefore the development of the whole structure is not so efficient. It does have a lot of issues on which various scientists and others are working on. Scheduling, load balancing, fault tolerance, etc. are various challenges faced by cloud computing. For this purpose various techniques and algorithms have been proposed. In this paper discussion will be done on the issue of load balancing in cloud computing and different types of load balancing techniques have been reviewed and a comparative analysis have been made among all the existing techniques.

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Section: Connection Mechanismmentioning

confidence: 99%

QOS Differentiation of Various Cloud Computing Load Balancing Techniques

Hans¹,

Singh²,

Kumar³

et al. 2015

CAE

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“…Without considering other important elements this does not sufficiently reflect the exact conditions. Paul Werstein et al [2] develop a new load balancing algorithm to measure CPU queue length, CPU utilization, memory utilization and network traffic from a homogenous distributed cluster environment. Dynamic thresholds of these elements (except memory utilization) are calculated and used to classify nodes into categories of idle, high, low or normal.…”

Section: Related Workmentioning

confidence: 99%

“…The first LB module is designed to keep track of node workload, analogous to the idea of system load balancing. Essentially, load balancing serves to balance a cluster system's workload by transferring processes to idle or more lightly loaded nodes in order to maximize the throughput and minimize the total response time [2]. In distributed clustering, users are directly connected to one of the processing nodes, where each node itself is responsible to make task scheduling decisions.…”

Section: Introductionmentioning

confidence: 99%

“…A referral node is tasked to direct clients' connection request to associate nodes based on their current workload. Among the many load balancing algorithm policies found in the literature, we refer to a set of policies [2] in our implementation: the load estimation policy that specifies types of load data to be collected and analyzed; information exchange policy that defines ways to exchange load information among nodes; process transfer policy that examines if a node is lightly loaded or heavily loaded based on pre-defined or dynamic thresholds; selection policy that decides which process is to be transferred; location policy that chooses the most appropriate node with minimum workload to run the process.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Load Balancing and Network Adaptive Virtual Storage Service for Mobile Appliances

Ong¹,

Lim²

2011

Journal of Information Processing Systems

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Abstract-With the steady growth of mobile technology and applications, demand for more storage in mobile devices has also increased. A lightweight block-level protocol, Internet Advanced Technology Attachment (iATA), has been developed to deliver a costeffective storage network solution for mobile devices to obtain more storage. This paper seeks to contribute to designing and implementing Load Balancing (LB), Network Monitoring (NM) and Write Replication (WR) modules to improve the protocol's scalability and data availability. LB and NM modules are invoked to collect system resources states and current network status at each associate node (server machine). A dynamic weight factor is calculated based on the collected information and sent to a referral server. The referral server is responsible to analyze and allocate the most ideal node with the least weight to serve the client. With this approach, the client can avoid connecting to a heavily loaded node that may cause delays in subsequent in-band I/O operations. Write replication is applied to the remaining nodes through a WR module by utilizing the Unison file synchronization program. A client initially connected to node IP A for write operations will have no hindrances in executing the relevant read operations at node IP B in new connections. In the worst case scenario of a node crashing, data remain recoverable from other functioning nodes. We have conducted several benchmark tests and our results are evaluated and verified in a later section.

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“…the time when a task arrives for processing. CPU load balancing [9] and [10] can be implemented as static scheme [2] or dynamic M. Murugananth is with the Department of Research and Development/Scientific Services, Tata Steel limited, Jamshedpur, India. Phone: +919934302578 balancing system [2].…”

Section: Introductionmentioning

confidence: 99%

Deploying CPU Load Balancing in the Linux Cluster Using Non-Repetitive CPU Selection

Jameel¹,

Murugananth²,

Chingtham³

2009

IJCEE

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Abstract-Maintaining load balancing in a computing cluster is an evident problem in distributed systems and research in this field is not new. The challenges in designing the load balancing algorithms are immense. This paper lists some of those challenges in the design of CPU load balancing algorithm and provides solutions to some of them. The algorithm considers one node in the cluster as the Master Server and another as the Load Balancer. The master server maintains the CPU and IP information of each machine. The nodes in the cluster send their CPU status and IP information to the master server after every 30 seconds. The implementation solves "readers-writers" problem exclusively using sockets. If a number of requests are sent before the next central database update, the load balancer selects other less busy nodes in the cluster. This ensures that all nodes are allocated with the new tasks coming from remote systems, thereby maintaining a load balance among the CPUs. This implementation is highly fault tolerant and reliable, guaranteeing a high probability of task completion. Results show that this scheme handles task allocation in much optimized way and with fewer overheads. The implementation can handle CPUs ranging in numbers from 1 to 255.

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Load Balancing in a Cluster Computer

Cited by 49 publications

References 4 publications

QOS Differentiation of Various Cloud Computing Load Balancing Techniques

QOS Differentiation of Various Cloud Computing Load Balancing Techniques

Dynamic Load Balancing and Network Adaptive Virtual Storage Service for Mobile Appliances

Deploying CPU Load Balancing in the Linux Cluster Using Non-Repetitive CPU Selection

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