A Cooperative Fog Approach for Effective Workload Balancing

Kapsalis, Andreas; Kasnesis, Panagiotis; Venieris, Iakovos S.; Kaklamani, Dimitra I.; Patrikakis, Charalampos Z.

doi:10.1109/mcc.2017.25

Cited by 71 publications

(47 citation statements)

References 9 publications

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“…The Four (4) C model is an acronym of computin Figure 5 summarizes the major milestones in the evolution of cellular networks topology towards achieving E-2-E low latency. Besides, the literature provides a comprehensive overview TTI [63][64][65][66], blocklength channel codes [67] SCMA [45,68],CDMA [69], NOMA [70] Resource allocation [35][36][37][38] Resource allocation Packet dropping, power allocation policy [61,2,62] CPU [56,57], Memory [58,59], Cloud storage device [60] Operating system [53], Application [54], Service [55] SM-MIMO [46], Jacobi [47], Gauss-Seidel [48], Maximum likelihood [49], Richardson [50], Successive overrelation (SOR) [51.52] Beam selection [43], User and Beam selection [44] Design and optimal positioning of DCs [40,41] Design and optimal positioning of distribution [39,40] Figure 5 summarizes the major milestones in the evolution of cellular networks topology towards achieving E-2-E low latency. Besides, the literature provides a comprehensive overview about the various new cellular networks topology involving software defined networking (SDN) [40,41], network function virtualization (NFV)…”

Section: The Four (4) C Model For Ultra-low Latency High Capacity 5g mentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.

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Section: The Four (4) C Model For Ultra-low Latency High Capacity 5g mentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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“…Our obtained results based on such trace produced by DTMC-based traffic trace generator are discussed in experiment-4 and experiment-5. 7) Capacity: In the simulation, the processing capacity of each fog node, K P j , is U (800, 1300) MIPS [55], and the processing capacity of each cloud server, K P k , is assumed to be 20 times that of the fog nodes. The storage capacity of the fog nodes, K S j , is assumed to be more than 25 GB, to host at most 50 services of the maximum size (size of services, L S a , is U (50, 500) MB for typical Linux containers).…”

Section: ) Qos Parametersmentioning

confidence: 99%

FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

Yousefpour

Patil

Ishigaki

et al. 2019

IEEE Internet Things J.

171

142

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Recent advances in the areas of Internet of Things (IoT), Big Data, and Machine Learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring Quality of Service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user.In this paper, we first introduce FOGPLAN, a framework for QoS-aware Dynamic Fog Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

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“…Both [11] and [12] have included the publish-subscribe pattern in their brokers. A clone brokering system, FogMQ is proposed in [12] to provide a device cloning service where clones are able to self-discover and autonomously migrate to potential cloud hosting platforms to achieve low latency.…”

Section: Broker In Fogmentioning

confidence: 99%

“…A clone brokering system, FogMQ is proposed in [12] to provide a device cloning service where clones are able to self-discover and autonomously migrate to potential cloud hosting platforms to achieve low latency. On the other hand, the fog broker in [11] is responsible for enriching the messages they receive from the lower layers, as well as for task management and allocation. This is achieved by means of the Workload Balancer.…”

Section: Broker In Fogmentioning

confidence: 99%

Find my trustworthy fogs: A fuzzy-based trust evaluation framework

Rahman

Newaz

et al. 2020

Future Generation Computer Systems

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Find My Trustworthy Fogs: A Fuzzy-based Trust Evaluation Frameworkhttp://researchonline.ljmu.ac.uk/id/eprint/8724/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. AbstractThe growth of IoT is proven with the massive amount of data generated in 2015, and expected to be even more in the years to come. Relying on the cloud to meet the expanding volume, variety, and velocity of data that the IoT generates may not be feasible. In the last two years, fog computing has become a considerably important research topic in an attempt to reduce the burden on cloud and solve cloud's inability to meet the IoT latency requirement. However, fog environment is different than in cloud since fog environment is far more distributed. Due to the dynamic nature of fog, backups such as redundant power supply would deem unnecessary, and relying on just one Internet Service Provider for their fog device would be sufficient. If obstacles arise in this fog environment, factors such as latency, availability or reliability would in turn be unstable. Fogs become harder to trust, and this issue is more complicated and challenging in comparison to the conventional cloud. This implies that trustworthiness in fog is an imperative issue that needs to be addressed. With the help of a broker, managing trust in a distributive environment can be tackled. Acting as an intermediary, a broker helps in facilitating negotiation between two parties. Although the brokering concept has been around for a long time and is widely used in the cloud, it is a new concept in fog computing. As of late, there are several research studies that incorporates broker in fog where these brokers focus towards pricing management. However to the best of our knowledge there is no literature on broker-based trust evaluation in fog service allocation. This is the first work that proposes broker-based trust evaluation framework that focuses on identifying a trustworthy fog to fulfill the user requests. In this paper, fuzzy logic is used as the basis for the evaluation while considering the availability and cost of fog. We propose Request Matching algorithm to identify a user request, and Fuzzybased Filtering algorithm to match the request with one of the predefined sets created and managed by the broker. In this paper, we present a use case that illustrates how fuzzy logic works in determining the trustworthiness of a fog. Our findings suggest that the algorithms can successfully provide users a trustworthy fog that matches their requirement. .bn (Wida Susanty Suhaili), g.m.lee@ljmu.ac.uk (Gyu-Myoung Lee)

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A Cooperative Fog Approach for Effective Workload Balancing

Cited by 71 publications

References 9 publications

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

Find my trustworthy fogs: A fuzzy-based trust evaluation framework

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