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This article introduces a Green Cloudlet Network (GCN) architecture in the context of mobile cloud computing. The proposed architecture is aimed at providing seamless and low End-to-End (E2E) delay between a User Equipment (UE) and its Avatar (its software clone) in the cloudlets to facilitate the application workloads offloading process. Furthermo...
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... software defined networking (SDN) to the cellular core network is one solution to enable a flexible and efficient network [10,11]. The SDN architecture separates the control plane and data plane. The structure of the SDN-based cellular core, as shown in Fig. 5, merges the cel- lular backhaul and core network together. The SDN-based cellular core comprises OpenFlow switches, middleboxes (which are the appliances on which network providers can expand extra functionalities, e.g., network address translation, transcoder, and firewall, in the network to meet various application demands), and one ...
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... Більше того, термін «передтуманні обчислення», знайшов широке застосування не тільки для кордонів безпровідних мереж, а й для стільникових мереж [3]. Системи, в яких хмару використовуються в кожній базовій станції стільникової мережі, стали називати «зеленою» хмарною мережею [4]. «Зелена» хмарна мережа є гетерогенною і дає змогу в деяких зонах комп'ютерної мережі під'єднувати кожну базову станцію до хмари, забезпечуючи на невеликій місцевості обслуговування за допомогою однієї хмари групи базових станцій. ...
В сучасному світі інформаційних технологій та в умовах зростаючого навантаження на комп’ютерні мережі актуальним завданням є їхня оптимізація та поліпшення продуктивності, шляхом ефективного керування ресурсами та зниженням затримки. Побудова архітектури інформаційних технологій здатна зменшити затримку шляхом переміщення хмарних структур на кордон мереж радіодоступу. Передтуманні обчислення, які передбачають обробку даних на межі комп’ютерної мережі, зменшують затримку та підвищують швидкість відповіді, а використання пропускної здатності на периферії допомагає зменшити навантаження на смугу пропускання. Передтуманні обчислення є важливою стратегією для поліпшення комп’ютерних мереж в середовищі Тактильного Інтернету. Метою роботи є моделювання передтуманних обчислень ієрархічної мережі граничних хмар, спрямованих для визначення затримки під час передачі трафіку, оптимізації продуктивності та управління ресурсами. Об’єктом дослідження є модель ієрархічної мережі граничних хмар, включно з граничними та передтуманними кластерами, туманними та хмарними обчисленнями. Предметом дослідження є модель передачі трафіку в ієрархічній хмарній мережі, для забезпечення оптимального управління ресурсами та передачі трафіку, враховуючи вимоги до затримки. У роботі змодельовано ієрархічну мережу граничних хмар, розроблено модель передачі трафіку та проведено аналіз затримки кластерів першого та другого рівнів ієрархічної хмарної мережі. Ієрархічна мережа граничних хмар розроблена для оптимізації передачі даних і управління ресурсами. Граничні кластери мають обмежені обчислювальні можливості, тому з’єднуються з потужнішими передтуманними кластерами. До того ж, туманні обчислення забезпечують узгоджену взаємодію передтуманних кластерів в рамках всієї комп’ютерної мережі. Модель передачі трафіку дає змогу досягти необхідної затримки, ефективності, гарантує безпеку та високу доступність, що надає їй актуальності та користі для середовища Тактильного Інтернету. Переваги змодельованої комп’ютерної мережі полягають у скороченні затримки від джерела даних до користувачів і зниженні ризику перевантаження мережі. При цьому забезпечується гнучкість у побудові мережі та підвищується її доступність, що задовольняє вимоги Тактильного Інтернету.
... Sun et al. [27] propose a green cloudlet network architecture, where all cloudlets are powered with green energy and grid energy. To minimize network energy consumption while meeting the low latency requirements between UDs and VMs, VMs are migrated to cloudlets with more green energy generation and less energy demand. ...
... ∀q ∈ Q ∀o ∈ O, x qo ∈ {0, 1}, (27) ∀q ∈ Q ∀v ∈ V, ∑ o∈O y qvo = 1, ...
Cloudlet networks are an emerging distributed data processing paradigm, which contain multiple cloudlets deployed beside base stations to serve local user devices (UDs). Each cloudlet is a small data center with limited memory, in which multiple virtual machines (VMs) can be instantiated. Each VM runs a UD’s application components and provides dedicated services for that UD. The number of VMs that serve UDs with low latency is limited by a lack of sufficient memory of cloudlets. Memory deduplication technology is expected to solve this problem by sharing memory pages between VMs. However, maximizing page sharing means that more VMs that can share the same memory pages should be instantiated on the same cloudlet, which prevents the communication distance between UDs and their VMs from minimizing, as each VM cannot be instantiated in the cloudlet with the shortest communication distance from its UD. In this paper, we study the problem of VM instantiation with the joint optimization of memory sharing and communication distance in cloudlet networks. First, we formulate this problem as a bi-objective optimization model. Then, we propose an iterative heuristic algorithm based on the ε-constraint method, which decomposes original problems into several single-objective optimization subproblems and iteratively obtains the subproblems’ optimal solutions. Finally, the proposed algorithm is evaluated through a large number of experiments on the Google cluster workload tracking dataset and the Shanghai Telecom base station dataset. Experimental results show that the proposed algorithm outperforms other benchmark algorithms. Overall, the memory sharing between VMs increased by 3.6%, the average communication distance between VMs and UDs was reduced by 22.7%, and the running time decreased by approximately 29.7% compared to the weighted sum method.
