A Scalable Architecture for Remote Model Context Protocol Servers in Enterprise Database Environments

Advancements in technology enable integration of multiple devices on a single core, resulting in increased on chip power and temperature densities. Higher temperatures, in turn, present a significant challenge for reliability. In this work we propose a comprehensive framework for analyzing reliability of multi-core systems, considering permanent faults. We show that aggressive power management can have an impact on reliability due to temperature cycling. Our cycle-accurate simulation methodology shows fine-grained variations of device failure rates over short time scales, thus enabling workload analysis and scheduling to control the reliability impact. On the other hand, the statistical reliability simulator and optimizer give a view into the long time horizon reliability analysis—over system lifetime, and help us optimize a power management policy under reliability and performance constraints. We show that our optimization strategy can achieve large power savings while still meeting the reliability and performance constraints.

The Network security aims to protect the network and the network-accessible resources from unauthorized access. However, the dynamic characteristics of contemporary networks combined with their increased size makes the vision of absolute network security almost impossible. Specifically, networks are vulnerable to infection by different types of electronic attacks such as viruses, Trojan horses or eavesdroppers that exploits the loopholes in the security mechanisms of networks [FAGY00]. Guaranteeing an acceptable level of security for a prospective system represents a common problem in systems engineering. Network security, is defined as a Non-Functional Requirement (NFR) that is influenced by functional aspects of the system such as the specification of antivirus and firewall mechanism employed on the network. This area of research has gained considerable popularity due to the implications it has on users’ satisfaction, business reputation and performance. Therefore, being able to quantify the security level of a future network early in the design phase is of vital importance to its sustainability. The need to validate security requirements early has been addressed also by Lamsweerde [CILN02] and Crook [L04].

With the development of power communication networks, the demand of information security involved in power network services also increases. Traditional secure communication technology have not been able to guarantee the security of the information transmitted in absolute security. This paper focuses on the problem of secure transmission of power communication networks, and proposes a suitable quantum secure communication technology scheme to ensure the key rate of the long-distance quantum secure communication and the security of the power communication network.

In restructured electric power systems under electricity market regulations, Distribution Companies (DISCOs) mainly aim at maximizing their profit subject to safe operation of the network. In this regard, optimal planning of Energy Storage Systems (ESSs) can be a very effective method to maximize DISCOs profit. This paper addresses an optimal methodology to signify the location and capacity of ESSs in distribution network under electricity market environment. The proposed optimal ESSs planning aims at maximizing DISCO profit subject to safe and secure operation constraints (e.g., voltage and flow limits). The maximization problem is mathematically expressed as a mixed integer non-linear programming and solved using particle swarm optimization algorithm. Simulations are carried out on a typical distribution network. Simulation results demonstrate significant impact of ESSs on the network operation, security constraint, and costs. The proposed planning not only increases the DISCO profit but also guarantees the safe operation of the network. As well, several stativity analyses are carried out to indicate the impact of parameters on the planning.

Wireless Sensor Networks (WSN) offer a significant potential for ubiquitous monitoring and control in future internet-connected pervasive systems architectures. This can be achieved through seamless integration along existing networking devices and systems, while taking account the data-driven nature and security constraints imposed upon such low power, efficient embedded networked platforms. We argue that only through a combined security and communication approach can the relevant goals be achieved, by considering security issues as an integral part of high level layers of the protocol stack. This paper presents an analysis of security and communication constraints of IPv6-based wireless sensor networks at the network and transport layer. Leveraging the Contiki operating system for resource constrained devices along with the ContikiSec security layer and RPL, IPv6 Protocol for Low Power and Lossy Networks, helpful insight is achieved for evaluation and deployment. The results of the analysis are discussed and functions of its modules are explained. This opens up the path towards robust implementation in real world applications following comparative experimental work on simulated and emulated mote platforms.

