Quantum networks are expected to enable distributed quantum computing, secure communication, and global entanglement distribution. However, operating such networks presents significant challenges, including stochastic quantum processes, fragile entanglement resources, dynamic topology, and cross-layer control requirements. Current quantum network control architectures largely rely on centralized or hierarchical controllers inspired by classical software-defined networking (SDN). While effective for small testbeds, these approaches face scalability, latency, and reliability limitations as quantum networks grow.

Current approaches to resilience for highly distributed, heterogeneous, large-scale scientific workflows are limited. Most existing workflow and resource management systems have a single point of failure and resilience strategies are often static, depend on a centralized control, and require considerable design effort from experts. The increasing scale and complexity of workflows coupled with limited resilience capabilities in centralized systems necessitates a fully decentralized, adaptive resource management approach. This paper addresses a very important slice of the overall problem by leveraging the advances in multi-agent systems (MAS). In particular, we explore the suitability of a MAS consisting of globally distributed agents to perform distributed job selection from a dynamic job pool in a truly decentralized, performant, and resilient manner. We present a novel consensus formulation of the distributed job selection problem. By introducing a cost function encapsulating the requirements and constraints of the job and resource loads, we design a novel, greedy consensus algorithm leveraging the Practical Byzantine Fault Tolerance (PBFT)-based consensus method, allowing agents to collectively select jobs in a resilient manner. We compared our algorithms with other state of the art approaches by deploying them in a network testbed infrastructure to emulate distributed job selection. Our evaluation results demonstrated that our greedy consensus algorithm employing the cost-function and PBFT-based consensus method outperforms the ones using the vanilla PBFT-based consensus method - improving scheduling latency by as much as 63.5 % and reducing resource idle time by as much as 63.8 %, with benefits increasing with higher numbers of agents emulated.

In contemporary scientific research, data acquisition and analysis platforms have grown increasingly complex, often spanning multiple facilities with diverse internal structures. Efficiently managing the interactions between job scheduling, resource allocation, and networking across these distributed systems requires a robust simulation framework. However, existing simulators fall short in capturing the detailed interactions necessary for comprehensive analysis of large-scale distributed environments. To address this gap, we introduce DISTRI, a versatile framework specifically designed for the development and testing of distributed multi-facility workflows. DISTRI allows for customizable facility configurations and includes built-in support for distributed, resilient scheduling and resource management, alongside detailed network simulation for data communication. Key features of DISTRI encompass inter- and intra-facility resource management, agent-based distributed scheduling, and extensive performance metrics logging for both resource and network management. By providing these essential tools, DISTRI enables thorough analysis and optimization, thereby advancing research in the resilience and efficiency of multi-facility systems.

Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the work-flow execution cause loss of scientific productivity and inefficient use of the infrastructure. Hence, detecting, diagnosing, and mitigating these anomalies are immensely important for reliable and performant scientific workflows. Since these workflows rely heavily on high-performance network transfers that require strict QoS constraints, accurately detecting anomalous network performance is crucial to ensure reliable and efficient workflow execution. To address this challenge, we have developed X-FLASH, a network anomaly detection tool for faulty TCP workflow transfers. X-FLASH incorporates novel hyperparameter tuning and data mining approaches for improving the performance of the machine learning algorithms to accurately classify the anomalous TCP packets. X-FLASH leverages XGBoost as an ensemble model and couples XGBoost with a sequential optimizer, FLASH, borrowed from search-based Software Engineering to learn the optimal model parameters. X-FLASH found configurations that outperformed the existing approach up to 28%, 29%, and 40% relatively for F-measure, G-score, and recall in less than 30 evaluations. From (1) large improvement and (2) simple tuning, we recommend future research to have additional tuning study as a new standard, at least in the area of scientific workflow anomaly detection.

Network traffic behavior is noisy and random, making it difficult to find patterns and predict future behavior. In this paper, we develop statistical models that use multivariate data model, incorporating seasonality, peak frequencies, and link relationships to improve future predictions. Using Fourier Transforms to extract seasons and peak frequencies from individual traces, we perform seasonality tests and ARIMA measures to determine optimal parameters to use in our prediction model. We develop a SARIMA multivariate model using real network traces to show improved prediction accuracy with better RMSE and smaller confidence intervals when compared to univariate approaches.

