Distributed cloud monitoring using Docker as next generation container virtualization technology

Dhakate, Suchit; Godbole, Anand

doi:10.1109/indicon.2015.7443771

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…For these items, we selected the most relevant category and/or element, and in the remainder of this section these items are included in the corresponding subsection. [22] 2015 Amannejad et al [26] 2015 Chiang et al [46] 2015 Dabbagh et al [49] 2015 Dhakate & Godbole [52] 2015 Huang et al [73] 2015 Jin et al [79] 2015 Katsalis et al [82] 2015 Kumbhare et al [87] 2015 Lee et al [89] 2015 Li & Kanso [92] 2015 Liu et al [95] 2015 Mashayekhy et al [101] 2015 Moens et al [112] 2015 Mukherjee et al [114] 2015 Petri et al [122] 2015 Sharma et al [137] 2015 Stankovski et al [140] 2015 Wang et al [149] 2015 Wuhib et al [155] 2015 Zhang et al [169] 2015…”

Section: Cloud Resource Management: State Of the Artmentioning

confidence: 99%

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Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

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Section: Cloud Resource Management: State Of the Artmentioning

confidence: 99%

“…Dhakate and Godbole proposed an architecture for monitoring, testing, reporting and alerting of an entire cloud environment [52]. The required monitoring software is packed inside Docker containers, which can be deployed directly from the Docker Hub repository.…”

Section: Monitoring Infrastructure Demand Profiling and Resource Utimentioning

confidence: 99%

Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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“…Maximize QoS: This occurs when the fogs are deployed in locations where a lot of devices are requesting services. It is known that when the service gets closer to the client, the QoS increases [9]. Initially, that was the motivation behind the fog concept.…”

Section: Fog Localization Processmentioning

confidence: 99%

Machine Learning Models for Scheduling On-Demand Fog Placement and Optimizing Container Deployment. (c2020)

Farhat¹

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Fog computing is an extended cloud computing technology allowing services embedded into virtual machines or containers to be placed at the edge with closer proximity to the end devices. Nevertheless, one of the main difficulties adding up to the complexity of the on-demand fog placement topic is deciding on the proper time and place of the fog deployment process, and deriving the adequate container and service distribution over the available fogs at a certain location. Several techniques have been suggested in the current literature as potential solutions, in which some consider users preferences or random-based approach for fog deployment, while others reside on threshold-based mechanisms. However, due to the huge increase in the number of requests coming from end devices including IoT users, fog deployment and container placement must be scheduled to serve locations with high service requesting profiles while decreasing the cloud processing load. In this context, we first propose a fog placement model that allows to produce an adequate scheduling decision by using a hybrid technique combining time series forecasting and reinforcement learning. The proposed technique learns the intensity of service invocations and behavior of end devices at different locations over time for predicting the localization plan. Second, we propose a K-Means based clustering model embedded within a multi-objective optimization scheme for fog and container placement. A comparison of our proposed solution with random-based and threshold-based fog placement scheduling approaches show that the number of processed requests performed by the cloud decreases from 100% to 29% compared to 93% and 67% in the other two models. The aforementioned results explore the efficiency of our proposed scheme in scheduling the fogs in their rightful place, which helps in decreasing the cloud’s load, decreasing the network congestion, and increasing the overall quality of service. Moreover, experimental results illustrate that the proposed optimization model and clustering technique further improve the pre-existing heuristic-based solutions for container distribution.

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Containers in Software Development: A Systematic Mapping Study

Koskinen

Mikkonen

Abrahamsson

2019

Product-Focused Software Process Improvement

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Over the past decade, continuous software development has become a common place in the field of software engineering. Containers like Docker are a lightweight solution that developers can use to deploy and manage applications. Containers are used to build both component-based architectures and microservice architectures. Still, practitioners often view containers only as way to lower resource requirements compared to virtual machines. In this paper, we conducted a systematic mapping study to find information on what is known of how containers are used in software development. 56 primary studies were selected into this paper and they were categorized and mapped to identify the gaps in the current research. Based on the results containers are most often discussed in the context of cloud computing, performance and DevOps. We find that what is currently missing is more deeply focused research.

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Distributed cloud monitoring using Docker as next generation container virtualization technology

Cited by 6 publications

References 7 publications

Resource Management in a Containerized Cloud: Status and Challenges

Resource Management in a Containerized Cloud: Status and Challenges

Machine Learning Models for Scheduling On-Demand Fog Placement and Optimizing Container Deployment. (c2020)

Containers in Software Development: A Systematic Mapping Study

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