Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 981-986· 0 citations· 19 references
Abstract
As the modern network infrastructure continue to become more and more complex, massive set of firewall rules have been produced, which causes large package classification latency and poor resource utilization. Firewall rule ordering is an NP-hard mathematical programming problem in which the wrong sequencing of firewall rules maximizes the cost of matching, as well as presents the possibility of policy violation. The current meta-heuristic algorithms are said to be constrained by predetermined traffic patterns and unresponsive to real time. In this paper, an adaptive Reinforcement Learning (RL) framework is introduced to the dynamic optimization of firewall rules. The issue is presented as a Markov Decision Process (MDP) and Deep Q-Network (DQN) agent can observe the frequency of rule hits and dependency restrictions and propose the best reorganization. In order to solve the scalability issue, A state-space dimensionality reduction algorithm is adopted, where we target high-traffic rule sets. The Directed Acyclic Graph (DAG) is used to make sure that the reordering of rules does not compromise the semantics of security policies. The experimental results prove that the throughput is increased and the cost of rule comparisons are decreased as compared to fixed configurations. Our framework is better in changing traffic dynamics.
This survey delivers the first systematic exploration of how artificial intelligence can bolster SR, from traffic classification and segment-list computation to fast reroute, service-function chaining, and multi-domain orchestration by spanning supervised and unsupervised learning, reinforcement learning, and hybrid pipelines that fuse forecasting, neural optimization, and heuristic search.
Noha W. Hassan, M. Khalil, Hazem M. Abbas· Telecommunications Systems· 0 citations
Modern network management requires dynamically reacting to a broad set of events occurring in the network. Detecting the traffic behaviors associated with these events requires identifying patterns and computing metrics over high-velocity streams of network packets flowing through a link, router, or switch. To facilitate performing computations, a number of traffic query languages have been proposed in the network systems community. However, existing traffic query languages primarily focus on expressing computations that can be efficiently implemented on particular hardware or software platforms (such as programmable switch ASICs or multi-core CPUs) and do not support the full range of behaviors required by modern network management tasks. For example, languages like Sonata efficiently support count-based metrics on switch ASICs, but fail to support complex inter-packet pattern identification, whereas a language like FLM can identify patterns on switch ASICs but cannot compute quantitative metrics. In this paper, we argue that an ideal traffic query language should give network operators flexibility to detect a diversity of events without sacrificing implementation efficiency. To create such a language, two key challenges must be overcome: 1) understanding the variety and differences of existing query languages and 2) efficiently using the available resources to execute those queries. As an initial step towards addressing these challenges, we present a stream processing model called Streaming Intermediate Representation (SIR), which can represent a wide range of queries while providing bounds on the resource usage required for efficient implementations. We demonstrate the generality of the model by building compilers from three representative query languages into SIR. We also outline a framework for reasoning about the resource usage of queries represented in SIR and present the analysis of example queries compiled into SIR from different query languages.
Anthony Dario, Chris Misa, Z. Ariola· Proceedings of the ACM SIGCO...· 0 citations
Networks with highly dynamic data transmission demands and network topologies are common in real world. A fundamental problem in such networks is achieving scalable traffic allocation to maximize long-term total throughput under link capacity constraints. However, state-of-the-art (SOTA) works lack scalability. This is primarily due to two reasons in large-scale networks: first, they require solving constrained optimization problems online, which leads to high decision latency; second, they rely on reinforcement learning algorithms for policy optimization, which are inefficient in exploration and challenging to train effectively. To address these issues, we propose the Fast Networked Control (FNC) policy framework, which firstly utilizes parallelizable neural network modules to process the state and generate raw decisions, followed by basic operations such as normalizations and comparisons, which do not require iteration or optimization, to obtain decisions that satisfy the constraints. Hence, FNC policy avoids solving constrained optimization problems and supports parallel execution, significantly reducing decision latency. Furthermore, this policy preserves gradient flow and supports backpropagation, which enable us to design an imitation learning algorithm to efficiently train the policy in an end-to-end manner. Experiments in large-scale networks show that our FNC policy achieves an average 8% improvement in demands satisfaction and 10 times reduction in decision latency versus SOTA works.
Zhaoxing Yang, Guiyun Fan, An-Jie Cao et al.· IEEE Transactions on Network...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
The results indicate that iScavenger provides configurable operating points in the latency–utilization trade-off, limiting additional Sticky-flow RTT while achieving higher background throughput than conservative baseline policies, and highlight the potential of short-term traffic-demand prediction for proactive contention management in ATSSS-enabled multi-access networks.
Shah M. Emad Uddin, Karl-Johan Grinnemo, Arunselvan Ramaswamy et al.· IEEE Open Journal of the Com...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
Mamoon M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
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