Scalable Control Research Articles

Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT

The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.

High-fidelity gate set for exchange-coupled singlet-triplet qubits

In order to enable semiconductor-based quantum computing with many qubits, issues like residual interqubit coupling and constraints from scalable control hardware need to be tackled to retain the high gate fidelities demonstrated in current single-qubit devices. Here, we focus on two exchange-coupled singlet-triplet spin qubits, considering realistic control hardware as well as Coulomb and exchange coupling that cannot be fully turned off. Using measured noise spectra, we optimize realistic control pulses and show that two-qubit (single-qubit) gate fidelities of 99.90\% ($\ge 99.69\%$) can be reached in GaAs, while 99.99\% ($\ge 99.95\%$) can be achieved in Si.

Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits

Microring resonators (MRRs) are reconfigurable optical elements ubiquitous in photonic integrated circuits. Owing to its high sensitivity, MRR control is very challenging, especially in large-scale optical systems. In this work, we experimentally demonstrate continuous, multi-channel control of MRR weight banks using simple calibration procedures. A record-high accuracy and precision are achieved for all the controlled MRRs with negligible inter-channel crosstalk. Our approach allows accurate transmission calibration without the need for direct access to the output of the microring weight bank and without the need to lay out electrical and optical I/Os specific for calibration purpose. These features mean that our MRR control approach can be applied to large-scale photonic integrated circuits while maintaining its accuracy with manageable cost of chip area and I/O complexity.

Diagnosis and Management of First Case of COVID-19 in Canada: Lessons Applied From SARS-CoV-1.

We report diagnosis and management of the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) hospitalized in Toronto, Canada. No healthcare-associated transmission occurred. In the face of a potential pandemic of COVID-19, we suggest sustainable and scalable control measures developed based on lessons learned from SARS.

Optimistic Transaction Processing in Deterministic Database

Deterministic databases can improve the performance of distributed workload by eliminating the distributed commit protocol and reducing the contention cost. Unfortunately, the current deterministic scheme does not consider the performance scalability within a single machine. In this paper, we describe a scalable deterministic concurrency control, Deterministic and Optimistic Concurrency Control (DOCC), which is able to scale the performance both within a single node and across multiple nodes. The performance improvement comes from enforcing the determinism lazily and avoiding read-only transaction blocking the execution. The evaluation shows that DOCC achieves 8x performance improvement than the popular deterministic database system, Calvin.

Smart Infrastructure for Future Urban Mobility

Real‐time traffic signal control presents a challenging multiagent planning problem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real‐time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal‐timing plans that optimize the movement of currently sensed approaching traffic through the intersection. Each time a new plan is produced (nominally every couple of seconds), the intersection communicates to its downstream neighbors what traffic it expects to send their way, allowing intersections to construct longer horizon plans and achieve coordinated behavior. Initial evaluation of Surtrac in the field has demonstrated significant performance improvements, and the technology is now deployed and operating in several U.S. cities. More recent work has focused on integrating real‐time adaptive signal control with emerging connected vehicle technology, and exploration of the opportunities for enhanced mobility that direct vehicle (or pedestrian) to infrastructure communication can provide. Current technology development efforts center on vehicle route sharing, smart transit priority, safe intersection crossing for pedestrians with disabilities, real‐time incident detection, and integrated optimization of signal control and route choice decisions. This article provides an overview of this overall research effort.

Augmenting Scalable Communication-Based Role Allocation for a Three-Role Task

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals that represent roles. This idea was successfully applied to evolve communication-based role allocation for a two-role task. However, it was necessary to reward signal differentiation in the fitness function, which is a serious limitation as it does not generalize to tasks where the number of roles is unknown a priori. In this paper, we show that rewarding signal differentiation is not necessary to evolve communication-based role allocation strategies for the given task, and we improve reported scalability, while requiring less a priori knowledge. Our approach for the two-role task puts fewer constrains on the evolutionary process and enhances the potential of evolving communication-based role allocation for more complex tasks. Furthermore, we conduct experiments for a three-role task where we compare two different cognitive architectures and several fitness functions and we show how scalable controllers might be evolved.

