Industrial Control Systems (ICS) have been compromised by cyber-attacks with the development of computer and network technologies over the past few decades. Intrusion Detection Systems (IDSs) play a significant role in ensuring information security, and the key mechanism is to identify various attacks in the network accurately. One common strategy when implementing an IDS is to use a machine-learning based method. In this paper, we compare five different well-known Recurrent Neural Network (RNN) models to identify the most effective approach for use in an IDS. We evaluate and compare the models using the ‘ICSFlow’ dataset, based on a simulation of a bottle-filling factory. Our results show that the Multi-layered Bidirectional Gated Recurrent Unit (MBI-GRU) model outperforms other models in both accuracy and training efficiency.

The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical Model Predictive Control scheme, as well as, vehicle-to-vehicle communication in order to generate redundancy of collision risk information. The efficacy of the method is demonstrated in Eclipse Simulation of Urban Mobility (SUMO) considering the scenario of junction traffic management.

In the rapidly advancing domain of autonomous vehicle technology, the management of intersection traffic emerges as a demanding challenge. This paper describes the development and implementation of a novel testbed designed for the investigation of an autonomous intersection management system (AIMS). The research primarily focuses on the complex coordination required among autonomous vehicles for efficient operation at urban intersections. At the heart of this system lies the Intersection Coordination Unit (ICU), which centralizes traffic management. Robotic vehicles, equipped with On-Board Units (OBU) and advanced sensors, communicate wirelessly with the ICU. This communication facilitates real-time updates on vehicle positions and movements, enhancing environmental perception and aiding in collision prevention. The paper includes a validation section, demonstrating the testbed’s capabilities and the quality of data it generates.

Decentralized Cloud Storage (DCS) is considered to be the future for sustainable data storage within Web 3.0, in which we will move from a single cloud service provider to creating an ecosystem where anybody could be a cloud storage provider. Currently, the cloud storage market is highly dominated by centralized players like Amazon S3, Google Cloud, Box, etc. Decentralized projects like Storj, Filecoin, and Sia have seen rising popularity with the advent of Web 3.0 applications. At the same time, any blockchain network is susceptible to large-scale DDoS attacks. This work focuses on the Storj DCS, where we aimed to analyze the robustness of the system under the influence of a coordinated DDoS attack which can be carried out by an adversary or a group of adversaries taking down a set of storage nodes. The novelty of our work lies in threefold: First, we use statistical methods to mathematically model the content distribution as well as the loss of a file or a segment from the system. Our model captures both the cases where we have homogeneous and non-homogeneous nodes. Secondly, we develop a cost-analytic approach to perform a robustness analysis of the Storj system and implement the proposed model in MATLAB. Finally, we calculate the cost of a DDoS attack that the adversary has to incur in order to be successful with the attack. Also, we propose a set of better parametric choices for erasure piece distribution under which the system has proved to be more robust than the parametric values implemented in Storj DCS.

Mission-critical applications such as industrial control processes are evolving towards a new development paradigm by offloading their heavy computations to the edge of the emerging Fifth Generation Wireless Specifications (5G) network. In this manner, the applications can gain the economical and efficiency benefits of cloud computing, as well as reliable communication from the 5G network. However, the limited access to a configurable infrastructure of the 5G network and its edge computing infrastructure has restrained academic researchers from experimenting and validating their mission-critical application design under reasonable communication and computation scenarios. In this paper, we present a configurable mid-band 5G Stand-Alone (SA) deployment and demonstrate a control process that is running over the edge of the 5G network. We show in this paper a complete system setup for Control over the Edge (CoE) of the 5G network, and validate the feasibility of deploying similar mission-critical applications over the edge of 5G network.

With the help of modern technologies and advances in communication systems, the functionality of Industrial control systems (ICS) has been enhanced leading toward to have more efficient and smarter ICS. However, this makes these systems more and more connected and part of a networked system. This can provide an entry point for attackers to infiltrate the system and cause damage with potentially catastrophic consequences. Therefore, in this paper, we propose a digital twin-based security framework for ICS that consists of two parts: attack detection and attack mitigation. In this framework we deploy an intrusion detection system in digital domain that can detect attacks in a timely manner. Then, using our mitigation method, we keep the system stable with acceptable performance during the attack. Additionally, we implement our framework on a real testbed and evaluate its capability by subjecting it to a set of attacks.

The notion of Cloud RAN is taking a prominent role in narrative for the next generation wireless infrastructure. It is also seen as a mean to industrial communication systems. In order to provide reliable wireless connectivity for industrial deployments, by conventional means, the cloud infrastructure needs to be reliable and incur little latency, which however, is contradictory to the stochastic nature of cloud infrastructures. In this paper, we investigate the impact of stochastic delay on a radio resource allocation process deployed in Cloud RAN. We proceed to propose a strategy for realizing timely cloud responses and then adapt that strategy to a radio resource allocation problem. Further, we evaluate the strategies in an industrial IoT scenario using a simulated environment. Experimentation shows that, with our proposed strategy, a significant performance improvement on timely responses can be achieved even with noisy cloud environment. Improvements in resource utilization can be also attained for a resource allocation process deployed over Cloud RAN with this strategy.

