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Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system's capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market. [ABSTRACT FROM AUTHOR]

Nation's economy, safety, and quality of life are influenced by a well-behaved transportation system. Yet, demands in transportation are ever increasing due to trends in population growth, emerging technologies, and [...]

Function-as-a-Service (FaaS) has become a mainstream cloud computing paradigm for developers to build cloud-native applications in recent years. By taking advantage of serverless architecture, FaaS applications bring many desirable benefits, including built-in scalability, high availability, and improved cost-effectiveness. However, predictability and trade-off of performance and cost are still key pitfalls for FaaS applications due to poor infrastructure transparency and lack of performance and cost models that fit the new paradigm. In this study, we therefore fill this gap by proposing formal performance and cost modeling and optimization algorithms, which enable accurate prediction and fine-grained control over the performance and cost of FaaS applications. The proposed model and algorithms provide better predictability and trade-off of performance and cost for FaaS applications, which help developers to make informed decisions on cost reduction, performance improvement, and configuration optimization. We validate the proposed model and algorithms via extensive experiments on AWS. We show that the modeling algorithms can accurately estimate critical metrics, including response time, cost, exit status, and their distributions, regardless of the complexity and scale of the application workflow. Also, the depth-first bottleneck alleviation algorithm for trade-off analysis can effectively solve two optimization problems with fine-grained constraints. [ABSTRACT FROM AUTHOR]

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller's objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-levelAgreements, while leading to efficient resource provisioning. [ABSTRACT FROM AUTHOR]

ABSTRACT Advanced metering infrastructure (AMI) is one of the key elements in smart grid, which facilitates the communication of metering data to a substation in one direction and control messages in the reverse direction. Using wireless technologies and communication devices (e.g., smart meters), which are located in the physically insecure places, makes the AMI vulnerable to cyber attacks. In order to ensure the reliability and security of AMI, attack prevention techniques and intrusion detection systems (IDSs) should be in place to protect the AMI communications from malicious attacks and security breaches, respectively. In this paper, we discuss the security requirements and vulnerabilities of AMI and review the existing threat prevention and detection solutions. We propose an IDS for neighborhood area network (NAN) in AMI, taking into account the NAN-specific requirements. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the performance of cloud centers with high degree of virtualization and Poisson batch task arrivals. To this end, we develop an analytical model and validate it with an independent simulation model. Task service times are modeled with a general probability distribution, but the model also accounts for the deterioration of performance due to the workload at each node. The model allows for calculation of important performance indicators such as mean response time, waiting time in the queue, queue length, blocking probability, probability of immediate service, and probability distribution of the number of tasks in the system. Furthermore, we show that the performance of a cloud center may be improved if incoming requests are partitioned on the basis of the coefficient of variation of service time and batch size. [ABSTRACT FROM PUBLISHER]

Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that quality of service parameters remain within agreed limits. In this paper, we employ both the analytical and simulation modeling to addresses the complexity of cloud computing systems. Analytical model is comprised of distinct functional submodels, the results of which are combined in an iterative manner to obtain the solution with required accuracy. Our models incorporate the important features of cloud centers such as batch arrival of user requests, resource virtualization, and realistic servicing steps, to obtain important performance metrics such as task blocking probability and total waiting time incurred on user requests. Also, our results reveal important insights for capacity planning to control delay of servicing users requests. [ABSTRACT FROM AUTHOR]

In this paper, we propose an analytical performance model that addresses the complexity of cloud centers through distinct stochastic submodels, the results of which are integrated to obtain the overall solution. Our model incorporates the important aspects of cloud centers such as pool management, compound requests (i.e., a set of requests submitted by one user simultaneously), resource virtualization and realistic servicing steps. In this manner, we obtain not only a detailed assessment of cloud center performance, but also clear insights into equilibrium arrangement and capacity planning that allows servicing delays, task rejection probability, and power consumption to be kept under control. [ABSTRACT FROM AUTHOR]

Smart grid, which is an upgrade of power electric system, mainly relies on powerful communication networks to provide a secure, reliable and efficient information delivery. Updating a system as complex as the electrical power grid with a large number of components has the potential of introducing new security vulnerabilities into the system. Hence, security mechanisms should be deployed to protect the smart grid as a first wall of defence against malicious attacks. As a second wall of defence, there should be intrusion detection systems in place to protect the smart grid against any security breaches. In this work, we describe an anomaly-based intrusion detection system (IDS) for neighbourhood area network whose security is of critical importance in smart grid. [ABSTRACT FROM AUTHOR]

Background: Analytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will benefit patients, care providers, and governments significantly. This work is a novel approach to realize health analytics as services in critical care units in particular. Objective: To design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis. Methods: We present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and configurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs). Results: We demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the final deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids' NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and 16 CPU cores, respectively. The required amount of storage was estimated as 8.6 TB per year. There will always be 34.9 patients in SickKids NICU on average. Currently, 46% of patients cannot get admitted to SickKids NICU due to lack of resources. By increasing the capacity to 90 beds, all patients can be accommodated. For such a provisioning, Artemis-IC will need 16 TB of storage per year, 55 GB of memory, and 28 CPU cores. Conclusions: Our contributions in this work relate to a cloud architecture for the analysis of physiological data for clinical decisions support for tertiary care use. We demonstrate how to size the equipment needed in the cloud for that architecture based on a very realistic assessment of the patient characteristics and the associated clinical decision support algorithms that would be required to run for those patients. We show the principle of how this could be performed and furthermore that it can be replicated for any critical care setting within a tertiary institution. [ABSTRACT FROM AUTHOR]

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other. [ABSTRACT FROM PUBLISHER]