Implementation of a Network Based Cloud Load Balancer

Ristov, Sasko; Gušev, Marjan; Cvetkov, Kiril; Velkoski, Goran

doi:10.15439/2014f454

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…We have developed a naive implementation of the proposed L3B architecture, which unfortunately did not yield the expected results [21]. The successful implementation of redeveloped balancer according to the same L3B architecture was presented in [5].…”

Section: Sasko Ristov and Kiril Cvetkov And Marjan Gusevmentioning

confidence: 99%

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Implementation of a Horizontal Scalable Balancer for Dew Computing Services

Ristov¹,

Cvetkov²,

Gušev³

2016

SCPE

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Cloud, fog and dew computing concepts offer elastic resources that can serve scalable services. These resources can be scaled horizontally or vertically. The former is more powerful, which increases the number of same machines (scaled out) to retain the performance of the service. However, this scaling is tightly connected with the existence of a balancer in front of the scaled resources that will balance the load among the end points. In this paper, we present a successful implementation of a scalable low-level load balancer, implemented on the network layer. The scalability is tested by a series of experiments for a small scale servers providing services in the range of dew computing services. The experiments showed that it adds small latency of several milliseconds and thus it slightly reduces the performance when the distributed system is underutilized. However, the results show that the balancer achieves even a super-linear speedup (speedup greater than the number of scaled resources) for a greater load. The paper discusses also many other benefits that the balancer provides.

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Section: Sasko Ristov and Kiril Cvetkov And Marjan Gusevmentioning

confidence: 99%

“…Fig. 2.1 [21]. The L3B balancer is hosted on a machine in front of a pool of endpoint servers and its main goal is to balance the clients' requests among the available servers.…”

Section: Dew Computing Servicesmentioning

confidence: 99%

Implementation of a Horizontal Scalable Balancer for Dew Computing Services

Ristov¹,

Cvetkov²,

Gušev³

2016

SCPE

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“…1 [12]. The L3B balancer is hosted on a machine in front of a pool of endpoint servers and its main goal is to balance the clients' requests among the available servers.…”

Section: A the Architectural Conceptmentioning

confidence: 99%

“…The L3B was developed as a Java implementation according to the proposed architecture, since modern Java virtual machines' performance is similar to C or C++ [13]. However, the results were not promising [12]. That is, introducing the L3B balancer reduced the performance even worse than the case without balancer.…”

Section: A the Architectural Conceptmentioning

confidence: 99%

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Successful implementation of L3B: Low level load balancer

Cvetkov

Ristov

Gušev

2015

2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Cloud computing paradigm offers instantiating and deactivating the virtual machine instances on demand according to the clients requirements. When some customer's application or a service needs more resources than a physical server can supply, the cloud provider offers a certain load balancing technique to distribute the load among several servers that will host the analyzed application or service. Additionally, the cloud provider should offer a resource broker to make the application scalable and elastic. In this paper we present a new solution for a low level load balancer, working on a network level. Our load balancer maps the memory addresses of the balancer and the target physical servers (or virtual machines on the cloud) and thus balances the load. The experiments showed that it adds small latency of several milliseconds and thus it slightly reduces the performance when the distributed system is underutilized. However, there is a region of client requests where the system achieves a superlinear speedup (speedup greater than the number of scaled resources). Our case study with doubled resources achieves up to 6.5 speedup (maximum expected linear speedup is 2).

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Energy‐aware cross‐layer resource allocation in mobile cloud

Liu

Luo

2017

Int J Communication

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Summary This paper proposed an energy‐aware cross‐layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation‐intensive tasks can be processed by the remote public cloud, while the delay‐sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy‐aware cross‐layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy‐aware cross‐layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy‐aware cross‐layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed.

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Implementation of a Network Based Cloud Load Balancer

Cited by 7 publications

References 9 publications

Implementation of a Horizontal Scalable Balancer for Dew Computing Services

Implementation of a Horizontal Scalable Balancer for Dew Computing Services

Successful implementation of L3B: Low level load balancer

Energy‐aware cross‐layer resource allocation in mobile cloud

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