Background. At the present stage, electronic communications must provide: ultra-fast data exchange between production systems, which increases productivity and safety; the use of robotic systems and artificial intelligence to support people in production processes. Reliable electronic communications ensure the security of industrial networks and personal data; intelligent factories with network sensors and automated data processing systems. In this context, new challenges are facing electronic communications and, accordingly, new problems are identified, the solution of which requires conducting promising scientific research. Objective. The purpose of this work is to generalise and highlight the key areas of development of electronic communications, which determine the current and future state of the industry. It is aimed at outlining scientific challenges and prospects associated with the digitalisation of society, the growth of demand for fast, reliable and secure data transmission. Methods. Analysis of factors and technologies that influence the growth of needs for the further development of electronic communications, the quality of telecommunications services in fifth and next generation networks, the introduction of new types of electronic communications and their integration with advanced information technologies. Results. The presented research directions determine the future of electronic communications. Research in these areas will ensure the sustainable development of the industry, taking into account the growing needs of modern society in fast, secure and reliable data transmission technologies. Conclusions. The main goal of the 6G direction and future generations of networks is to create ultra-fast, ultra-reliable and intelligent networks capable of supporting new types of services and applications, from autonomous control and remote medicine to full-scale multimedia communications. The direction of quantum communications is a revolutionary direction that promises to ensure absolute security of information transmission and open up new opportunities for communication. The direction of IoT scaling is a key direction that determines the future of many communication technologies. The research area that includes artificial intelligence (AI) and machine learning (ML) calls for further integration of AI and ML into all aspects of electronic communications, leading to smarter, more efficient and secure networks. Security and privacy in electronic communications is a dynamic area that is constantly evolving due to the growth of cyber threats and technological advancements. Edge and Fog Computing are key technologies for the future of electronic communications, and their development requires an interdisciplinary approach that combines computer science, telecommunications, AI and IoT. Research in the field of NFV and SDN is necessary to improve the performance, automation and security of networks. They play a key role in the future of 5G/6G, cloud computing, IoT and industrial networks. Green communications is an important research area that aims to improve the energy efficiency and sustainability of telecommunications networks. Networks for critical applications are a key element of modern infrastructures, and their development requires an interdisciplinary approach that combines telecommunications, cybersecurity, artificial intelligence and other fields. Holographic and tactile communication interaction technologies will become the basis of future digital communications, changing the way people and machines interact.

Reliability based design optimization is a methodology for nding optimized designs that are characterized with a low probability of failure. Primarily, reliability based design optimization consists of optimizing a merit function while satisfying reliability constraints. The reliability constraints are constraints on the probability of failure corresponding to each of the failure modes of the system or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. During the last few years, a variety of dierent formulations have been developed for reliability based design optimization. Traditionally, these have been formulated as a doubleloop (nested) optimization problems. The upper level optimization loop generally involves optimizing a merit function subject to reliability constraints and the lower level optimization loop(s) compute the probabilities of failure corresponding to the failure mode(s) that govern the system failure. This formulation is, by nature, computationally intensive. Researchers have provided sequential strategies to address this issue, where the deterministic optimization and reliability analysis are decoupled, and the process is performed iteratively until convergence is achieved. These methods, though attractive in terms of obtaining a workable reliable design at considerably reduced computational costs, often lead to premature convergence and therefore, yield spurious optimal designs. In this paper, a novel unilevel formulation for reliability based design optimization is developed. In the proposed formulation, the lower level optimization (evaluation of reliability constraints in the double-loop formulation) is replaced by its corresponding rst order Karush-Kuhn-Tucker (KKT) necessary optimality conditions at the upper level optimization. Such a replacement is equivalent to solving the original nested optimization if the constraint qualication conditions are satised. It is shown through the use of test problems that the proposed formulation is numerically robust (stable) and computationally ecient compared to the existing approaches for reliability based design optimization.

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

Replication in a data grid reduces access latency and bandwidth consumption. However, when different sites hold replicas of a particular file, there is a significant benefit realized by selecting the best replica from among them. The best replica is the one that optimizes the desired performance criterion such as absolute performance (i.e. speed), cost, security or transfer time. By selecting the best replica, the access latency can be minimized. We develop a predictive framework that uses data from various sources and predicts transfer times of the sites that host replicas. With this estimate, one site can request the replica from the site that has the lowest transfer time. We use a neural network (NN) for transfer time prediction of different sites that currently hold file replicas. We compare the results with a multi-regression model and the simulation results demonstrate that the neural network technique is capable of predicting transfer time more accurately than the regression based model.

