Osmotic Computing and Related Challenges: A Survey

Kaur, Akashdeep; Kumar, Rajesh; Saxena, Sharad

doi:10.1109/pdgc50313.2020.9315757

Cited by 8 publications

(4 citation statements)

References 16 publications

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Section: Osmotic Computingmentioning

confidence: 99%

“…The process of Osmosis consist of Solvent, Solution, Semipermeable membrane and Concentration. OC aims to decompose applications into microservices to perform dynamic tailoring on them and exploit Edge and Cloud resources, evolving towards balanced deployment of microservices satisfying high‐level needs and low‐level constraints 2,11 Definitions: Solute : Computational power, energy, current load, and processing time are considered as solutes. As the services need to be migrated, the services are considered as the soluble part. Solvent : The Solvent moves through the semipermeable membrane.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OCTRA‐5G: Osmotic computing based task scheduling and resource allocation framework for 5G

Kaur

Kumar

Saxena

2022

Concurrency and Computation

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Long term evolution (LTE) mobile technology provides high data rate and low latency.5G Technology is capable of handling the increasing number of IoT devices and provides ultra-low latency, higher throughput, and higher reliability. Mobile edge computing (MEC) a key 5G technology strengthens the real-time processing ability, releases the load on the Core Network, and helps in the real-time processing of data, fulfilling the promise of high data rate and low latency. MEC is used to manage services efficiently to the near user resource. Using Osmotic Computing the services are efficiently scheduled and migrated. The work presented in this article proposes OCTRA-5G Framework to effectively schedule services and allocate resources using Osmotic Computing (OC) by segregating the services into microservices and macroservices. The results are validated on the sets of 10, 20, and 30 gNBs (base stations) through simulation. OCTRA-5G is tested on First Come First Serve (FCFS), Priority Scheduling (PS), and Shortest Job First (SJF) algorithm. FCFS provides less time complexity and higher throughput. The results presented using numerical simulations shows better performance by an average of 66.921% with OC than without OC.

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Section: Osmotic Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OCTRA‐5G: Osmotic computing based task scheduling and resource allocation framework for 5G

Kaur

Kumar

Saxena

2022

Concurrency and Computation

Self Cite

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show abstract

“…Osmotic Computing (OC) uses micro virtual machines (MVM) that can be moved between the local level and the cloud depending on available resources. This paradigm is inspired by osmotic pressure to distribute tasks between Edge and Cloud level [1,2]. However, for the moment, security aspects in terms of privacy or data confidentiality are lowly or not managed.…”

Section: Introductionmentioning

confidence: 99%

A New Edge Computing Architecture for IoT and Multimedia Data Management

2022

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The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT).

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“…In this context, Osmotic computing 8–10 aims at encompassing all different Cloud‐IoT paradigms under the umbrella of a generic and adaptive application management approach, seeking to optimise different QoS parameters (e.g. resource usage and end‐to‐end latencies).…”

Section: Introductionmentioning

confidence: 99%

Osmotic management of distributed complex systems: A declarative decentralised approach

Forti

Lera

Guerrero

et al. 2021

J Software Evolu Process

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Osmotic computing encompasses emerging Cloud‐Internet of Things (IoT) computing paradigms, by featuring the possibility for application services to adapt into different functionally equivalent flavours, depending on the contextually available resources and on specific requirements of running applications. This article proposes a fully decentralised declarative framework that enables both application and infrastructure operators to declare management policies for the service instances and the nodes they manage, respectively. Policies are composed of a simple and well‐defined set of management operations, declared in Prolog, which trigger based on locally available contextual information on application requests and infrastructure resources. A prototype implementation of the framework is showcased and assessed via simulation over a lifelike Smart Campus use case with multiple applications, at increasing infrastructure sizes and number of mobile users. Experimental results show that the proposed management framework scales to large infrastructure sizes and suits the needs of multiflavoured Osmotic applications in dynamic deployment conditions, by improving the trade‐off between their response times and suitable service usage.

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Osmotic Computing and Related Challenges: A Survey

Cited by 8 publications

References 16 publications

OCTRA‐5G: Osmotic computing based task scheduling and resource allocation framework for 5G

OCTRA‐5G: Osmotic computing based task scheduling and resource allocation framework for 5G

A New Edge Computing Architecture for IoT and Multimedia Data Management

Osmotic management of distributed complex systems: A declarative decentralised approach

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