... Green MEC is an emerging technology that combines the benefits of MEC with energy-efficient computing to create a more sustainable and ecofriendly approach to mobile computing [300]. The need for Green MEC arises from the growing awareness of the environmental impact of mobile computing and the increasing demand for sustainable solutions. ...
... Unfortunately, designing a green MEC is more difficult than creating a green wireless communication system. The amount of computational resources that need to be managed in order to have a satisfactory computation performance is greater, which makes traditional green radio techniques less practical [300]. ...
... To achieve energy-efficient servers, the processing speeds of lightly-loaded edge servers can be reduced. Another method is geographical load balancing (GLB) [300], which utilizes spatial variations in workload patterns, temperatures, and electricity costs to direct the flow of workload between different data centers. The third method is renewable energy-powered MEC systems [300], which utilize clean energy sources such as wind and solar power to reduce the carbon footprint of MEC systems. ...
Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.
... Sun and Ansari [75] proposed a hierarchical paradigm comprised of cloudlet facility and mobile device infrastructures. Cloudlet coverage zones are typically small due to their reliance on Wi-Fi availability. ...
Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC’s available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements.
... Accurately, User Equipment (UE) via a Wide Area Network (WAN) delivers their application to the cloud. UEs not only use the Virtual Machine (VMs) existing in the Cloud but Also deliver orders to VMs passing through WANs [2]. ...
The current field of which our day is cloud computing. It is used in several fields like medical field. Moreover, for several reasons, such as diversity and the rapid increase in the number of connected devices, Cloud Computing is unable to meet certain requirements such as support for mobility, a high level of scalability, low latency and real time. This creates many challenges for the traditional architecture of Cloud Computing. to meet its requirements, several paradigms have appeared in recent years, such as mobile edge computing, mobile cloud computing and fog computing. Based on our research, fog computing is complementary to the cloud and uses network devices to process the latency of data collected using end users. In addition, MCC (Mobile Cloud Computing) devices offer many advantages such as streaming services to Fog Nodes. Due to the open features and high scalability of these networks, security is not guaranteed, where most of the existing research focuses on protecting systems and their platforms against attacks from unauthenticated devices only on a peripheral paradigm. To answer these questions and secure the IT architecture, which combines the advantages of three emerging technologies: Cloud computing, Fog Computing and Mobile Cloud Computing. In this article, we provide a method called MFCC (Mobile Fog Cloud Computing) that is used to distribute and collaborate firewalls to prevent network-based attacks for healthcare application. Different levels of collaboration, based on a model for assessing confidence in relation to risk, are introduced.
This evaluation framework used the NeSSi² tool, where the results show that the proposed architecture is better in terms of transmission delay and blocking rate compared to related works. The most important result is that our proposal is able to prevent distributed attacks, such as DDoS.
... Therefore, there has been an increasing interest in "greening" the cloudlets in a MEC network. Green cloudlet networks have been proposed in [21], [22]. In these networks, the cloudlets are powered by green energy sources like solar energy. ...
Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, large number of cloudlets needed in such an edge computing network and a tremendous increase in carbon emissions of computing networks globally envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). GCNs must deal with the challenge of the unpredictability of green energy available to them while optimizing the performance delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green-energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reductiof reduction in the latency while ensuring up to 50.15% of reductiof reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests it leads to an optimal task assignment by considering the various factors mentioned above during task assignment process. Thus, Ge-LATA considers an extensive set of parameters during task allotment process mentioned above. It also proposes an efficient green energy allotment scheme which adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.
... The cloudlet model based on dynamic energy aware mobile computing helps in optimizing the usages of the infrastructure as well as the services. It does so by leveraging Cloudlet Latency An application to utilize the cognitive functionalities in the cloudlet by the virtual machines has been proposed [13] Energy Utilizing virtual machines in establishing cloudlet leads to green computing [12] A dynamic model optimizes both the infrastructure and the service usage helps in achieving green computing by leveraging the cloud computing techniques [10] Latency, energy and cost The applications are offloaded easily by reducing the delay between the user devices with the help of an architecture [14] Femtolet Latency and energy Femtolet is derived from femtocell and cloudlet that results in low latency and less power consumption while providing the services [17] Mobile Edge Computing (MEC) ...
... This femtolet consists of data copies that are cached that extends its support to all the associated cloud services. The author also figured it out that the energy consumed by the radio network is found to be higher than that of the Wi-Fi connection [14][15][16]. As only Wi-Fi connection is supported by the cloudlets, it is clear that the power consumed by femtolet is higher than that of the power consumed by the cloudlets. ...
