A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Lim, Joo Hyun; Lee, DaeWon

doi:10.3390/electronics9040686

Cited by 25 publications

(15 citation statements)

References 30 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One common characteristic of stream processing systems is that they heuristically and/or statistically optimize the computation costs of computing on fogs and edges. No systems leverage machine learning techniques to balance high loads [94].…”

Section: Summaries and Issues Of Stream Processing Systemsmentioning

confidence: 99%

A Survey on IoT Big Data Analytic Systems: Current and Future

Sasaki

2022

IEEE Internet Things J.

View full text Add to dashboard Cite

The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as IoT big data. The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop-and Spark-based batch processing systems for spatio-temporal and trajectory data. We also review fog-and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability.We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.

show abstract

Section: Summaries and Issues Of Stream Processing Systemsmentioning

confidence: 99%

A Survey on IoT Big Data Analytic Systems: Current and Future

Sasaki

2022

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…The studies reported in [11] [12] and [13] employed genetic algorithms for server placement and task allocation, while Lim et al [11] aimed to find an effective strategy based on graph coloring to offload tasks to edge servers to balance the load. Tang et al [12] focused on the energy consumption in the communication network of a data center.…”

Section: Related Workmentioning

confidence: 99%

Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

Huang

Chin²,

Chuang

2021

IEEE Access

View full text Add to dashboard Cite

Offloading tasks to cloud servers has increasingly been used to provide terminal users with powerful computation capabilities for a variety of services. Recently, edge computing, which offloads tasks from user devices to nearby edge servers, has been exploited to avoid the long latency associated with cloud computing. However, edge server placement and task allocation strongly affect the offloading process and the quality of a user's experience. Therefore, appropriately deploying the edge servers within a network and evenly allocating the workload to the servers are vital. This paper thus considers both the workload of edge servers and the distances involved in offloading tasks to these servers. To improve the user experience, edge server locations are carefully selected and the workload for the servers are allocated in a balanced manner. This scenario is formulated as a mixed-integer linear programming problem, and a novel solution that searches for the best server placement using simulated annealing while integrating task allocation using the Lagrangian duality theory with the sub-gradient method is proposed. Numerical simulations verify that the proposed algorithm can achieve better results than conventional heuristics. INDEX TERMSCloud computing, Edge computing, Server placement, Task allocation I. INTRODUCTION

show abstract

“…To support real-time applications in MEC, the edge devices can offload their task to the nearest edge server. A loadbalancing algorithm (LBA) for edge devices is proposed in [121]. This work aims to assign the offloaded task to edger servers in an effective method.…”

Section: ) Lbamentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

View full text Add to dashboard Cite

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

show abstract

A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Cited by 25 publications

References 30 publications

A Survey on IoT Big Data Analytic Systems: Current and Future

A Survey on IoT Big Data Analytic Systems: Current and Future

Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

Contact Info

Product

Resources

About