As the numbers of internet users and connected devices continue to multiply, due to big data and Cloud applications, network traffic is growing at an exponential rate. WAN networks, in particular, are witnessing very large traffic spikes cause by large file transfers that last from a few minutes to hours on network links and there is a need to develop innovative ways in which flows can be managed in real-time.In this work, we develop a reinforcement learning approach, in particular Upper-Confidence Algorithm, to learn optimal paths and reroute traffic to improve network utilization. We present throughput and flow diversions using Mininet and demo the technique using Chameleon's Testbed (Bring-Your-Own-Controller [BYOC] functionality). This work is initial implementation towards DeepRoute, which combines Deep reinforcement learning algorithms with SDN controllers to create and route traffic using deployed OpenFlow switches.

Summary Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault‐tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault‐tolerant techniques exist for building self‐autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault‐tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis `on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

Cloud computing provides outsourcing of resources bringing economic benefits. The outsourcing however does not allow data owners to outsource the responsibility of confidentiality, integrity and access control, as it still is the responsibility of the data owner. As cloud computing is transparent to both the programmers and the users, it induces challenges that were not present in previous forms of distributed computing. Furthermore, cloud computing enables its users to abstract away from low-level configuration such as configuring IP addresses and routers. It creates an illusion that this entire configuration is automated. This illusion is also true for security services, for instance automating security policies and access control in cloud, so that individuals or end-users using the cloud only perform very high-level (business oriented) configuration. This paper investigates the security challenges posed by the transparency of distribution, abstraction of configuration and automation of services by performing a detailed threat analysis of cloud computing across its different deployment scenarios (private, bursting, federation or multi-clouds). This paper also presents a risk inventory which documents the security threats identified in terms of availability, integrity and confidentiality for cloud infrastructures in detail for future security risks. We also propose a methodology for performing security risk assessment for cloud computing architectures presenting some of the initial results.

Cloud transformations require dynamic redistribution of resources across cloud infrastructure. From a legal perspective this movement of data from one data processor to another without the explicit consent of the data subject is a threat to data privacy. Levels of assurance and accountability have to be provided from the cloud infrastructure providers to the data subject in order to maintain trust. In cases of Cloud Transformation multiple providers are present and passing accountability down the chain is essential. Existing Service Level Agreements (SLA) and policy based privacy implementations fail to provide the flexibility and accountability needed in establishing these new relationships. By introducing combined risk and privacy assessment alongside SLA negotiation, the legal and data management implications of Cloud Transformation events can be better accounted for. This will better protect the privacy of data subjects and increase confidence and trust in the Cloud computing platform.

Despite their increasing popularity, agent-based models are hard to test, and so far no established testing technique has been devised for this kind of software applications. Reverse engineering an agent-based model specification from model simulations can help establish a confidence level about the implemented model and in some cases reveal discrepancies between observed and normal or expected behaviour. In this study, a multiobjective optimisation technique based on a simple random search algorithm is deployed to dynamically infer and refine the specification of three agent-based models from their simulations. The multiobjective optimisation technique also incorporates a dynamic invariant detection technique which serves to guide the search towards uncovering new model behaviour that better captures the model specification. The Non-dominated Sorting Genetic Algorithm (NSGA-II) was also deployed to replace the random search algorithm, and the results from both approaches were compared. While both algorithms revealed good potential in capturing the model specifications, the pure exploratory nature of random search was found more suitable for the application at hand, compared to the balanced exploitation/exploration nature of genetic algorithms in general.

The principles of risk management have been introduced in grid computing to help document and anticipate certain risks and manage them to ensure job executions are successful. Clouds are more complex environments with further concerns like risk, trust, eco-efficiency, green, security or cost. In this paper we present ongoing research work to analyze and address the risk factor in clouds with the aim of optimizing cloud services. The main contribution of this work is the presentation of a methodology for performing risk assessment in cloud environments including the target use cases, risk identification, mitigation and monitoring. Together with the corresponding mitigation strategies, the methodology provides technological assurance that will lead to a high confidence of Cloud service consumers on one side, and a cost effective and reliable productivity of cloud Service/Infrastructure Providers on the other side. The design of the risk assessment framework and its software toolkit implementation are part of the research and development work of the OPTIMIS (Optimized Infrastructure Services) project whose objective is to enable an open and dependable Cloud Service Ecosystem that delivers IT services that are adaptable, reliable, auditable and sustainable both ecologically and economically. The paper presents some preliminary results on the risk assessment of a Service/Infrastructure Provider at the cloud service deployment stage.