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1. Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning

   https://doi.org/10.1007/978-3-030-59016-1_13  

   Hsieh, L.; Liu, H.; Guo, Y.; Gazda, R. (January 2020, Springer LNCS Wireless Algorithms, Systems, and Applications)

   Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks (DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns the optimal task assignment policy with no assumption on the knowledge of the underlying dynamics. In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning. The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards, while scaling reasonably well as the network size increases. 

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2. Reducing Access Disparities in Networks using Edge Augmentation

   https://doi.org/10.1145/3593013.3594105  

   Bashardoust, Ashkan; Friedler, Sorelle; Scheidegger, Carlos; Sullivan, Blair D.; Venkatasubramanian, Suresh (June 2023, Conference on Fairness, Accountability, and Transparency)

   In social networks, a node’s position is, in and of itself, a form of social capital. Better-positioned members not only benefit from (faster) access to diverse information, but innately have more potential influence on information spread. Structural biases often arise from network formation, and can lead to significant disparities in information access based on position. Further, processes such as link recommendation can exacerbate this inequality by relying on network structure to augment connectivity. In this paper, we argue that one can understand and quantify this social capital through the lens of information flow in the network. In contrast to prior work, we consider the setting where all nodes may be sources of distinct information, and a node’s (dis)advantage takes into account its ability to access all information available on the network, not just that from a single source. We introduce three new measures of advantage (broadcast, influence, and control), which are quantified in terms of position in the network using access signatures – vectors that represent a node’s ability to share information with each other node in the network. We then consider the problem of improving equity by making interventions to increase the access of the least-advantaged nodes. Since all nodes are already sources of information in our model, we argue that edge augmentation is most appropriate for mitigating bias in the network structure, and frame a budgeted intervention problem for maximizing broadcast (minimum pairwise access) over the network. Finally, we propose heuristic strategies for selecting edge augmentations and empirically evaluate their performance on a corpus of real-world social networks. We demonstrate that a small number of interventions can not only significantly increase the broadcast measure of access for the least-advantaged nodes (over 5 times more than random), but also simultaneously improve the minimum influence. Additional analysis shows that edge augmentations targeted at improving minimum pairwise access can also dramatically shrink the gap in advantage between nodes (over ) and reduce disparities between their access signatures. 

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3. Towards a Threat Model for Fog Computing

   https://doi.org/10.1109/UEMCON47517.2019.8993005  

   Karim, Yasser; Hasan, Ragib (October 2019, 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON))

   null (Ed.)

   In recent years, the addition of billions of Internet of Thing (IoT) device spawned a massive demand for computing service near the edge of the network. Due to latency, limited mobility, and location awareness, cloud computing is not capable enough to serve these devices. As a result, the focus is shifting more towards distributed platform service to put ample computing power near the edge of the networks. Thus, paradigms such as Fog and Edge computing are gaining attention from researchers as well as business stakeholders. Fog computing is a new computing paradigm, which places computing nodes in between the Cloud and the end user to reduce latency and increase availability. As an emerging technology, Fog computing also brings newer security challenges for the stakeholders to solve. Before designing the security models for Fog computing, it is better to understand the existing threats to Fog computing. In this regard, a thorough threat model can significantly help to identify these threats. Threat modeling is a sophisticated engineering process by which a computer-based system is analyzed to discover security flaws. In this paper, we applied two popular security threat modeling processes - CIAA and STRIDE - to identify and analyze attackers, their capabilities and motivations, and a list of potential threats in the context of Fog computing. We posit that such a systematic and thorough discussion of a threat model for Fog computing will help security researchers and professionals to design secure and reliable Fog computing systems. 

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4. Towards a Threat Model for Fog Computing

   Karim, Md. Yasser; Hasan, Ragib (October 2019, IEEE UEMCON 2019)

   In recent years, the addition of billions of Internet of Thing (IoT) device spawned a massive demand for computing service near the edge of the network. Due to latency, limited mobility, and location awareness, cloud computing is not capable enough to serve these devices. As a result, the focus is shifting more towards distributed platform service to put ample com- puting power near the edge of the networks. Thus, paradigms such as Fog and Edge computing are gaining attention from researchers as well as business stakeholders. Fog computing is a new computing paradigm, which places computing nodes in between the Cloud and the end user to reduce latency and increase availability. As an emerging technology, Fog computing also brings newer security challenges for the stakeholders to solve. Before designing the security models for Fog computing, it is better to understand the existing threats to Fog computing. In this regard, a thorough threat model can significantly help to identify these threats. Threat modeling is a sophisticated engineering process by which a computer-based system is analyzed to discover security flaws. In this paper, we applied two popular security threat modeling processes – CIAA and STRIDE – to identify and analyze attackers, their capabilities and motivations, and a list of potential threats in the context of Fog computing. We posit that such a systematic and thorough discussion of a threat model for Fog computing will help security researchers and professionals to design secure and reliable Fog computing systems. 

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5. Harnessing HPC resources for CMS jobs using a Virtual Private Network

   https://doi.org/10.1051/epjconf/202125102032  

   Tovar, Benjamin; Bockelman, Brian; Hildreth, Michael; Lannon, Kevin; Thain, Douglas (January 2021, EPJ Web of Conferences)

   Biscarat, C.; Campana, S.; Hegner, B.; Roiser, S.; Rovelli, C.I.; Stewart, G.A. (Ed.)

   The processing needs for the High Luminosity (HL) upgrade for the LHC require the CMS collaboration to harness the computational power available on non-CMS resources, such as High-Performance Computing centers (HPCs). These sites often limit the external network connectivity of their computational nodes. In this paper we describe a strategy in which all network connections of CMS jobs inside a facility are routed to a single point of external network connectivity using a Virtual Private Network (VPN) server by creating virtual network interfaces in the computational nodes. We show that when the computational nodes and the host running the VPN server have the namespaces capability enabled, the setup can run entirely on user space with no other root permissions required. The VPN server host may be a privileged node inside the facility configured for outside network access, or an external service that the nodes are allowed to contact. When namespaces are not enabled at the client side, then the setup falls back to using a SOCKS server instead of virtual network interfaces. We demonstrate the strategy by executing CMS Monte Carlo production requests on opportunistic non-CMS resources at the University of Notre Dame. For these jobs, cvmfs support is tested via fusermount (cvmfsexec), and the native fuse module. 

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