Rate enforcement is routinely employed in modern networks (e.g. ISPs rate-limiting users traffic to the subscribed rates). In addition to correctly enforcing the desired rates, rate-limiting mechanisms must be able to support rich rate-sharing policies within each traffic aggregate (e.g. per-flow fairness, weighted fairness, and prioritization). And all of this must be done at scale to efficiently support the vast magnitude of users. There are two primary rate-limiting mechanisms -- traffic shaping (that buffers packets in queues to enforce the desired rates and policies) and traffic policing (that filters packets as per the desired rates without buffering them). Policers are light-weight and scalable, but do not support rich policy enforcement and often provide poor rate enforcement (being notoriously hard to configure). Shapers, on the other hand, achieve desired rates and policies, but at the cost of high system resource (memory and CPU) utilization which impacts scalability. In this paper, we explore whether we can get the best of both worlds -- the scalability of a policer with the rate and policy enforcement properties of a shaper. We answer this question in the affirmative with our system BC-PQP. BC-PQP augments a policer with (i) multiple phantom queues that simulate buffer occupancy using counters, and enable rich policy enforcement, and (ii) a novel burst control mechanism that enables auto-configuration of the queues for correct rate enforcement. We implement BC-PQP as a middlebox over DPDK. Our evaluation shows how it achieves the rate and policy enforcement properties close to that of a shaper with 7x higher efficiency.
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Policy Creation for Enterprise-Level Data Sharing
Enterprises, including military, law enforcement, medical, financial, and commercial organizations, must often share large quantities of data, some potentially sensitive, with many other enterprises. A key issue, the mechanics of data sharing, involves how to precisely and unambiguously specify which data to share with which partner or group of partners. This issue can be addressed through a system of formal data sharing policy definitions and automated enforcement. Several challenges arise when specifying enterprise-level data sharing policies. A first challenge involves the scale and complexity of data types to be shared. An easily understood method is required to represent and visualize an enterprise’s data types and their relationships so that users can quickly, easily, and precisely specify which data types and relationships to share. A second challenge involves the scale and complexity of data sharing partners. Enterprises typically have many partners involved in different projects, and there are often complex hierarchies among groups of partners that must be considered and navigated to specify which partners or groups of partners to include in a data sharing policy. A third challenge is that defining policies formally, given the first two challenges of scale and complexity, requires complex, precise language, but these languages are difficult to use by non-specialists. More useable methods of policy specification are needed. Our approach was to develop a software wizard that walks users through a series of steps for defining a data sharing policy. A combination of innovative and well known methods is used to address these challenges of scale, complexity, and usability.
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- Award ID(s):
- 1662487
- NSF-PAR ID:
- 10127228
- Date Published:
- Journal Name:
- Lecture Notes in Computer Science
- Volume:
- 11594
- Issue:
- 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-22351-9_17
