Service cost-based resource optimization and load balancing for edge and cloud environment

Li, Chunlin; Tang, Jianhang; Luo, Yingwei

doi:10.1007/s10115-020-01489-6

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…There are certain limitations and constraints associated with the development and implementation of different load balancing algorithms. The algorithm's complexity, communication overhead, costs (setup cost, maintenance cost, communication cost and/or execution cost) [209] directly impact the implementation. In the case of implementing a global load-balancer, there are always scalability challenges, inefficient resource utilization, adaptability issues and the need for intelligent configurations.…”

Section: Reduces Energy Consumption Execution Cost and Makespanmentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

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Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

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Section: Reduces Energy Consumption Execution Cost and Makespanmentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

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“…Among them, the constraint of workload and computing capacity are mainly considered in the optimization model [17][18][19][20]. And the objective for the problem mainly includes energy consumption [21], workload balancing [22], and the total cost [23,24]. To avoid the effect of traffic data storage and processing on the sensor deployment problem, this paper intends to investigate it by integrating the problem of server placement for highways.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways

Tong,

Li,

Cui

2024

Applied Sciences

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This paper mainly studies the deployment of hybrid sensors on highways. By constructing the deployment location constraint model, the overall accuracy of sensor deployment can be maximized. In addition, in order to meet the needs of intelligent transportation, the consideration of traffic data communication is added to the work. The highway under study is first divided into several units, and the combination type of sensors is used to represent the possible layout of two adjacent sensors. Then, a 0–1 optimization model reflecting the interaction between the sensor position and the server position is established. Then, a two-step search algorithm is proposed to find the optimal solution of the model and determine the deployment scheme with maximum accuracy. Finally, an example is given to verify the method. The results show that there are significant differences between uniform unit-deployment schemes and non-uniform unit-deployment schemes. Through the sensitivity analysis of each factor, the influence of budget and communication radius on the deployment plan is proven. In addition, the ramp length can also have a negative impact on the target value.

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An adaptive function placement in serverless computing

Sun

2022

Cluster Comput

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Service cost-based resource optimization and load balancing for edge and cloud environment

Cited by 6 publications

References 29 publications

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways

An adaptive function placement in serverless computing

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