Poly-stable matching based scalable controller placement with balancing constraints in SDN

Software Defined Networking achieves programmability of the control plane by separating it from the data plane. In this paper, we propose a strategy for controller placement to minimize the maximum load imbalance between controllers while enforcing the placement and balancing constraints. We also propose a scalable algorithm to compute near optimal solutions of the problem on large scale networks. The algorithm uses poly-stable matching to distribute a fraction of switches equally among controllers so as to reduce the load imbalance. The remaining switches are assigned to their nearest controllers while considering latency of switches and load of controllers. Further, the algorithm relocates some of the switches from the controller to which they are currently assigned so as to reduce the latency between switches and controllers. The proposed algorithm is evaluated on widely used Chinanet and Interoute networks extracted from internet topology zoo. The results show that the proposed algorithm outperforms the existing controller placement solutions for software defined wide area networks in terms of load imbalance and inter controller latency without affecting the average and standard deviation in switch to controller latency.

A real‐time attack defense framework for 5G network slicing

SummaryNetwork Slicing (NS) is a key enabler to support 5G network services on‐demand. However, since NS is a result of the recent advancement in Software‐Defined Networking and Network Function Virtualization, it introduces new security issues which include attacks against an NS instance within an operator network and interslice security threats. In this scenario, identifying and mitigating attacks in real‐time is of paramount importance to improve security aspects. However, it is far from being straightforward. Therefore, this work proposes the FrameRTP4, a P4‐based framework that aims to deliver real‐time attack detection and mitigation mechanisms in 5G NS scenarios. For this, it provides a P4‐based switch that implements an Service Function Chaining protocol layer, an efficient and scalable Access Control List for the detection and mitigation of known attacks, and a monitoring system aiming to reduce the overhead induced on the control channel. Furthermore, it delivers an orchestrator that aims to control all switches in order to enable lifecycle management of NS instances and P4 table rules. Besides, it also performs some autonomous tasks such as the wildcard rules generation and the detection of new threats by using machine learning algorithms. Preliminary results point to the potential benefits of FrameRTP4 to be part of a 5G NS infrastructure.

Server‐aided revocable attribute‐based encryption for cloud computing services

SummaryAttribute‐based encryption (ABE) has been regarded as a promising solution in cloud computing services to enable scalable access control without compromising the security. Despite of the advantages, efficient user revocation has been a challenge in ABE. One suggestion for user revocation is using the binary tree in the key generation phase of an ABE scheme, which enables a trusted key generation center to periodically distribute the key update information to all nonrevoked users over a public channel. This revocation approach reduces the size of key updates from linear to logarithmic in the number of users. But it requires each user to keep a private key of the logarithmic size, and asks each nonrevoked user to periodically update his/her decryption key for each new time period. To further optimize user revocation in ABE, a server‐aided revocable ABE (SR‐ABE) scheme has been proposed, in which almost all workloads of users incurred by the user revocation are outsourced to an untrusted server, and each user only needs to store a private key of the constant size. In addition, SR‐ABE does not require any secure channel for the key transmission, and a user only needs to perform a small amount of calculations to decrypt a ciphertext. In this paper, we revisit the notion of SR‐ABE, and present a generic construction of SR‐ABE, which can transform a revocable ABE (RABE) scheme to an SR‐ABE scheme. In addition, we give an instantiation of SR‐ABE by applying the generic construction on a concrete RABE scheme, and implement an instantiation of SR‐ABE and an RABE scheme to evaluate the performance of SR‐ABE.

Scalable distributed optimal control of vibrating modular structures

A scalable optimal control method for structural vibration mitigation is studied. The method relies on a structure's partitioning that leads to a set of dynamically interconnected subsystems. Each subsystem is operated with an individual subcontroller that collects the local state information and collaborates with the neighboring subcontrollers to estimate a short time prediction of the interconnecting forces defining the subsystem's boundary conditions. Using the extended model that represents the subsystem's dynamics together with the evolution of its boundary conditions, each subcontroller computes the control decision based on the solution to a finite-time horizon optimal control problem. In order to cope with the changes in the boundary conditions, the optimal solution is computed repetitively according to the receding horizon scheme. The method is validated numerically for a cantilever structure equipped with actively controlled electromagnetic actuators and subjected to a variety of initial condition scenarios. The performance of the designed controller is tested by comparisons to the centralized and isolated decentralized controllers. The introduced system partitioning and distributed controller allow performing parallel computing which makes the method fully scalable and applicable to large-scale structures. The computational complexity of the designed distributed control is studied for different settings in the modeling of the subsystem's boundary conditions.