The mobile users have acquired the benefits of cloud computing with the help of Mobile Edge Computing (MEC) technology in order to satisfy the increasing data demands. The efficiency of the system is highly limited by the bandwidth limitations and limitations associated with the mobile devices despite the rapid development of MEC as well as the cloud computing technology. Our aim is to provide an optimal method to optimize the energy consumption in the mobile edge computing. In this regard, the research paper proposed a Green Cloud based Queue Management system for 5G networks that helps in addressing the issues related to latency and energy consumption. While serving the users, the proposed methodology results in less amount of energy being wasted and hence the reduced latency. By means of alleviating the congestion and implementing the virtual list, this issue can be resolved greatly. Simulation is done with the help of NS2 green cloud simulator and the results are obtained by comparing the proposed model to conventional cloud model and cloudlet based on throughput, latency, energy consumption and normalized overhead as these are the evaluation measures. The results show that there has been considerable enhancement in the energy consumption. As the throughput increases, the quality of the service also increases.
... To increase the security of authorizations, IIoT-Ds are grouped to manage the access control jointly, as shown in Fig. 1. After some of the IIoT-Ds form an IIoT group (IIoT-G), IIoT-Ds within the same group can communicate with each other [11], e.g., sharing authorization data and data from sensors. 2 Thus, one IIoT-G constitutes a peer-to-peer (P2P) network in which MAC is better suited than ABAC. ...
The Industrial of Internet of things (IIoT), allowing direct wireless communications between industrial machines and people, is the key component of Industry 4.0. By grouping several IIoT devices (IIoT-Ds) into IIoT groups (IIoT-Gs), the IIoT-Ds within one group can share information and deter malicious users from accessing the network by modifying the access control list (ACL) in IIoT-Ds. In an IIoT-G, several IIoT-Ds jointly manage the access control of IIoT sensors. The security of ACL is protected by blockchains deployed in IIoT-Ds. Even secured by blockchains, a Blockchain based IIoT-G (B-IIoT-G) still cannot secure correct access control in a system with more than half of the Blockchain based IIoT-Ds (B-IIoT-Ds) from spreading wrong authorization information. In this paper, we propose a B-IIoT-G with trust evaluation for each B-IIoT-D to evaluate the weight in voting for final decisions of authorization. By designing a new voting mechanism with trust evaluation for the access control in B-IIoT-G, the system has a higher probability of making correct authorization even with malicious B-IIoT-Ds. Simulation results have demonstrated that the mechanism with trust evaluation can still make, with a high probability, correct authorization even with more than half of the B-IIoT-Ds in B-IIoT-G being malicious.
... MEC can provide services with low latency and high reliability due to the characteristics of low latency, mobility awareness, high bandwidth, location awareness, and security protection. Together with other edge computing paradigms, such as cloudlets [123] and fog computing [124], [125], MEC can be regarded as a bridge between numerous IoT devices and the centralized cloud. In addition, IoT has been considered as a typical use case of MEC by ETSI in its white papers [9], [126]. ...
... However, densely deployed MEC servers still result in large energy consumption. Hence, how to design green MEC systems [123] becomes a critical issue. As mentioned in [1], it is much more challenging to design green MEC than green communication systems. ...
To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet of Things (IoT) applications such as smart city, healthcare, augmented/virtual reality (AR/VR), the fifth generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, Multi-access edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this paper investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT, and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, SDN/NFV, information centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this paper provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this paper further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.
... This section provides an insight into the existing cloudlet based mobile augmentation approaches with the intent to evaluate possible solutions addressing resource scarcity challenges at the cloudlet level. The schemes presented in [25][26][27][28][29] are focused on the improvement of performance by reducing delay, CPU load and energy consumption. For delay improvement, the assumptions of static node and maximum hop count of two hops have been considered. ...
A Cloud computing paradigm augments the limited resources of mobile devices resulting in increased distance, limited Internet bandwidth, and seamless connectivity challenges between a remote cloud and mobile devices. Cloudlet computing based solutions are widely used to address these challenges by bringing the computational facility closer to the user. The ever growing number of mobile devices, Internet of Things (IoT) sensors and Information Communication Technology (ICT) infrastructure used for smart cities demand more resources. The existing cloudlet based solutions are unable to manage the ever-increasing demand for power, storage, and computational resources, and therefore forward the resource extensive tasks to a remote cloud, limiting cloudlet computing benefits. We present the Cloudlet Federation for Resource Optimization (CFRO), a federated cloudlet model for resource optimization to address these resource scarcity challenges. The proposed model exerts the features of scalability, resource collaboration, and robustness. The underlying scheme for resource optimization has been modeled as a Nested Multi Objective Resource Optimization Problem (NMOROP) and a novel algorithm has been proposed to solve it. The detailed analysis and comparative results show that the proposed model offers improved performance and more resource elasticity as compared to the conventional cloudlet model.
