An official website of the United States government
Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions. Constructing accurate models of such interactions–in the meantime–can become prohibitive in applications. Data-driven control methods avoid such complications by directly synthesizing a controller from the observed data. In this paper, we propose an algorithm referred to as Data-driven Structured Policy Iteration (D2SPI), for synthesizing an efficient feedback mechanism that respects the sparsity pattern induced by the underlying interaction network. In particular, our algorithm uses temporary “auxiliary” communication links in order to enable the required information exchange on a (smaller) sub-network during the “learning phase”—links that will be removed subsequently for the final distributed feedback synthesis. We then proceed to show that the learned policy results in a stabilizing structured policy for the entire network. Our analysis is then followed by showing the stability and convergence of the proposed distributed policies throughout the learning phase, exploiting a construct referred to as the “Patterned monoid.” The performance of D2SPI is then demonstrated using representative simulation scenarios.
more »
« less
Design and Implementation of a Strong Representation System for Network Policies
Policy information in computer networking today, such as reachability objectives of a controller program running on a Software Defined Network (henceforth referred to as SDN) or Border Gateway Protocol (henceforth referred to as BGP) configurations independently set by autonomous networks, are hard to manage. This is in sharp contrast to the relational data structured in a database that allows easy access. This paper asks why cannot (or how can) we turn network policies into relational data. One difficulty to such an approach is that a policy does not always translate to a \textit{definite} network snapshot, but rather is fully described only when we include all the possible network states it admits. We propose relational policies that, while capable of representing and manipulating sets of network states in exactly the same way as a single one, form a strong representation system and accurately capture the information in a policy with the usual Structured Query Language (henceforth referred to as SQL) interface. We demonstrate how, like relational database improves application productivity and enables rapid innovation, relational policies allow us to extend the elegant solutions that the database community developed, to mediate multiple data sources in order to address long-standing challenges and new opportunities for autonomous policy making in the distributed networking environment. We also show the feasibility of relational policies by evaluation on synthetic policies and realistic network topologies.
more »
« less
- Award ID(s):
- 1909450
- PAR ID:
- 10358963
- Date Published:
- Journal Name:
- The 31st International Conference on Computer Communications and Networks (ICCCN 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Data privacy policy requirements are a quickly evolving part of the data management domain. Healthcare (e.g., HIPAA), financial (e.g., GLBA), and general laws such as GDPR or CCPA impose controls on how personal data should be managed. Relational databases do not offer built-in features to support data management features to comply with such laws. As a result, many organizations implement ad-hoc solutions or use third party tools to ensure compliance with privacy policies. However, external compliance framework can conflict with the internal activity in a database (e.g., trigger side-effects or aborted transactions). In our prior work, we introduced a framework that integrates data retention and data purging compliance into the database itself, requiring only the support for triggers and encryption, which are already available in any mainstream database engine. In this demonstration paper, we introduce DBCompliant – a tool that demonstrates how our approach can seamlessly integrate comprehensive policy compliance (defined via SQL queries). Although we use PostgreSQL as our back-end, DBCompliant could be adapted to any other relational database. Finally, our approach imposes low (less than 5%) user query overhead.more » « less
-
Most organizations rely on relational database(s) for their day-to-day business functions. Data management policies fall under the umbrella of IT Operations, dictated by a combination of internal organizational policies and government regulations. Many privacy laws (such as Europe’s General Data Protection Regulation and California’s Consumer Privacy Act) establish policy requirements for organizations, requiring the preservation or purging of certain customer data across their systems. Organization disaster recovery policies also mandate backup policies to prevent data loss. Thus, the data in these databases are subject to a range of policies, including data retention and data purging rules, which may come into conflict with the need for regular backups. In this paper, we discuss the trade-offs between different compliance mechanisms to maintain IT Operational policies. We consider the practical availability of data in an active relational database and in a backup, including: 1) supporting data privacy rules with respect to preserving or purging customer data, and 2) the application performance impact caused by the database policy implementation. We first discuss the state of data privacy compliance in database systems. We then look at enforcement of common IT operational policies with regard to database backups. We consider different implementations used to enforce privacy rule compliance combined with a detailed discussion for how these approaches impact the performance of a database at different phases. We demonstrate that naive compliance implementations will incur a prohibitively high cost and impose onerous restrictions on backup and restore process, but will not affect daily user query transaction cost. However, we also show that other solutions can achieve a far lower backup and restore costs at a price of a small (<5%) overhead to non-SELECT queries.more » « less
-
Passively collected big data sources are increasingly used to inform critical development policy decisions in low- and middle-income countries. While prior work highlights how such approaches may reveal sensitive information, enable surveillance, and centralize power, less is known about the corresponding privacy concerns, hopes, and fears of the people directly impacted by these policies --- people sometimes referred to asexperiential experts.To understand the perspectives of experiential experts, we conducted semi-structured interviews with people living in rural villages in Togo shortly after an entirely digital cash transfer program was launched that used machine learning and mobile phone metadata to determine program eligibility. This paper documents participants' privacy concerns surrounding the introduction of big data approaches in development policy. We find that the privacy concerns of our experiential experts differ from those raised by privacy and developmentdomain experts.To facilitate a more robust and constructive account of privacy, we discuss implications for policies and designs that take seriously the privacy concerns raised by both experiential experts and domain experts.more » « less
-
null (Ed.)This paper will demonstrate a novel method for consolidating data in an engineered hypercube network for the purpose of optimizing query processing. Query processing typically calls for merging data collected from a small subset of server nodes in a network. This poses the problem of managing efficiently the exchange of data between processing nodes to complete some relational data operation. The method developed here is designed to minimize data transfer, measured as the product of data quantity and network distance, by delegating the processing to a node that is relatively central to the subset. A hypercube not only supports simple computation of network distance between nodes, but also allows for identifying a node to serve as the center for any data consolidation operations.We will show how the consolidation process can be performed by selecting a subgraph of a complex network to simplify the selection of a central node and thus facilitate the computations required. We will also show a prototype implementation of a hypercube using Software-Defined Networking to support query optimization in a distributed heterogeneous database system, making use of network distance information and data quantity.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
