Dynamic application placement in the Mobile Cloud Network

Trneberg, William; Mehta, Amardeep; Wadbro, Eddie; Tordsson, Johan; Eker, Johan; Kihl, Maria; Elmroth, Erik

doi:10.1016/j.future.2016.06.021

Cited by 61 publications

(57 citation statements)

References 19 publications

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“…Works including mobility on the demand side only are, for example, Plachy et al [68] who consider that computational resources needed by a user are allocated in a stationary base station in a VM, which can be transferred to another base station if the user is moving. Similar solutions are presented by Tärneberg et al [67], Gomes et al [13], Oueis [63], Fan et al [62], and Wang et al [77].…”

Section: Summary Of Edge Resource Locationsupporting

confidence: 82%

“…Sometimes the VMs are used as a means to ensure that a task can run given enough underlying resources in the device hosting the VM, for example in the work by Tärneberg et al [67].…”

Section: Computation and Communicationmentioning

confidence: 99%

“…For example, Tärneberg et al [67] study applicationdriven placement and present a system model for mobile cloud network with a dynamic placement algorithm that guarantees application performance, minimizes cost, and tackles resource asymmetry problems. Plachy et al [68] propose a cooperative and dynamic VM placement algorithm associated with another cooperative algorithm for selecting a suitable communication path.…”

Section: Migrationmentioning

confidence: 99%

See 2 more Smart Citations

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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Section: Summary Of Edge Resource Locationsupporting

confidence: 82%

“…Sometimes the VMs are used as a means to ensure that a task can run given enough underlying resources in the device hosting the VM, for example in the work by Tärneberg et al [67].…”

Section: Computation and Communicationmentioning

confidence: 99%

Section: Migrationmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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“…Mycocloud [19] is another work, which provides elasticity through self-organized service placement in decentralized clouds. The work of Elmroth [20] takes into account rapid user mobility and resource cost when placing applications in Mobile Cloud Networks (MCN). A recent work of Tantawi [21] uses biased statistical sampling methods for cloud workload placement.…”

Section: O U D S U I T E B E N C H M a R K R E S U Lt Smentioning

confidence: 99%

Practical Service Placement Approach for Microservices Architecture

Selimi

Cerdà-Alabern

Sánchez-Artigas

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Abstract-Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of a "careful" placement of micro-clouds and services over the network.To achieve this, this paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is vital to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 211% in terms of bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the loss packet rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic.

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“…In 2017, data traffic of mobile devices is expected to exceed 6 Exabytes (6 * 10 9 Gigabytes) per month, and when combined with the traffic generated by laptops and machine-to-machine communications, the overall demand should reach 11 Exabytes per month [1]. Although cloud computing appears as a straightforward solution for processing such an amount of data, in certain scenarios the latency introduced by sending/retrieving heavy payloads from/to the cloud can be prohibitive [2]. To address data-intensive and low latency requirements, as well as to avoid the bottlenecks of centralized servers, edge computing proposes to bring computation to the edge of the network, that is, near to where it is needed by users and devices [3].…”

Section: Introductionmentioning

confidence: 99%