A review on control systems hardware and software for robots of various scale and purpose. Part 2. Service robotics

A review of robotic systems was carried out. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, the achievement of fault tolerance, and the reduction of the weight and size of control system elements belonging to various classes of robotic systems were carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to service robotics. The following conclusions are made on the basis of the review results: the key technology in service robotics from the point of view of scalability is the Robot Operating System (ROS); service robotics is today the main springboard for testing intelligent algorithms for the tactical and strategic control levels that are integrated into a common system based on ROS; the problem of ensuring fault tolerance in the service robotics is practically neglected, with the exception of the issue of increasing reliability by changing behavioral algorithms; in a number of areas of service robotics, in which the reduction of mass and dimensions is especially important, the robot control systems are implemented on a single computing device, in other cases a multi-level architecture implemented on Linux-based embedded computers with ROS are used.

Key regeneration-free ciphertext-policy attribute-based encryption and its application

Attribute-based encryption (ABE) provides a promising solution for enabling scalable access control over encrypted data stored in the untrusted servers (e.g., cloud) due to its ability to perform data encryption and decryption defined over descriptive attributes. In order to bind different components which correspond to different attributes in a user’s attribute-based decryption key together, key randomization technique has been applied in most existing ABE schemes. This randomization method, however, also empowers a user the capability of regenerating a newly randomized decryption key over a subset of the attributes associated with the original decryption key. Because key randomization breaks the linkage between this newly generated key and the original key, a malicious user could leak the new decryption key to others without taking any responsibility for the key abuse. To solve this problem, we think of key regeneration-free ABE to disallow a user from randomizing his/her decryption key in any manner, i.e., a user can only delegate his/her decryption key in exactly the same form without any modification so that any abused or pirated key can be traced back to its original owner. Motivated by strongly unforgeable signature, we first define a security notion called strong key unforgeability, and show that ABE schemes equipped with the strong key unforgeability are immune to key regeneration. We then provide a generic transformation to convert ciphertext-policy ABE (CP-ABE) schemes of certain type to key regeneration-free CP-ABE schemes, and show how the transformation works by presenting two concrete constructions.

A Scalable Attribute-Based Access Control Scheme with Flexible Delegation cum Sharing of Access Privileges for Cloud Storage

Nowadays cloud servers have become the primary choice to store and share data with multiple users across the globe. The major challenge in sharing data using cloud servers is to protect data against untrusted cloud service provider and illegitimate users. Attribute-Based Encryption (ABE) has emerged as a useful cryptographic technique to securely share data with legitimate recipients in fine-grained manner. Several solutions employing ABE have been proposed to securely share data using cloud servers. However, most of the solutions are data owner-centric and focus on providing data owner complete control on his outsourced data. The existing solutions in cloud computing fail to provide shared access privileges among users and to enable cloud users to delegate their access privileges in a flexible manner. In order to simultaneously achieve the notion of fine-grained access control, scalability and to provide cloud users shared access privileges and flexibility on delegation of their access privileges, we propose a scalable attribute-based access control scheme for cloud storage. The scheme extends the ciphertext policy attribute-based encryption to achieve flexible delegation of access privileges and shared access privileges along with scalability and fine-grained access control. The scheme achieves scalability by employing hierarchical structure of users. Furthermore, we formally prove the security of our proposed scheme based on security of the ciphertext-policy attribute-based encryption. We also implement the algorithm to show its scalability and efficiency.

Cloud-based model predictive control with variable horizon

A novel method using the cloud to implement a variable horizon model predictive controller is presented. In case of sudden long delays and downtime, a graceful degradation is used. Robust, best effort strategies allow industrial grade use of the powerful, efficient, and quickly improving cloud ecosystems. The variable horizon strategy finds use in, for example, non-linear control problems, and the proposed method can be generalized to implement robust and scalable controllers that benefit from cloud technology. We show results from two horizon selection strategies, service degradation and connectivity issues.

Design of Scalable Controllers for Power Systems

This paper studies scalable control of power systems, i.e., control with the constraint that controllers of all generators are the same. This control framework is useful to reduce the cost of constructing large-scale power systems because we can obtain controllers of all generators merely by designing one controller. The problem addressed here is to find the same controllers stabilizing an equilibrium point of the resulting feedback system and improving the performance in terms of the time response. As a solution to this problem, we present controllers to uniformly increase the damping forces of generators. We then show that an equilibrium point of the resulting feedback system is stable under certain conditions. In addition, we present a design method of the controller gain for improving the performance of the resulting feedback system in terms of the time response.

