Notes on the Effect of Different Access Patterns on the Intensity of Mistreatment in Distributed Caching Groups

In this report, we extend our study of the intensity of mistreatment in distributed caching groups due to state interaction. In our earlier work (published as BUCS-TR-2006-003), we analytically showed how this type of mistreatment may appear under homogeneous demand distributions. We provided a simple setting where mistreatment due to state interaction may occur. According to this setting, one or more "overactive" nodes generate disproportionately more requests than the other nodes. In this report, we extend our experimental evaluation of the intensity of mistreatment to which non-overactive nodes are subjected, when the demand distributions are not homogeneous

The State of Network Neutrality Regulation

The Network Neutrality (NN) debate refers to the battle over the design of a regulatory framework for preserving the Internet as a public network and open innovation platform. Fueled by concerns that broadband access service providers might abuse network management to discriminate against third party providers (e.g., content or application providers), policymakers have struggled with designing rules that would protect the Internet from unreasonable network management practices. In this article, we provide an overview of the history of the debate in the U.S. and the EU and highlight the challenges that will confront network engineers designing and operating networks as the debate continues to evolve.BMBF, 16DII111, Verbundprojekt: Weizenbaum-Institut für die vernetzte Gesellschaft - Das Deutsche Internet-Institut; Teilvorhaben: Wissenschaftszentrum Berlin für Sozialforschung (WZB)EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNe

Implications of Selfish Neighbor Selection in Overlay Networks

In a typical overlay network for routing or content sharing, each node must select a fixed number of immediate overlay neighbors for routing traffic or content queries. A selfish node entering such a network would select neighbors so as to minimize the weighted sum of expected access costs to all its destinations. Previous work on selfish neighbor selection has built intuition with simple models where edges are undirected, access costs are modeled by hop-counts, and nodes have potentially unbounded degrees. However, in practice, important constraints not captured by these models lead to richer games with substantively and fundamentally different outcomes. Our work models neighbor selection as a game involving directed links, constraints on the number of allowed neighbors, and costs reflecting both network latency and node preference. We express a node's "best response" wiring strategy as a k-median problem on asymmetric distance, and use this formulation to obtain pure Nash equilibria. We experimentally examine the properties of such stable wirings on synthetic topologies, as well as on real topologies and maps constructed from PlanetLab and AS-level Internet measurements. Our results indicate that selfish nodes can reap substantial performance benefits when connecting to overlay networks composed of non-selfish nodes. On the other hand, in overlays that are dominated by selfish nodes, the resulting stable wirings are optimized to such great extent that even non-selfish newcomers can extract near-optimal performance through naive wiring strategies.Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230); National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 020206

Distributed Selfish Coaching

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

Distributed Mega-Datasets: The Need for Novel Computing Primitives

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.With the ongoing digitalization, an increasing number of sensors is becoming part of our digital infrastructure. These sensors produce highly, even globally, distributed data streams. The aggregate data rate of these streams far exceeds local storage and computing capabilities. Yet, for radical new services (e.g., predictive maintenance and autonomous driving), which depend on various control loops, this data needs to be analyzed in a timely fashion. In this position paper, we outline a system architecture that can effectively handle distributed mega-datasets using data aggregation. Hereby, we point out two research challenges: The need for (1) novel computing primitives that allow us to aggregate data at scale across multiple hierarchies (i.e., time and location) while answering a multitude of a priori unknown queries, and (2) transfer optimizations that enable rapid local and global decision making.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNe

Distributed Storage Control Algorithms for Dynamic Networks

Recent technological advances have rendered storage a readily available resource, yet there exist few examples that use it for enhancing network performance. We revisit in-network storage and we evaluate its usage as an additional degree of freedom in network optimization. We consider the network design problem of maximizing the volume of end-to-end transferred data and we derive storage allocation (placement) solutions. We show that different storage placements have different impact on the performance of the network and we introduce a systematic methodology for the derivation of the optimal one. Accordingly, we provide a framework for the joint optimization of routing and storage control (usage) in dynamic networks for the case of a single commodity transfer. The derived policies are based on time-expanded graphs and ensure maximum performance improvement with minimum possible storage usage. We also study the respective multiple commodity problem, where the network link capacities and node storage resources are shared by the different commodities. A key advantage of our methodology is that it employs algorithms that are applicable to both centralized as well as to distributed execution in an asynchronous fashion, and thus, no tight synchronization is required among the various involved storage and routing devices in an operational network. We also present an extensive performance evaluation study using the backbone topology and actual traffic traces from a large European Internet Service Provider, and a number of synthetic network topologies. Our results show that indeed our approach offers significant improvements in terms of delivery time and transferred traffic volume.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNetEC/FP7/628441/EU/Improving Performance and Cost of Content Delivery in a Hyperconnected World/CDN-

On the importance of Internet eXchange Points for today's Internet ecosystem

Internet eXchange Points (IXPs) are generally considered to be the successors of the four Network Access Points that were mandated as part of the decommissioning of the NSFNET in 1994/95 to facilitate the transition from the NSFNET to the "public Internet" as we know it today. While this popular view does not tell the whole story behind the early beginnings of IXPs, what is true is that since around 1994, the number of operational IXPs worldwide has grown to more than 300 (as of May 2013), with the largest IXPs handling daily traffic volumes comparable to those carried by the largest Tier-1 ISPs, but IXPs have never really attracted any attention from the networking research community. At first glance, this lack of interest seems understandable as IXPs have apparently little to do with current "hot" topic areas such as data centers and cloud services or software defined networking (SDN) and mobile communication. However, we argue in this article that, in fact, IXPs are all about data centers and cloud services and even SDN and mobile communication and should be of great interest to networking researchers interested in understanding the current and future Internet ecosystem. To this end, we survey the existing but largely unknown sources of publicly available information about IXPs to describe their basic technical and operational aspects and highlight the critical differences among the various IXPs in the different regions of the world, especially in Europe and North America. More importantly, we illustrate the important role that IXPs play in today's Internet ecosystem and discuss how IXP-driven innovation in Europe is shaping and redefining the Internet marketplace, not only in Europe but increasingly so around the world.Comment: 10 pages, keywords: Internet Exchange Point, Internet Architecture, Peering, Content Deliver

Online Replication Strategies for Distributed Data Stores

The rate at which data is produced at the network edge, e.g., collected from sensors and Internet of Things (IoT) devices, will soon exceed the storage and processing capabilities of a single system and the capacity of the network. Thus, data will need to be collected and preprocessed in distributed data stores - as part of a distributed database - at the network edge. Yet, even in this setup, the transfer of query results will incur prohibitive costs. To further reduce the data transfers, patterns in the workloads must be exploited. Particularly in IoT scenarios, we expect data access to be highly skewed. Most data will be store-only, while a fraction will be popular. Here, the replication of popular, raw data, as opposed to the shipment of partially redundant query results, can reduce the volume of data transfers over the network. In this paper, we design online strategies to decide between replicating data from data stores or forwarding the queries and retrieving their results. Our insight is that by profiling access patterns of the data we can lower the data transfer cost and the corresponding response times. We evaluate the benefit of our strategies using two real-world datasets