Resource Constrained Offloading in Fog Computing

Kattepur, Ajay; Dohare, Harshit; Mushunuri, Visali; Rath, Hemant Kumar; Simha, Anantha

doi:10.1145/3017116.3022871

Cited by 25 publications

(12 citation statements)

References 11 publications

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“…This decision‐making problem can be formulated using Markov decision process and a mathematical framework can be used to solve the problem . The design of such decision‐making frameworks in fog must also consider the impact of the mobility feature in the fog computing environment on the decision making process …”

Section: Fog Computing Aspectsmentioning

confidence: 99%

“…110 The design of such decision-making frameworks in fog must also consider the impact of the mobility feature in the fog computing environment on the decision making process. 122 Since the fog computing environment is resource-constrained, lightweight container virtualization mechanisms such as Docker, Rkt, and LXC are more suitable than traditional hypervisor-based virtual machine approaches. Planning migration ahead of time will ensure that QoS violations do not occur and also reduces the overall network utilization.…”

Section: Migrationmentioning

confidence: 99%

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Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Section: Fog Computing Aspectsmentioning

confidence: 99%

Section: Migrationmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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“…The use of resource optimal protocols in the robotic world has been studied in centralized deployments [20]. Decomposition of optimization algorithms across multiple nodes has been used extensively in the networking literature to design cross-layer resource allocation mechanisms [19].…”

Section: Related Workmentioning

confidence: 99%

Distributed Optimization Framework for Industry 4.0 Automated Warehouses

Kattepur¹,

Rath²,

Mukherjee³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Robotic automation is being increasingly proselytized in the industrial and manufacturing sectors to increase production efficiency. Typically, complex industrial tasks cannot be satisfied by individual robots, rather coordination and information sharing is required. Centralized robotic control and coordination is ill-advised in such settings, due to high failure probabilities, inefficient overheads and lack of scalability. In this paper, we model the interactions among robotic units using intelligent agent based interactions. As such agents behave autonomously, coordinating task/resource allocation is performed via distributed algorithms. We use the motivating example of warehouse inventory automation to optimally allocate and distribute delivery tasks among multiple robotic agents. The optimization is decomposed using primal and dual decomposition techniques to operate in minimal latency, minimal battery usage or maximal utilization scenarios. These techniques may be applied to a variety of deployments involving coordination and task allocation between autonomous agents.

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“…The fog, despite it being a relatively recent computing architecture, has been used for a large number of applications. These applications include providing resilience at scale [4], robotics ([6], [12]), data analysis [21], and social sensing for limited internet connectivity [16]. It is likely the fog could target many applications that require additional computing resources, making it ideal for mobile and Internet of Things applications.…”

Section: Introductionmentioning

confidence: 99%

Gait-Based Identification Using Wearables in the Personal Fog

Walter

Gamble

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Wearables are becoming more computationally powerful, with increased sensing and control capabilities, creating a need for accurate user authentication. Greater control and power allow wearables to become part of a personal fog system, but introduces new attack vectors. An attacker that steals a wearable can gain access to stored personal data on the wearable. However, the new computational power can also be employed to safeguard use through more secure authentication. The wearables themselves can now perform authentication. In this paper, we use gait identification for increased authentication when potentially harmful commands are requested. We show how the relying on the processing and storage inherent in the personal fog allows distributed storage of information about the gait of the wearer and the ability to fully process this data for user authentication locally at the edge. While gait-based authentication has been examined before, we show an additional, low-power method of verification for wearables.

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Resource Constrained Offloading in Fog Computing

Cited by 25 publications

References 11 publications

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Distributed Optimization Framework for Industry 4.0 Automated Warehouses

Gait-Based Identification Using Wearables in the Personal Fog

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