Latency Aware Intelligent Task Offloading Scheme for Edge-Fog-Cloud Computing – a Review

B Swapna; V Divya

doi:10.15622/ia.23.1.10

Б Свапна Образовательный фонд Конеру Лакшмайи (Считается университетом)
В Дивья Образовательный фонд Конеру Лакшмайи (Считается университетом)

DOI:

https://doi.org/10.15622/ia.23.1.10

Ключевые слова:

разгрузка задач, облачные вычисления, периферийные вычисления, туманные вычисления, Интернет вещей, задержка

Аннотация

Огромный объем данных, создаваемых процедурами Интернета вещей, требует вычислительной мощности и места для хранения, предоставляемого облачными, периферийными и туманными вычислительными системами. Каждый из этих способов вычислений имеет как преимущества, так и недостатки. Облачные вычисления улучшают хранение информации и вычислительные возможности, одновременно увеличивая задержку соединения. Периферийные и туманные вычисления предлагают аналогичные преимущества с уменьшенной задержкой, но имеют ограниченное хранилище, емкость и покрытие. Первоначально оптимизация применялась для решения проблемы сброса трафика. И наоборот, традиционная оптимизация не может удовлетворить жесткие требования к задержке принятия решений в сложных системах, варьирующейся от миллисекунд до долей секунды. В результате алгоритмы машинного обучения, особенно обучение с подкреплением, набирают популярность, поскольку они могут быстро решать проблемы разгрузки в динамических ситуациях, включающих определенные неопознанные данные. Мы проводим анализ литературы, чтобы изучить различные методы, используемые для решения этой интеллектуальной задачи по разгрузке задач с учетом задержек для облачных, периферийных и туманных вычислений. Уроки, полученные в результате этих исследований, затем представлены в настоящем отчете. Наконец, мы определяем некоторые дополнительные возможности для изучения и проблемы, которые необходимо преодолеть, чтобы достичь минимальной задержки в системе разгрузки задач.

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Интеллектуальная схема распределения задач с учетом задержек вычислений в Edge-Fog-Cloud – обзор

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