Predictive Load Balancing in Cloud Computing Environments Based on Ensemble Forecasting

Sommer, Matthias; Klink, Michael J.; Tomforde, Sven; Hähner, Jörg

doi:10.1109/icac.2016.16

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…Load Balancing is the major challenge in any network or system because it adversely affects three main important parameters of the system, i.e., performance, functionality and cost in cloud [6]. The main purpose of resource management technique s is to effectively utilize all resources at minimum cost.…”

Section: Purpose Of Load Balancingmentioning

confidence: 99%

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Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

Dhaliwal¹,

Naseem²,

Lawaye³

et al. 2018

IJET

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The rapid advancement of the internet has given birth to many technologies. Cloud computing is one of the most emerging technology which aim to process large scale data by using the computational capabilities of shared resources. It gives support to the distributed parallel processing. Using cloud computing, we can process data by paying according to its uses which eliminates the requirement of device by individual users. As cloud computing grows, more users get attracted towards it. However, providing an efficient execution time and load distribution is a major challenging issue in the distributed systems. In our approach, weighted round robin algorithm is used and benefits of Fibonacci sequence is combined which results in better execution time than static round robin. Relevant virtual machines are chosen and jobs are assigned to them. Also, number of resources being utilized concurrently is reduced, which leads to resource saving thereby reducing the cost. There is no need to deploy new resources as resources such as virtual machines are already available.

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Section: Purpose Of Load Balancingmentioning

confidence: 99%

“…(5) We have demonstrated the working of our proposed load balancing algorithms using Fibonacci numbers. (6) We have performed simulation of the proposed load balancing algorithms and have compared the result with existing algorithm to show the improvement in load balancing.…”

mentioning

confidence: 99%

Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

Dhaliwal¹,

Naseem²,

Lawaye³

et al. 2018

IJET

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“…To reduce the number of SLA violations by migrating VMs before the host is overloaded, a proactive VM migration policy is presented in [12]. This method for the dynamic and anticipatory consolidation of VMs in IaaS cloud data centers uses short-term forecasts of the future utilization of hosts, to proactively detect overloaded hosts.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers

Djennane

Aoudjit

Bouzefrane

2018

2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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Nowadays, cloud computing is an effective solution for providing computing services to consumers. However, data centers that host computing resources are still faced with a misuse of resources and a workload imbalance of servers, where some servers become overloaded while others are underloaded or even idle. This results in performance degradation and resource wastage. The load balancing is a key aspect and has an important role in the management of cloud data centers. It allows an optimal use of the resources and improves the desired Quality of Service (QoS) using optimal methods for allocating resources and distributing workload.In this paper, we propose a load-balancing algorithm that is based on a new parameter called the balance factor of the data center, introduced here, to determine if a data center is imbalanced or not, in order to redistribute the workload equally over all the hosts. To minimize the energy consumption of the data center, our strategy relies on the live migration of virtual machines (VMs) while using a mathematical model. The simulation results, using the CloudSim toolkit, have shown that the energy efficiency can be managed by reassigning VMs to the data-center hosts.

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“…The general design concept as illustrated by Figure 1 is explained in detail in Tomforde et al (2011). Besides robotics, it has been successfully applied to domains such as control of urban traffic lights, see Prothmann et al (2011), adaptation of data communication protocols, see Tomforde et al (2009Tomforde et al ( , 2010, or cloud computing environments, see Sommer et al (2016). However, dealing with robots and robot ensembles opens a new range of challenges , mainly concerned with the human-ensemble interaction mechanisms -which are a major contribution of this article.…”

Section: Observer/controller Architecturementioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

Self Cite

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Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behavior of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework, which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g., by means of adapting to changing goals and conditions. Not only for the sake of generalizability but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organize themselves to fulfill spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimization as well as of the optimization of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

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Predictive Load Balancing in Cloud Computing Environments Based on Ensemble Forecasting

Cited by 19 publications

References 15 publications

Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers

An Organic Computing Approach to Self-Organizing Robot Ensembles

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