This research paper presents a new method focused on resource allocation in Fog computing systems. We propose an approach based on reinforcement learning that aims to maximize the utilization of Fog resources. By formulating the problem as a Markov Decision Process (MDP), we effectively address the complexities and uncertainties present in Fog computing systems. Through experimental evaluations, the results show the potential of our approach to enhance resource allocation in Fog computing environments.

Machine Learning research has progressed tremendously in recent years. Major fields that machine learning pushed its frontier were prediction and data modeling. In this work we evaluate the applicability of a handpicked prediction models on predicting inter-day aggregate network traffic. We chose models that work best with multi-variate feature space. They represent linear, decision trees, and neural network models. Over the years, predicting network traffic has resorted to predicting point values. This approach is not descriptive enough and naively gives a shallow conclusion about the data. We propose using a quantile loss function that predicts boundaries or prediction intervals. Our results show that linear models fared well compared to their simplicity while Long Short-Term Memory Neural Networks gave best results across all experiments.

In the recent decades computing systems have become ubiquitous in our daily life. Due to wear and tear, limited component lifetime, and extraneous factors, among other reasons, all of the systems that we design and implement are subject to failure. One of the main areas in the field of fault tolerance, system evaluation, is concerned with the analysis of systems and faults as well as their operational environments. In the context of system evaluation, this paper is concerned with failure modeling and fault prediction. We propose a model for evaluating network systems in the context of failure and repair. Although the focus here is on sensor networks, it can surely be extended to other situations. A systems engineer can use the proposed model to estimate the longevity of a system and plan appropriate maintenance during the system design or maintenance phases. The approach makes use of Markov chains to model failure states of the system based on historical data. The effectiveness of this model is demonstrated through preliminary experiments and a case study, which also confirm intuitions about the effects of network topology on the network's reliability.

A hierarchical cloud infrastructure involving cloud, cluster, and node controllers is the most preferred deployment architecture by the cloud providers since it helps them to achieve the desired scalability and geographical distribution. In this architecture, the cloud controller indirectly controls a large number of node controllers through a few cluster controllers. A Virtual Machine (VM) placement algorithm, therefore, should provide for intelligent cluster selection before node selection. To the best of our knowledge, there is no VM placement algorithm available that has addressed this issue. In this paper, we propose a new VM placement algorithm that intelligently selects the appropriate cluster, and then a node inside the cluster. We use multi-criteria-decision-analysis (MCDA) technique for this purpose. Simulation results have confirmed the superior performance of our algorithm.

Telecommunication networks are under stress due to rapid traffic increase. Since most of this traffic increase is due to large file transfers, this paper proposes a cross-layer transport protocol specifically designed to efficiently handle large transactions. Traffic generated from large transactions is shaped into a periodic succession of fixed-size data frames. Each transaction is then scheduled for transmission using a two-way reservation protocol. Simulation results show that the proposed approach is capable of significantly improving goodput and end-to-end delay relative to TCP, improving efficiency of bandwidth utilization by over 40%.

This paper focuses on the problem of resource consolidation management within cloud computing environments and extends our previous IMPROMPTU model which demonstrated the viability of distributed Multiple Criteria Decision Analysis (MCDA) to provide a resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and fewer service level agreement (SLA) violations as compared to other approaches. A core limitation of our previous work was that it only assessed the PROMETHEE II outranking-based MCDA method, leaving open the question of whether better outranking schemes exist and, more generally, what denotes the properties of good outranking approaches for this problem domain. This work addresses these deficiencies through extending the IMPROMPTU model to directly compare PROMETHEE II with ELECRE III and PAMSSEM II, two other well-known outranking-based MCDA schemes. An in-depth analysis of the generated simulation results are then used to highlight the core trade-offs between each of these MCDA approaches.

In computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility.

Ultra-wideband (UWB) is a new wireless technology that transfers data over shorter distances with higher data rates and lower transmission power. WiMedia Alliance's Media Access Control (MAC) protocols for UWB-based wireless personal area networks have become an international standard. The Distributed Reservation Protocol (DRP) is part of this standard that reserves the wireless channel on a slot-by-slot basis for different flows. Knowing that one of the most common use of DRP is to transport multimedia traffic, we propose distributed reservation algorithms for UWB-based home networks according to the video streaming traffic specification and quality-of-service requirement. The proposed algorithms also take the WiMedia UWB MAC policies into account. We analyze and validate the reservation algorithms and policies with a tiered overflow model, and evaluate and compare their performance with Network Simulator (NS-2) and a MPEG-4 video traffic generator. We further discuss the ways of improving video streaming quality and system resource utilization in UWB-based home networks.

With the proliferation of wireless technologies and the convenience they offer, transporting Quality-of-Service (QoS) demanding traffic such as compressed video over wireless links becomes a trend and a challenging issue. Among many factors, Media Access Control (MAC) protocols play an important role in the network stack to ensure the QoS provisioning for multimedia applications and the efficient utilization of wireless channels. Various contention-based or contention-free MAC protocols have been proposed to solve these problems. In this paper, we model, analyze with an existing framework, and evaluate two reservation algorithms, subframe-fit and isozone-fit, proposed for distributed reservation protocols exampled by WiMedia UWB MAC. The models have been validated by extensive simulations using ns-2 and an MPEG-4 traffic generator. We further improve the system performance by introducing cross-isozone allocation and on-demand compaction to isozone-fit, and discuss how to leverage both contention-based and contention-free MAC protocols.

In Voice Over IP (VoIP) systems, intruders can launch DoS attacks by establishing a large number of open connections to prevent the system from serving legitimate users. Existing defenses against DoS attacks on VoIP systems maintain full state information and thus are not scalable to implement at core routers. To this end, we adopt a two-layer aggregation scheme, termed Advanced Partial Completion Filters (APCF), to defend against DoS attacks without tracking state information of each individual connection. APCF provides adjustable control parameters so that both false alarms and detection rate can be controlled.

Virtual Private Networks (VPN) provide a secure and reliable communication between customer sites over a shared network. Two models were proposed for the service provisioning in VPNs. The "hose model" for VPNs alleviates the scalability problem of the "pipe model" by reserving bandwidths for aggregate ingress and egress requirements instead of between every pair of VPN endpoints. In this work, VPN endpoints are connected using a tree structure and our algorithm optimizes the total bandwidth reserved on edges of the VPN tree. We introduce a fast and efficient algorithm in finding the shared VPN tree to reduce the total provisioning cost. Our simulation results indicate that the VPN trees constructed by our proposed algorithm reduce bandwidth requirements as compared to previously proposed algorithms while having a much smaller execution time.

Virtual private networks (VPN) provide a secure and reliable communication between customer sites over a shared network. With increase in number and size of VPNs, providers need efficient provisioning techniques that adapt to customer demands. The recently proposed hose model for VPN alleviates the scalability problem of the pipe model by reserving for its aggregate ingress and egress bandwidth instead of between every pair of VPN endpoints. Existing studies on quality of service guarantees in the hose model either deal only with bandwidth requirements or regard the delay requirement as the main objective ignoring the bandwidth cost. In this work we propose a new approach to enhance the hose model to guarantee delay requirements between endpoints while optimizing the provisioning bandwidth cost. We connect VPN endpoints using a tree structure and our algorithm attempts to optimize the total bandwidth reserved on edges of the VPN tree. Our proposed approach takes into account the user preferences in meeting the delay requirements and provisioning cost to find the optimal solution of resource allocation problem. Our experimental results indicate that the VPN trees constructed by our proposed algorithm meet minimum delay requirements while reducing the bandwidth requirements as compared to previously proposed algorithms.