Cloud computing has become a prominent technology for the computing paradigm in various industrial sectors nowadays. For most industrial applications to perform in real-time, the support of periodic computing is required. However, it remains a challenge when the computing is executed in a cloud, since both the network connection and the cloud environment are uncertain. In this paper, we propose a new architecture to deploy real-time applications in the cloud. We call it punctual cloud. We detail the implementation and demonstrate how punctual cloud is deployed in a cloud-native manner on Kubernetes. We evaluate the system’s performance with a real-time resource allocation problem and show that, compared to a system without punctual cloud, which has maximum 40% punctual deliveries, our proposed architecture can attain over 90% responses to be delivered punctually for the application, while also being capable of remedying the performance degradation caused by long and uncertain response delays in the system.

Service meshes factor out code dealing with inter-micro-service communication. The overall resilience of a cloud application is improved if constituent micro-services return stale data, instead of no data at all. This paper proposes and implements application agnostic caching for micro services. While caching is widely employed for serving web service traffic, its usage in inter-micro-service communication is lacking. Micro-services responses are highly dynamic, which requires carefully choosing adaptive time-to-life caching algorithms. Our approach is application agnostic, is cloud native, and supports gRPC. We evaluate our approach and implementation using the micro-service benchmark by Google Cloud called Hipster Shop. Our approach results in caching of about 80% of requests. Results show the feasibility and efficiency of our approach, which encourages implementing caching in service meshes. Additionally, we make the code, experiments, and data publicly available.

Summary This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Along with the recent popularity of Docker, the container-based virtualization starts receiving more attention for being a promising alternative. Since both of the virtualization solutions are not resource-free, their performance overheads would lead to negative impacts on the quality of Cloud services. To help fundamentally understand the performance difference between these two types of virtualization solutions, we use a physical machine with “just-enough” resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Although the container-based solution is undoubtedly lightweight, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed.

The Mobile Cloud Network is an emerging cost and capacity heterogeneous distributed cloud topological paradigm that aims to remedy the application performance constraints imposed by centralised cloud infrastructures. A centralised cloud infrastructure and the adjoining Telecom network will struggle to accommodate the exploding amount of traffic generated by forthcoming highly interactive applications. Cost effectively managing a Mobile Cloud Network computing infrastructure while meeting individual application's performance goals is non-trivial and is at the core of our contribution. Due to the scale of a Mobile Cloud Network, a centralised approach is infeasible. Therefore, in this paper a distributed algorithm that addresses these challenges is presented. The presented approach works towards meeting individual application's performance objectives, constricting system-wide operational cost, and mitigating resource usage skewness. The presented distributed algorithm does so by iteratively and independently acting on the objectives of each component with a common heuristic objective function. Systematic evaluations reveal that the presented algorithm quickly converges and performs near optimal in terms of system-wide operational cost and application performance, and significantly outperforms similar naïve and random methods.

Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. Therefore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing, and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

The spot pricing scheme has been considered to be resource-efficient for providers and cost-effective for consumers in the Cloud market. Nevertheless, unlike the static and straightforward strategies of trading on-demand and reserved Cloud services, the market-driven mechanism for trading spot service would be complicated for both implementation and understanding. The largely invisible market activities and their complex interactions could especially make Cloud consumers hesitate to enter the spot market. To reduce the complexity in understanding the Cloud spot market, we decided to reveal the backend information behind spot price variations. Inspired by the methodology of reverse engineering, we developed a Predator-Prey model that can simulate the interactions between demand and resource based on the visible spot price traces. The simulation results have shown some basic regular patterns of market activities with respect to Amazon's spot instance type m3.large. Although the findings of this study need further validation by using practical data, our work essentially suggests a promising approach (i.e.~using a Predator-Prey model) to investigate spot market activities.

In this paper, we propose a telco cloud meta-model that can be used to simulate different infrastructure configurations and explore their consequences for system performance and costs. To achieve this, we analyse current telecommunication and data centre infrastructure paradigms, describe the architecture of the telco cloud, and detail the benefits of merging both infrastructures in a unified system. Next, we detail the dynamics of the telco cloud and identify the components that are the most relevant from the perspective of modelling performance and cost. As a number of well established simulation technologies exist for most of the telco cloud components, we survey existing models in an attempt to construct a suitable composite meta-model. Finally, we present a showcase scenario to demonstrate the scope of our telco cloud simulator.

