We expand the scope of cache memory to include LEO constellations, which are highly distributed systems with thousands of satellites connected with free-space optics inter-satellite links (ISL) with numerous nodes only one hop from any point on earth. We show how to increase the number of cache hits and improve the speed of inference for the important use case of LLMs. These benefits apply not only to LLMs, both terrestrially hosted and on satellites, but also generalize to any cache distributed over multiple locations that needs to be accessed in a timely manner. We show the benefit of our key value cache (KVC) protocol in simulations and present a proof-of-concept implementation of the protocol for KVCs on a testbed comprising 5 Intel NUC Linux mini PCs hosting a 95 node, 19x5 constellation, with an NVIDIA Jetson Nano 8GB GPU hosting the LLM.

To explore service-level appropriate network configurations efficiently, we propose a processing pipeline that generates a digital twin given only a packet capture trace from the network. Our pipeline outputs an instance of a discrete-event simulator, which can then be easily customized to explore arbitrary what-if configuration scenarios for the network. The parameters of this simulator instance are optimized to fit measured KPIs in the real-life network, using a genetic algorithm operating on a mix of actual fitness from the simulator and surrogate fitness implemented by neural networks. We show that our surrogate model can both speed up the time of finding a good fit, by 17%, as well as improve the fit, by 1.8x, compared to random parameter selection. We also evaluate our approach in an experimental testbed using what-if predictions of Wi-Fi primary channel configurations for several application workloads, showing 153% improvement in collision probability and 110% improvement in throughput predictions over a random baseline. Furthermore, we show that we only have to train our neural networks on a subset of the network workloads as it generalizes well.

We propose a new way to share licensed spectrum bandwidth capacity in mobile networks between operators, service providers, and end users using blockchain-based smart contracts. We discuss the foundational building blocks in the contract as well as various extensions to support more advanced features such as bulk purchases and future reservations. Furthermore, we demonstrate how the system can be implemented with an open source, permissioned enterprise blockchain, Hyperledger Sawtooth. We show that our smart contract implementation can improve blockchain transaction performance, by approximately four orders of magnitude compared to serial transactions and one order of magnitude compared to parallel transactions, by using public key infrastructure driven bulk purchases of mobile access grants, paving the way for fully automated, efficient, and fine-grained roaming agreements. We conclude with a discussion of lessons learned from two end-to-end use cases we implemented to validate our distributed ledger design.

We present a Web survey system demo that computes aggregates of sensitive data while protecting individual contributions using a novel secure aggregation algorithm implemented in a Web browser.

Current location-based services (LBS) typically allow users to locate points of interest (POI) in their vicinity but can detract from the user's emotional experience of exploring a new location. In this paper, we examine how cues in the form of popular music (musicons) can emotionally engage users and enhance their experience of discovering nearby POIs serendipitously in unfamiliar places. The primary contribution of this paper is a field study, in which we evaluate the performance and emotional engagement of different types of audio-based cues for directing users' attention to specific POIs. Musicons and mixed-modality cues performed close to visual and speech cues, and significantly better than auditory icons, for POI identification while creating a much more pleasant and engaging user experience. We conclude that cues for POI discovery need not always be as explicit as the baseline visual cues. Indeed, the most challenging cues, auditory icons, led to a heightened sense of autonomy.

In this workshop, we explore novel applications, services, tools, and systems that take advantage of the audio channel on mobile devices to feed users with a flow of information. We call for innovative ideas to introduce ambient context-aware, location-aware, and/or social audio as a more effective means of communicating information and providing experiences to mobile users.

We present the design, implementation and evaluation of a new geotagging service, Gloe, that makes it easy to find, rate and recommend arbitrary on-line content in a mobile setting. The service automates the content search process by taking advantage of geographic and social context, while using crowdsourced expertise to present a personalized feed of targeted information ranked by a novel geo-aware rating and incentive mechanism.

Increasingly, large organizations are experimenting with internal social media (e.g., blogs, forums) as a platform for widespread distributed collaboration. Contributions to their counterparts outside the organization’s firewall are driven by attention from strangers, in addition to sharing among friends. However, employees in a workplace under time pressures may be reluctant to participate–and the audience for their contributions is comparatively smaller. Participation rates also vary widely from group to group. So what influences people to contribute in this environment? In this paper, we present the results of a year-long empirical study of internal social media participation at a large technology company, and analyze the impact attention, feedback, and managers’ and coworkers ’ participation have on employees ’ behavior. We find feedback in the form of posted comments is highly correlated with a user’s subsequent participation. Recent manager and coworker activity relate to users initiating or resuming participation in social media. These findings extend, to an aggregate level, the results from prior interviews about blogging at the company and offer design and policy implications for organizations seeking to encourage social media adoption.