Anonymous Electronic Health Record Sharing Scheme Based on Decentralized Hierarchical Attribute-Based Encryption in Cloud Environment

The rapid development of communication technologies, the network, advanced computing methods and wireless medical sensors gives rise to a modern medical system. In this system, large-scale electronic health records (EHRs) are often outsourced to be stored at the third parties, such as cloud service providers (CSPs). However, CSPs are not trustworthy, that is, serious security and privacy concerns about cloud service exist because it may expose the user's sensitive data to CSPs or unauthorized users in transmission, storage and sharing. To prevent the privacy disclosure of patients better and realize information sharing more effectively, this paper proposes an anonymous EHRs sharing scheme based on decentralized hierarchical attribute-based encryption (ABE). In the proposed scheme, (1) Multiple attribute authority (AA) ABE is leveraged to achieve fine-grained and scalable data access control and avoid bottleneck. Meanwhile, hierarchical access tree is used to encrypt multiple files in one operation, thereby saving calculation and storage load greatly. Moreover, the hidden access policy enhances user privacy protection. (2) The global identifier (GID) of a user is introduced to resist the collusion attack of users. Subsequently, an anonymous key generation mechanism is equipped to prevent multiple AAs from building a full profile using the user's GID. (3) To ensure the correctness and integrity of EHRs, users can conduct double verification based on the verification tag and convergent key. Finally, the efficiency analysis and experiments show that the scheme meets the security requirements of key management and privacy preservation in cloud and is proven secure and efficient in practice under the decisional bilinear Diffie-Hellman (DBDH) assumption.

Decentralized Multivariable Vector Current Control of Grid-connected Voltage Source Inverters

Increasing the number of grid-connected inverters in power systems imposes several challenges. One of the main challenges is the complexity and uncertainty of the dynamical model of the inverters due to the large numbers of grid-connected inverters and disconnection of inverters. To address this challenge, we present a scalable direct-quadrature current control strategy for parallel voltage source inverters in a rotating reference frame. The control structure is based on a decentralized multivariable proportional integral (PI) current control mechanism and provides stability and zero steady-state errors. The proposed control approach has the main advantages of flexibility, allowing disconnection/connection of inverters on the basis of the required power level. The effectiveness of the proposed vector current control strategy is evaluated through simulation case studies in MATLAB/Simscape Electrical.

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerable amount of load flexibility in the demand side. Demand response (DR) programs can reveal and utilize this demand flexibility by enabling the participation of a large number of grid-interactive efficient buildings (GEB). Existing approaches on DR require significant modelling or training efforts, are computationally expensive, and do not guarantee the satisfaction of end users. To address these limitations, this paper proposes a scalable hierarchical model-free transactional control approach that incorporates elements of virtual battery, game theory, and model-free control (MFC) mechanisms. The proposed approach separates the control mechanism into upper and lower levels. The MFC modulates the flexible GEB in the lower level with guaranteed thermal comfort of end users, in response to the optimal pricing and power signals determined in the upper level using a Stackelberg game integrated with aggregate virtual battery constraints. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable even on small embedded devices. The effectiveness of this approach is demonstrated through a large-scale case study with 10,000 heterogenous GEB. The results show that the proposed approach can achieve peak load reduction and profit maximization for the distribution system operator, as well as cost reduction for end users while maintaining their comfort.

High-Fidelity Control of Superconducting Qubits Using Direct Microwave Synthesis in Higher Nyquist Zones

Control electronics for superconducting quantum processors have strict requirements for accurate command of the sensitive quantum states of their qubits. Hinging on the purity of ultra-phase-stable oscillators to upconvert very-low-noise baseband pulses, conventional control systems can become prohibitively complex and expensive when scaling to larger quantum devices, especially as high sampling rates become desirable for fine-grained pulse shaping. Few-GHz radio-frequency digital-to-analog converters (RF DACs) present a more economical avenue for high-fidelity control while simultaneously providing greater command over the spectrum of the synthesized signal. Modern RF DACs with extra-wide bandwidths are able to directly synthesize tones above their sampling rates, thereby keeping the system clock rate at a level compatible with modern digital logic systems while still being able to generate high-frequency pulses with arbitrary profiles. We have incorporated custom superconducting qubit control logic into off-the-shelf hardware capable of low-noise pulse synthesis up to 7.5 GHz using an RF DAC clocked at 5 GHz. Our approach enables highly linear and stable microwave synthesis over a wide bandwidth, giving rise to high-resolution control and a reduced number of required signal sources per qubit. We characterize the performance of the hardware using a five-transmon superconducting device and demonstrate consistently reduced two-qubit gate error (as low as 1.8%) which we show results from superior control chain linearity compared to traditional configurations. The exceptional flexibility and stability further establish a foundation for scalable quantum control beyond intermediate-scale devices.