A novel blockchain enabled resource allocation and task offloading strategy in cloud computing environment DOI Creative Commons

G. Senthilkumar,

K. N. Madhusudhan,

Y. Jeyasheela

и другие.

Automatika, Год журнала: 2024, Номер 65(3), С. 973 - 982

Опубликована: Март 12, 2024

Large amounts of processing resources are required for the sensed raw big data during generation process. Furthermore, as typically privacy sensitive, blockchain technology can be used to ensure concerns. This study examines a multiuser mobile offloading network that consists cloud server located remotely and an edge node. We formulate problem joint optimization task decision making all users, computation resource allocation among executing applications, radio assignment remote-processing applications. The goal is minimize maximum weighted cost users. When compared other benchmark approaches, simulation results show proposed algorithm achieves optimal in terms both energy consumption delay result collaboration. Finally strategy with 93% efficiency obtained.

Язык: Английский

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues DOI Open Access
Mohammed Maray, Junaid Shuja

Mobile Information Systems, Год журнала: 2022, Номер 2022, С. 1 - 17

Опубликована: Июнь 28, 2022

Cloud and mobile edge computing (MEC) provides a wide range of services for applications. In particular, enables storage infrastructure provisioned closely to the end-users at cellular network. The small base stations are deployed establish network that can be coined with cloud infrastructure. A large number enterprises individuals rely on offered by clouds meet their computational demands. Based user behavior demand, tasks first offloaded from users then executed one or several specific in MEC architecture has capability handle devices turn generate high volumes traffic. this work, we provide holistic overview MCC/MEC technology includes background evolution remote computation technologies. Then, main part paper surveys up-to-date research concepts offloading mechanisms, granularities, techniques. Furthermore, discuss mechanism static dynamic environment along optimization We further challenges potential future directions research.

Язык: Английский

Процитировано

85

Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions DOI
Mohammad Yahya Akhlaqi, Zurina Mohd Hanapi

Journal of Network and Computer Applications, Год журнала: 2022, Номер 212, С. 103568 - 103568

Опубликована: Дек. 29, 2022

Язык: Английский

Процитировано

74

A review on the edge caching mechanisms in the mobile edge computing: A social-aware perspective DOI

Mohammad Reiss-Mirzaei,

Mostafa Ghobaei‐Arani, Leila Esmaeili

и другие.

Internet of Things, Год журнала: 2023, Номер 22, С. 100690 - 100690

Опубликована: Янв. 10, 2023

Язык: Английский

Процитировано

66

Reinforcement Learning Methods for Computation Offloading: A Systematic Review DOI Open Access
Zeinab Zabihi, Amir Masoud Eftekhari Moghadam, Mohammad Hossein Rezvani

и другие.

ACM Computing Surveys, Год журнала: 2023, Номер 56(1), С. 1 - 41

Опубликована: Июнь 9, 2023

Today, cloud computation offloading may not be an appropriate solution for delay-sensitive applications due to the long distance between end-devices and remote datacenters. In addition, a can consume bandwidth dramatically increase costs. However, such as sensors, cameras, smartphones have limited computing storage capacity. Processing tasks on battery-powered energy-constrained devices becomes even more complex. To address these challenges, new paradigm called Edge Computing (EC) emerged nearly decade ago bring resources closer end-devices. Here, edge servers located end-device perform user tasks. Recently, several paradigms Mobile (MEC) Fog (FC) complement Cloud (CC) EC. Although are heterogeneous, they further reduce energy consumption task response time, especially applications. Computation is multi-objective, NP-hard optimization problem. A significant part of previous research in this field devoted Machine Learning (ML) methods. One essential types ML Reinforcement (RL), which agent learns how make best decision using experiences gained from environment. This article provides systematic review widely used RL approaches offloading. It covers complementary mobile computing, fog Internet Things. We explain reasons various methods technical point view. analysis includes both binary partial techniques. For each method, elements characteristics environment discussed regarding most important criteria. Research challenges Future trends also mentioned.

Язык: Английский

Процитировано

53

A decade of research in fog computing: Relevance, challenges, and future directions DOI
Satish Narayana Srirama

Software Practice and Experience, Год журнала: 2023, Номер 54(1), С. 3 - 23

Опубликована: Июль 18, 2023

Abstract Recent developments in the Internet of Things (IoT) and real‐time applications, have led to unprecedented growth connected devices their generated data. Traditionally, this sensor data is transferred processed at cloud, control signals are sent back relevant actuators, as part IoT applications. This cloud‐centric model, resulted increased latencies network load, compromised privacy. To address these problems, Fog Computing was coined by Cisco 2012, a decade ago, which utilizes proximal computational resources for processing Ever since its proposal, fog computing has attracted significant attention research fraternity focused addressing different challenges such frameworks, simulators, resource management, placement strategies, quality service aspects, economics so forth. However, after research, we still do not see large‐scale deployments public/private networks, can be utilized realizing interesting In literature, only pilot case studies small‐scale testbeds, utilization simulators demonstrating scale specified models respective technical challenges. There several reasons this, most importantly, did present clear business companies participating individuals yet. article summarizes technical, non‐functional, economic challenges, been posing hurdles adopting computing, consolidating them across clusters. The also academic industrial contributions provides future directions considering emerging trends federated learning quantum computing.

Язык: Английский

Процитировано

51

Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet DOI Open Access
Ali Asghari, Mohammad Karim Sohrabi

Computer Science Review, Год журнала: 2024, Номер 51, С. 100616 - 100616

Опубликована: Янв. 3, 2024

Язык: Английский

Процитировано

42

Task offloading and multi-cache placement in multi-access mobile edge computing DOI
Linbo Zhai, Ping Zhao, Kai Xue

и другие.

Computer Networks, Год журнала: 2025, Номер unknown, С. 111030 - 111030

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

6

Joint wireless power transfer and task offloading in mobile edge computing: a survey DOI
Ehzaz Mustafa, Junaid Shuja,

Sardar Khaliq uz Zaman

и другие.

Cluster Computing, Год журнала: 2021, Номер 25(4), С. 2429 - 2448

Опубликована: Авг. 6, 2021

Язык: Английский

Процитировано

77

A survey of mobility-aware Multi-access Edge Computing: Challenges, use cases and future directions DOI
Ramesh Singh, Radhika Sukapuram, Suchetana Chakraborty

и другие.

Ad Hoc Networks, Год журнала: 2022, Номер 140, С. 103044 - 103044

Опубликована: Ноя. 18, 2022

Язык: Английский

Процитировано

55

COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction DOI Creative Commons

Sardar Khaliq uz Zaman,

Ali Imran Jehangiri, Tahir Maqsood

и другие.

Applied Sciences, Год журнала: 2022, Номер 12(7), С. 3312 - 3312

Опубликована: Март 24, 2022

In mobile edge computing (MEC), devices limited to computation and memory resources offload compute-intensive tasks nearby servers. User movement causes frequent handovers in 5G urban networks. The resultant delays task execution due unknown user position base station lead increased energy consumption resource wastage. current MEC offloading solutions separate from mobility. For offloading, techniques that predict the user’s future location do not consider direction. We propose a framework termed COME-UP Computation Offloading with Long-short term (LSTM) based direction prediction. nature of mobility data is nonlinear leads time series prediction problem. LSTM considers previous features, such as location, velocity, direction, input feed-forward mechanism train learning model next location. proposed architecture also uses fitness function calculate priority weights for selecting an optimum server on latency, energy, load. simulation results show latency are lower than baseline techniques, while utilization enhanced.

Язык: Английский

Процитировано

40