In deregulated power markets, Independent System Operators (ISOs) maintains adequate reserve requirement in order to respond to generation and system security constraints. In order to estimate accurate reserve requirement and handling non-linearity and non-convexity of the problem, an efficient computational framework is required. In addition, ISO executes SCUC in order to reach the consistent operation. In this paper, a novel type of application which is Benders decomposition (BD) and Mixed integer non linear programming (MINLP) can be used to assess network security constraints by using AC optimal power flow (ACOPF) in a power system. It performs ACOPF in network security check evaluation with line outage contingency. The process of solving modified system would be close to optimal solution, the gap between the close to optimal and optimal solution is expected to determine whether a close to optimal solutionis accepetable for convenientpurpose. This approach drastically betters the fast computational requirement in practical power system .The numerical case studies are investigated in detail using an IEEE 118-bus system.

Efficient and reliable scheduling of maintenance for power generation and transmission infrastructure is essential for minimizing operational costs and ensuring grid stability. This paper introduces an integrated optimization framework for coordinated maintenance scheduling of generators and transmission lines under resource and reliability constraints. The model minimizes a composite cost function including maintenance and generation costs, as well as penalties for delayed maintenance, while satisfying N−1 security constraints, operational limits, and crew availability. Case studies on the IEEE 300-bus test system demonstrate the effectiveness of the proposed approach in producing feasible and cost-effective maintenance schedules. To address scalability and combinatorial complexity, the model is mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling exploration of solution approaches based on Quantum Imaginary Time Evolution (QITE). While the QUBO reformulation provides a foundation for future quantum-inspired optimization, this study focuses primarily on the development and demonstration of the classical optimization framework and illustrates the potential applicability of QITE in large-scale maintenance scheduling.

In this paper, we evaluate the performances of state-of-the-art higher order masking schemes for the AES. Doing so, we pay a particular attention to the comparison between specialized solutions introduced exclusively as countermeasures against side-channel analysis, and a recent proposal by Roche and Prouff exploiting multiparty computation (MPC) techniques. We show that the additional security features this latter scheme provides (e.g., its glitch-freeness) come at the cost of large performance overheads. We then study how exploiting standard optimization techniques from the MPC literature can be used to reduce this gap. In particular, we show that “packed secret sharing” based on a modified multiplication algorithm can speed up MPC-based masking when the order of the masking scheme increases. Eventually, we discuss the randomness requirements of masked implementations. For this purpose, we first show with information theoretic arguments that the security guarantees of masking are only preserved if this randomness is uniform, and analyze the consequences of a deviation from this requirement. We then conclude the paper by including the cost of randomness generation in our performance evaluations. These results should help actual designers to choose a masking scheme based on security and performance constraints.

SummaryCloud computing is a technology that enables elastic, on‐demand resource provisioning, allowing application developers to build highly scalable systems. Multi‐tenancy, the hosting of multiple customers by a single application instance, leads to improved efficiency, improved scalability, and less costs. While these technologies make it possible to create many new applications, legacy applications can also benefit from the added flexibility and cost savings of cloud computing and multi‐tenancy. In this article, we describe the steps required to migrate existing applications to a public cloud environment, and the steps required to add multi‐tenancy to these applications. We present a generic approach and verify this approach by means of two case studies, a commercial medical communications software package mainly used within hospitals for nurse call systems and a schedule planner for managing medical appointments. Both case studies are subject to stringent security and performance constraints, which need to be taken into account during the migration. In our evaluation, we estimate the required investment costs and compare them to the long‐term benefits of the migration. Copyright © 2015 John Wiley & Sons Ltd.

Neuromorphic architectures are widely used in many applications for advanced data processing and often implement proprietary algorithms. However, in an adversarial scenario, such systems may face elaborate security attacks including learning attack. In this article, we prevent an attacker with physical access from learning the proprietary algorithm implemented by the neuromorphic hardware. For this purpose, we leverage the obsolescence effect in memristors to judiciously reduce the accuracy of outputs for any unauthorized user. For a legitimate user, we regulate the obsolescence effect, thereby maintaining the accuracy of outputs in a suitable range. We extensively examine the feasibility of our proposed method with four datasets. We experiment under different settings, such as activation functions and constraints such as process variations, and estimate the calibration overhead. The security versus cost and performance versus resistance range tradeoffs for different applications are also analyzed. We then prove that the defense is still valid even if the attacker has the prior knowledge of the defense mechanism. Overall, our methodology is compatible with mainstream classification applications, memristor devices, and security and performance constraints.

A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN