Stochastic Load Balancing for Virtual Resource Management in Datacenters

Yu, Lei; Chen, Liuhua; Cai, Zhipeng; Shen, Haiying; Liang, Yi; Pan, Yi

doi:10.1109/tcc.2016.2525984

Cited by 111 publications

(53 citation statements)

References 29 publications

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“… Proactive Methods: In proactive load balancing methods, the decision of load balancing is done based on prediction of resource utilization [10]. The author Yu, Lei et al [11] considered stochastic characteristics for predicting resource demand and proposed a stochastic load balancer. Prediction based load balancing schemes sometimes proved to be inefficient because of inaccurate predictions for bursty traffic patterns.…”

Section: A Load Balancingmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advancement of Auto-Scaling in LTE M2M Communication.

2019

IJITEE

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Lately Machine to Machine (M2M) Communication has gathered huge research interest because of its peculiar nature of communication without any or less human intervention. With the increase in wide variety of devices and application, there is huge change in traffic patterns of Machine Type Communication (MTC) system. Existing traditional Long-Term Evolution (LTE) network will not be able to handle these growing demands of the bandwidth and network availability. There are some challenges in the existing network like latency, scalability, reliability, interference and delay, which degrade the Quality of Service (QoS). Hence to address these issues would require some advanced network resource management capabilities such as Network Functions Virtualization (NFV), Software Defined Networking (SDN). These technologies would help the operators to provide efficient services to consumer. In this literature we present survey of auto-scaling the resources required for LTE communication using SDN, NFV and Machine Learning (ML) for facilitating MTC, along with its requirements, existing work and challenges. This paper first describes in brief about SDN/NFV and its limitations. Then review the existing work and their applicability to MTC along with open problems and finally some future research in this area.

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Section: A Load Balancingmentioning

confidence: 99%

“…Yu, Lei et al [11] Considered stochastic characteristics for predicting resource demand and proposed a stochastic load balancer.…”

Section: Load Balancing Issuesmentioning

confidence: 99%

Recent Advancement of Auto-Scaling in LTE M2M Communication.

2019

IJITEE

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show abstract

“…Resource allocation and resource scheduling are the key technologies to manage the data centers, which contribute a great deal to lowering the carbon emission, improving the resource utilization, and obtaining load balancing for the data centers [12][13][14]. In the cloud environment, the main goal of resource allocation is to optimize the number of active physical machines (PMs) and make the workloads of the running PMs distributed in a balanced manner, to avoid bottlenecks and overloaded or low-loaded resource usage [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Resource Allocation for Load Balancing in Fog Environment

Qing

et al. 2018

Wireless Communications and Mobile Computing

157

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Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

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“…Most importantly, there is a need for a distributed intelligent platform at the edge that manages distributed computing, networking, and storage resources. In Fog networks, however, making an optimal distribution decision faces a lot of challenges due to uncertainties associated with task demands and resources available at the Fog nodes [11] and the wide range of computing power capacities of nodes. Furthermore, the distribution decision should also consider the communication delay between nodes, which can lead to prolonged processing time [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm

Baek

Kaddoum

Garg

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.

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Stochastic Load Balancing for Virtual Resource Management in Datacenters

Cited by 111 publications

References 29 publications

Recent Advancement of Auto-Scaling in LTE M2M Communication.

Recent Advancement of Auto-Scaling in LTE M2M Communication.

Dynamic Resource Allocation for Load Balancing in Fog Environment

Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm

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