Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing

Puthal, Deepak; Obaidat, Mohammad S.; Nanda, Priyadarsi; Prasad, Mukesh; Mohanty, Saraju P.; Zomaya, Albert Y.

doi:10.1109/mcom.2018.1700795

Cited by 209 publications

(85 citation statements)

References 13 publications

(18 reference statements)

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“…Puthal et al focused on developing an efficient dynamic load-balancing algorithm with an authentication method for edge data centers [175]. Tasks were assigned to an underutilized edge data center by applying the Breadth First Search (BFS) method.…”

Section: Load-balancingmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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Section: Load-balancingmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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“…The authors of [33] developed an authentication technique in edge cloud computing environments. In terms of task allocation, their technique tries to find less overloaded edge servers by monitoring edge servers in the system and performing the breadth first search method to implement the load balancing technique.…”

Section: Literature Reviewmentioning

confidence: 99%

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Lim

Lee

2020

Electronics

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As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.

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“…The former is enables continuous queries in cloud applications as they determine considerable amounts of resource usage, while the latter refers to both secure authentication (significantly concerning in edge computing scenarios) and online end‐to‐end security. The need for secure authentication, as illustrated in the work of Puthal et al, becomes particularly evident if we compare full cloud deployments, which are always performed in secured and trusted environments, against edge computing datacenters and edge devices that, conversely, are only partially trusted (since they are located at the network edge), thus requiring the cloud to be the initial authentication point. Online end‐to‐end security is a relevant issue for near‐/real‐time big data processing scenarios where traditional online security verification approaches cannot cope well with huge volumes of data …”

Section: Related Workmentioning

confidence: 99%

An osmotic computing infrastructure for urban pollution monitoring

2019

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Summary Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet‐of‐Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet‐of‐Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.

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Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing

Cited by 209 publications

References 13 publications

Resource Management in Fog/Edge Computing

Resource Management in Fog/Edge Computing

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

An osmotic computing infrastructure for urban pollution monitoring

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