With the rapid advancement of the Internet of Things and 5G networks in smart cities, the inevitable generation of massive amounts of data, commonly known as big data, has introduced increased latency within the traditional cloud computing paradigm. In response to this challenge, Mobile Edge Computing (MEC) has emerged as a viable solution, offloading a portion of mobile device workloads to nearby edge servers equipped with ample computational resources. Despite significant research in MEC systems, optimizing the placement of edge servers in smart cities to enhance network performance has received little attention. In this article, we propose RESP , a novel Recursive clustering technique for Edge Server Placement in MEC environments. RESP operates based on the median of each cluster determined by the number of base transceiver stations, strategically placing edge servers to achieve workload balance and minimize network traffic between them. Our proposed clustering approach substantially improves load balancing compared to existing methods and demonstrates superior performance in handling traffic dynamics. Through experimental evaluation with real-world data from Shanghai Telecom’s base station dataset, our approach outperforms several representative techniques in terms of workload balancing and network traffic optimization. By addressing the ESP problem and introducing an advanced recursive clustering technique, this work makes a substantial contribution to optimizing mobile edge computing networks in smart cities. The proposed algorithm outperforms alternative methodologies, demonstrating a 10% average improvement in optimizing network traffic. Moreover, it achieves a 53% more suitable result in terms of computational load.

The Internet of Things (IoT) has grown at a rapid pace in recent years. It requires a large amount of data and massive computational resources, thus the concept of Fog Computing (FC) has emerged. FC attempts to overcome network latency by bringing computational resources closer to IoT devices. One important part of FC is an offloading mechanism to make proper decisions for better utilizing of FC node(s), especially for real-time (low latency and high throughput) applications. Generally, offloading policies are categorized as centralized and distributed. However, by growing numbers of IoT devices which leads to expansion of FC layer beyond the initial configurations, centralized scheduling solutions for time-sensitive tasks suffers from two major challenges: first, increasing complexity, and second, non-fault tolerating. In order to address these issues, scalable decentralized/distributed approaches have been developed to schedule tasks through an autonomous collaboration between a small number of nodes (neighbors). Without a thorough picture of the network or nodes’ state, it is difficult to design algorithms that make optimum decisions. This paper presents a scalable algorithm for offloading time-sensitive tasks through a semi-network aware distributed scheduling mechanism. Based on the evaluation results obtained for acceptance rate, response time, and network resource usage, the proposed method outperforms the state-of-the-art on average.

Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high Quality of Service (QoS) and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered as one of the main critical challenges in a heterogeneous VCS. Due to this, in this article, we design two task scheduling algorithms for VCSs, named Min-CCV and Min-V . The main goal of the proposed algorithms is jointly minimizing the computation, communication, and delay violation cost for the Internet of Things (IoT) requests. Our extensive simulation results show that proposed algorithms are able to allocate tasks to volunteer fog/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm.

In this preliminary study, we consider the server allocation problem for edge computing system deployment. Our goal is to minimize the average turnaround time of application requests/tasks, generated by all mobile devices/users in a geographical region. We consider two approaches for edge cloud deployment: the flat deployment, where all edge clouds co-locate with the base stations, and the hierarchical deployment, where edge clouds can also co-locate with other system components besides the base stations. In the flat deployment, we demonstrate that the allocation of edge cloud servers should be balanced across all the base stations, if the application request arrival rates at the base stations are equal to each other. We also show that the hierarchical deployment approach has great potentials in minimizing the system's average turnaround time. We conduct various simulation studies using the CloudSim Plus platform to verify our theoretical results. The collective findings trough theoretical analysis and simulation results will provide useful guidance in practical edge computing system deployment.

Fog computing is a decentralised model which can help cloud computing for providing high quality‐of‐service (QoS) for the Internet of Things (IoT) application services. Service placement problem (SPP) is the mapping of services among fog and cloud resources. It plays a vital role in response time and energy consumption in fog–cloud environments. However, providing an efficient solution to this problem is a challenging task due to difficulties such as different requirements of services, limited computing resources, different delay, and power consumption profile of devices in fog domain. Motivated by this, in this study, we propose an efficient policy, called MinRE, for SPP in fog–cloud systems. To provide both QoS for IoT services and energy efficiency for fog service providers, we classify services into two categories: critical services and normal ones. For critical services, we propose MinRes, which aims to minimise response time, and for normal ones, we propose MinEng, whose goal is reducing the energy consumption of fog environment. Our extensive simulation experiments show that our policy improves the energy consumption up to 18%, the percentage of deadline satisfied services up to 14% and the average response time up to 10% in comparison with the second‐best results.