Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

Lu, Yi; Xie, Qiaomin; Kliot, Gabriel; Geller, Alan; Larus, James R.; Greenberg, Albert

doi:10.1016/j.peva.2011.07.015

Cited by 324 publications

(296 citation statements)

References 9 publications

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“…Join Idle Queue: Join Idle Queue load balancing algorithm [14] is applied for dynamically scalable web services. This technique involves a dispatcher to whom processors informs at the time of their idleness, without interfering with job arrivals.…”

Section: Existing Load Balancing Algorithms In Cloudcomputingmentioning

confidence: 99%

Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey

Kumar¹,

Rana²

2015

IJCA

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Cloud computing is emerging as a new standard model for enabling ubiquitous network access, computing resources, deploying, organizing, and accessing vast distributed computing applications over the network. In cloud computing, Load balancing is one of the main challenges which are required to distribute the workload equally across all the nodes. Load balancing uses services offered by many computer network service provider corporations. Load balancing can be different types like network load, storage capacity, memory capacity and CPU load. Load balancing helps to achieve a high user satisfaction and resource utilization ratio by confirming an efficient and fair allocation of every computing resource. Proper load balancing support in implementing failover, enabling scalability, overprovisioning, and decreases costs associated with document management systems and maximizes the availability of resources. This paper describes a survey of different dynamic load balancing algorithms in the cloud environment with their comparisons on the bases of different load balancing metrics. KeywordsCloud Computing, Load balancing, load balancer, static load balancing, dynamic load balancing algorithm, load balancing metrics.

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Section: Existing Load Balancing Algorithms In Cloudcomputingmentioning

confidence: 99%

Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey

Kumar¹,

Rana²

2015

IJCA

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“…This algorithm uses a queue based memory in which information about the idle processor is kept and if an availability request comes idle process is allotted and it reduces queue length [17] …”

Section: Platform Used-cloud Data Centermentioning

confidence: 99%

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

Yadav¹,

Joshi²,

Verma³

2015

IJCA

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Cloud computing is the most demanding technologies in today's era due to the reduction of cost and complexity of applications, as a result number of users demanding more services of cloud. Thus, for overall cloud performance load balancing in the cloud is an important research area. Cloud handles large number of users as it is working on powerful datacenters. So for better efficiency it has to be featured with reliable load balancer for handling the load in the cloud. Many load balancing algorithms have been proposed for the better management of user request among cloud resources. This paper presents our research with a survey of various existing load balancing algorithms along with state of the art and its research challenges.

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“…Often load-balancing policies consider web server systems as a target [11,26], where one of the most important result is to bound the maximum response time that the clients are exposed to [19]. Load-balancing strategies can be guided by many different purposes, for example geographical [2,33], driven by the electricity price to reduce the datacenter operation cost [15], or specifically designed for cloud applications [5,23,24].…”

Section: Related Workmentioning

confidence: 99%

“…Despite the fact that load-balancing techniques have been widely studied [5,23,24,29], state-of-the-art load-balancers forward requests based on metrics that cannot discriminate between a replica that is avoiding overload by not executing the optional code and a replica that is not subject to overload. Therefore, the novelty of our problem consists in finding a brownout-compliant load-balancing technique that is aware of each replica's self-adaptation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Control-theoretical load-balancing for cloud applications with brownout

Dürango

Dellkrantz

Maggio

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -copies of the applications having the same functionalities -for redundancy and adding a load-balancer to direct incoming traffic.However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a selfadaptive application simulator, together with some state-of-theart solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

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Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

Cited by 324 publications

References 9 publications

Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey

Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

Control-theoretical load-balancing for cloud applications with brownout

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