As a cost-effective option for Cloud consumers, spot service has been considered to be a significant supplement for building a full-fledged market economy for the Cloud ecosystem. However, unlike the static and straightforward way of trading on-demand and reserved Cloud services, the market-driven regulations of employing spot service could be too complicated for Cloud consumers to comprehensively understand. In particular, it would be both difficult and tedious for potential consumers to determine suitable bids from time to time. To reduce the complexity in applying spot resources, we propose to use a feedback control to help make bidding decisions. Based on an arccotangent-function-type system model, our novel bidding mechanism imitates fuzzy and intuitive human activities to refine and issue new bids according to previous errors. The validation is conducted by using Amazon's historical spot price trace to perform a set of simulations and comparisons. The result shows that the feedback control-based mechanism obtains a better trade-off between bidding rationality and success rate than the other five comparable strategies. Although this mechanism is only for black-box bidding (price prediction) at this current stage, it can be conveniently and gradually upgraded to take into account external constraints in the future.

Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Our results show that the user and the session arrival rates for the TV4 workload does not follow a Poisson process. The arrival rate distribution is modeled using a lognormal distribution while the inter-arrival time distribution is modeled using a stretched exponential distribution. We observe the "impatient user" behavior where users abandon streaming sessions after minutes or even seconds of starting them. Both very popular videos and non-popular videos are particularly affected by impatient users. We investigate if this behavior is an invariant for VoD workloads.

Long Term Evolution (LTE) is the new generation of infrastructure-based wireless technologies that can be used for vehicular communications, and already a standard considered as the preliminary version of 4G mobile communications. It provides a cellular networks-based solution, generally called vehicle to infrastructure (V2I), for vehicular safety applications. In this work, we evaluate the performance of different downlink scheduling strategies under various urban and rural scenarios, in which voice, video and safety data traffic coexist. The traffic safety application scenarios we consider are variations of collision avoidance using LTE-based broadcast communication. Our main evaluation criteria are delay and packet loss rate. We present our findings for each scenario and compare the results.

Multi-step ahead response time prediction of CPU constrained computing systems is vital for admission control, overload protection and optimization of resource allocation in these systems. CPU constrained computing systems such as web servers can be modeled as single server queuing systems. These systems are stochastic and nonlinear. Thus, a well-designed nonlinear prediction scheme would be able to represent the dynamics of such a system much better than a linear scheme. A nonlinear autoregressive neural network with exogenous inputs based multi-step ahead response time predictor has been developed. The proposed estimator has many promising characteristics that make it a viable candidate for being implemented in admission control products for computing systems. It has a simple structure, is nonlinear, supports multi-step ahead prediction, and works very well under time variant and non-stationary scenarios such as single server queuing systems under time varying mean arrival rate. Performance of the proposed predictor is evaluated through simulation. Simulations show that the proposed predictor is able to predict the response times of single server queuing systems in multi-step ahead with very good precision represented by very small mean absolute and mean squared prediction errors.

Advanced telecommunication applications are often based on a multi-tier architecture, with application servers and database servers. With a rapidly increasing development of cloud computing and data centers, characterizations of the dynamics for database servers during changing workloads will be a key factor for analysis and performance improvements in these applications. We propose a multi-step ahead response time predictor for database queries based on a nonlinear autoregressive neural network model with exogenous inputs. The estimator shows many promising characteristics which make it a viable candidate for being implemented in admission control products for database servers. Performance of the proposed predictor is evaluated through experiments on a lab setup with a MySQL-server.

Resource management in IT-enterprises gain more and more attention due to high operation costs. For instance, web sites are subject to very changing traffic-loads over the year, over the day, or even over the minute. Online adaption to the changing environment is one way to reduce losses in the operation. Control systems based on feedback provide methods for such adaption, but is in nature slow, since changes in the environment has to propagate through the system before being compensated. Therefore, feed-forward systems can be introduced that has shown to improve the transient performance. However, earlier proposed feed-forward systems have been based on offline estimation. In this article we show that off-line estimations can be problematic in online applications. Therefore, we propose a method where parameters are estimated online, and thus also adapts to the changing environment. We compare our solution to two other control strategies proposed in the literature, which are based on off-line estimation of certain parameters. We evaluate the controllers with both discrete-event simulations and experiments in our testbed. The investigations show the strength of our proposed control system.

This paper investigates the dimensioning of a server used for external overload control in service architecture. Great savings can be obtained by an operator if the dimensioning analysis is performed correctly. As one of the main parts of this paper it is shown that Poissonian arrivals is a good assumption for some services in service architectures. Methods that can be used for dimensioning are presented and examples are provided

All service control nodes can be modelled as a server system with one or more servers processing incoming requests. In this paper we show how non-linear control theory may be used when analyzing admission control mechanisms for server systems. Two models are developed, one linear and one non-linear. We show that, due to the non-linearities appearing in a real server system, linear control theory is sufficient when designing controllers for these systems. With non-linear analysis, however, the dynamics of a server system may be analysed and taken care of by choosing the controller parameters appropriately.