scholarly journals Elegant Computational Intensive Task Offloading Scenario for Android

2021 ◽  
Author(s):  
MAHESH KALUTI
Keyword(s):  
Cloud Computing ◽  
Smart Phone ◽  
Computation Offloading ◽  
Smart Devices ◽  
Small Scale ◽  
Cloud Framework ◽  
Iot Devices ◽  
And Storage ◽  
Day By Day ◽  
Task Offloading

Despite the technical changes and enormous day by day upgradiation in the field of mobile computing the smart devices as well as IOT devices had experienced tremendous technical glitch, which narrow’s the life span and survivability of small scale processing devices. Today, end users are becoming more demanding and are expecting to run computational intensive tasks on their Smart phone devices and IOT devices. Therefore, virtual cloud computing (VCC) integrates local device computing and Cloud Computing (CC) in order to extend computational capabilities of smart phone devices and IOT devices using cloud offloading techniques. Computation Offloading tackles limitations of Smart phone devices and IOT devices such as limited battery duration, limited computational capabilities, and limited storage capacity by offloading the execution and workload to cloud which has better systems with better computation and storage capabilities. This paper aims to present the techniques to offload computational intensive tasks to cloud framework and analyses them along with traditional local execution techniques and their issues. Furthermore, it explores other important parameters based on which the applications are implemented such as offloading technique and partitioning of tasks.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

Examining IoT's Applications Using Cloud Services

2018 ◽  
pp. 147-163 ◽  
Author(s):  
Saravanan K ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Distributed Computing ◽  
Knowledge Sharing ◽  
Effective Communication ◽  
Cloud Services ◽  
On Demand ◽  
Computing Paradigm ◽  
Iot Devices ◽  
And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

A Survey on Comparison of Performance Analysis on a Cloud-Based Big Data Framework

2022 ◽  
pp. 1865-1875
Author(s):  
Krishan Tuli ◽  
Amanpreet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
It Services ◽  
Huge Amount ◽  
Data Framework ◽  
Development Tools ◽  
Cloud Framework ◽  
Suitable Framework ◽  
Cloud Applications ◽  
Day By Day

Cloud computing is offering various IT services to many users in the work on the basis of pay-as-you-use model. As the data is increasing day by day, there is a huge requirement for cloud applications that manage such a huge amount of data. Basically, a best solution for analyzing such amounts of data and handles a large dataset. Various companies are providing such framesets for particular applications. A cloud framework is the accruement of different components which is similar to the development tools, various middleware for particular applications and various other database management services that are needed for cloud computing deployment, development and managing the various applications of the cloud. This results in an effective model for scaling such a huge amount of data in dynamically allocated recourses along with solving their complex problems. This article is about the survey on the performance of the big data framework based on a cloud from various endeavors which assists ventures to pick a suitable framework for their work and get a desired outcome.

scholarly journals Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Real Time ◽  
Large Scale ◽  
Smart Devices ◽  
Budget Constraints ◽  
Smart Systems ◽  
Data Intensive ◽  
Cost Efficient ◽  
And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2019 ◽  
pp. 1927-1951
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals Modeling and Analyzing Offloading Strategies of IoT Applications over Edge Computing and Joint Clouds

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 402
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Network Performance ◽  
Disruptive Technology ◽  
Essential Factor ◽  
Service Architecture ◽  
Loosely Coupled ◽  
Iot Applications ◽  
Overall Performance ◽  
Iot Devices ◽  
And Storage ◽  
Task Offloading

Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the computational requirements exerts more influence on the IoT application’s performance compared to the communication requirement. However, when the system scales up to accommodate more IoT devices, communication bandwidth will turn to be the dominant resource and becomes the essential factor that will directly impact the overall performance. Thus, orchestration is a necessary procedure to encompass optimized solutions under different constraints for optimal offloading placement.

scholarly journals Edge-Computing Architectures for Internet of Things Applications: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6441 ◽  
Author(s):  
Salam Hamdan ◽  
Moussa Ayyash ◽  
Sufyan Almajali
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Resource Limitation ◽  
Edge Computing ◽  
Iot Applications ◽  
Life Tasks ◽  
Iot Devices ◽  
Processing And Storage ◽  
Time Critical ◽  
And Storage

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

scholarly journals The embedded framework for securing the Internet of Things

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Feroz Khan A.B ◽  
◽  
Anandharaj G ◽  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Security Protocols ◽  
Smart Devices ◽  
The Internet ◽  
Home Automation ◽  
Iot Applications ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Entire Network

The smart devices connected on the internet turn to be the internet of things, which connect other objects or devices through unique identifiers with the capability of transferring and receiving the information over the internet. There are numerous applications in different areas such as healthcare, home automation, transportation, military, agriculture, and still so many sectors that incorporate cutting-edge technologies of communication, networking, cloud computing, sensing, and actuation. With this huge increase in the number of connected devices, a strong security mechanism is required to protect the IoT devices. Hence, it is required to focus on the challenges and issues of IoT enabled applications to safeguard the entire network from the outside invasion. This paper discusses some of the challenges in building IoT applications, a detailed study of the existing security protocols, and its issues, and the potential of the IoT.

scholarly journals QoS-aware CR-BM-based hybrid framework to improve the fault tolerance of fog devices

2021 ◽  
Vol 19 (1) ◽  
pp. 66-76
Author(s):  
P. Sharma ◽  
P. K. Gupta
Keyword(s):  
Fault Tolerance ◽  
Performance Metrics ◽  
Fault Tolerant ◽  
Optimal Placement ◽  
Smart Devices ◽  
Computation Cost ◽  
Hybrid Framework ◽  
Iot Devices ◽  
And Storage ◽  
The Internet Of Things

With the evolution of the Internet of Things (IoT), the use of smart devices has completely changed the day-to-day life of the human being. IoT devices are of flexible use which is implemented to sense the environment and data efficiently. However, these devices have some constrained capabilities concerning fault tolerance, computation cost, and storage. This requires an improved framework and algorithms for performing effective operations. In this paper, a hybrid framework is proposed, which incorporates the various IoT devices in fog environments to enhance fault tolerance. The proposed framework implements a novel QoS-aware technique based on the combination of checkpoints and replication (CR) for diagnosing faults and the bee-mutation (BM) algorithm for optimal placement of service. A fog service monitor is established to observe the performance of fog nodes. Both the CR module and BM module access the service monitor to ensure that the proposed hybrid framework is fault-tolerant. Furthermore, the proposed CR-BM-based hybrid framework has been evaluated for its performance by using various performance metrics. In the comparative analysis, it is observed that the proposed hybrid framework outperforms the existing genetic algorithm-based framework.

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