Supporting mobility-aware computational offloading in mobile cloud environment

Junior, Warley; França, Adriano Henrique de Melo; Dias, Kelvin Lopes; Souza, José Neuman de

doi:10.1016/j.jnca.2017.07.008

Cited by 17 publications

(5 citation statements)

References 29 publications

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“…In a similar manner, the whole virtual server can migrate to the topologically closest Edge server to the user, reactively, every time a relocation is detected [148]. For instance, utilizing IP tracking, remote caching and the Software-Defined Networking (SDN) paradigm, can set the ground for efficient and timely task offloading as well; an SDN controller is able to track the user's network location, i.e., the Edge server in proximity and quickly react to changes in it by rerouting the offloaded task's response [149].…”

Section: Mobile (High-mobility)mentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

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Section: Mobile (High-mobility)mentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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“…In [13], a scheme is proposed to address seamless offloading using software-defined networking (SDN) and remote cache. This scheme is divided into three layers: the mobile environment, network infrastructure, and cloud infrastructure layers.…”

Section: Related Workmentioning

confidence: 99%

Mobility and Execution Time Aware Task Offloading in Mobile Cloud Computing

Mary

Alyosius

2022

Int. J. Interact. Mob. Technol.

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Nowadays, mobile devices perform almost all tasks that can be performed by a computer but empties the battery and consumes memory. It is not necessary to execute the tasks on mobile devices; instead, it is executed in the far-away cloud. To save battery energy, the tasks are offloaded and hopped through several access points to reach the cloud and executed which increased the execution time of the task. Therefore, to save execution time and energy, the tasks are offloaded to a nearby cloudlet and as the device moves, the cloudlet and mobile device are disconnected. The mobile device is connected to the next cloudlet; while the offloaded tasks are partially executed in the previous cloudlet VM migrates to the new cloudlet. The previous cloudlet examined the remaining execution time of the task. If it is less than the connection time, the task is finished and the result is transferred to the new cloudlet; otherwise, the task is offloaded to the new cloudlet. It is seen that the mobility and execution time aware task offloading model reduces the execution time and power consumption by 21-40% and 26-34% approximately to the existing mobility-aware offloading approach.

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“…Junior et al 50 proposed a mobility‐aware computational offloading approach named as MOSys , which supports seamless offloading operations during user mobility across the wireless networks. Authors used remote caching technique to reduce the offloading response time.…”

Section: Overview Of MCC Applicationsmentioning

confidence: 99%

Emergency management systems using mobile cloud computing: A survey

Nanda

Panigrahi

Pati

2020

Int J Communication

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Among today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource-limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.

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Supporting mobility-aware computational offloading in mobile cloud environment

Cited by 17 publications

References 29 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Mobility and Execution Time Aware Task Offloading in Mobile Cloud Computing

Emergency management systems using mobile cloud computing: A survey

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