We propose, implement and evaluate three admission models for computational Grids. The models take the expected demand into account and offer a specific performance guarantee. The main issue addressed is how users and providers should make the tradeoff between a best effort (low guarantee) spot market and an admission controlled (high guarantee) reservation market. Using a realistically modeled high performance computing workload and utility models of user preferences, we run experiments highlighting the conditions under which different markets and admission models are efficient. The experimental results show that providers can make large efficiency gains if the admission model is chosen dynamically based on the current load, likewise we show that users have an opportunity to optimize their job performance by carefully picking the right market based on the state of the system, and the characteristics of the application to be run. Finally, we provide simple functional expressions that can guide both users and providers when making decisions about guarantee levels to request or offer.

Abstract The SweGrid Accounting System (SGAS) allocates capacity in collaborative Grid environments by coordinating enforcement of Grid‐wide usage limits as a means to offer usage guarantees and prevent overuse. SGAS employs a credit‐based allocation model where Grid capacity is granted to projects via Grid‐wide quota allowances that can be spent across the Grid resources. The resources collectively enforce these allowances in a soft, real‐time manner. SGAS is built on service‐oriented principles with a strong focus on interoperability and Web services standards. This article covers the SGAS design and implementation, which, besides addressing inherent Grid challenges (scale, security, heterogeneity, decentralization), emphasizes generality and flexibility to produce a customizable system with lightweight integration into different middleware and scheduling system combinations. We focus the discussion around the system design, a flexible allocation model, middleware integration experiences and scalability improvements via a distributed virtual banking system, and finally, an extensive set of testbed experiments. The experiments evaluate the performance of SGAS in terms of response times, request throughput, overall system scalability, and its performance impact on the Globus Toolkit 4 job submission software. We conclude that, for all practical purposes, the quota enforcement overhead incurred by SGAS on job submissions is not a limiting factor for the job‐handling capacity of the job submission software. Copyright © 2008 John Wiley & Sons, Ltd.

We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers whopurchase services expect both price and performance guarantees. They need to predict future demand to budget for sustained performance despite price fluctuations. Conversely, providers need to estimate demand to price future usage. The skewed and bursty nature of demand in large-scale computer networks challenges the common statistical assumptions of symmetry, independence, and stationarity. This discrepancy leads to under estimation of investment risk. We confirm this non-normal distribution behavior in our study of demand in computational markets.

We present the implementation and analysis of a market-based resource allocation system for computational grids. Although grids provide a way to share resources and take advantage of statistical multiplexing, a variety of challenges remain. One is the economically efficient allocation of resources to users from disparate organizations who have their own and sometimes conflicting requirements for both the quantity and quality of services. Another is secure and scalable authorization despite rapidly changing allocations. Our solution to both of these challenges is to use a market-based resource allocation system. This system allows users to express diverse quantity- and quality-of-service requirements, yet prevents them from denying service to other users. It does this by providing tools to the user to predict and tradeoff risk and expected return in the computational market. In addition, the system enables secure and scalable authorization by using signed money-transfer tokens instead of identity-based authorization. This removes the overhead of maintaining and updating access control lists, while restricting usage based on the amount of money transferred. We examine the performance of the system by running a bioinformatics application on a fully operational implementation of an integrated grid market

We present the SweGrid Accounting System (SGAS) — a decentralized and standards-based system for Grid resource allocation enforcement that has been developed with an emphasis on a uniform data model and easy integration into existing scheduling and workload management software. The system has been tested at the six high-performance computing centers comprising the SweGrid computational resource, and addresses the need for soft, real-time quota enforcement across the SweGrid clusters. The SGAS framework is based on state-of-the-art Web and Grid services technologies. The openness and ubiquity of Web services combined with the fine-grained resource control and cross-organizational security models of Grid services proved to be a perfect match for the SweGrid needs. Extensibility and customizability of policy implementations for the three different parties that the system serves (the user, the resource manager, and the allocation authority) are key design goals. Another goal is end-to-end security and single sign-on, to allow resources to reserve allocations and charge for resource usage on behalf of the user. We conclude this paper by illustrating the policy customization capabilities of SGAS in a simulated setting, where job streams are shaped using different modes of allocation policy enforcement. Finally, we discuss some of the early experiences from the production system.

In this paper, we present an Open Grid Services Architecture (OGSA)-based decentralized allocation enforcement system, developed with an emphasis on a consistent data model and easy integration into existing scheduling, and workload management software at six independent high-performance computing centers forming a Grid known as SweGrid. The Swedish National Allocations Committee (SNAC) allocates resource quotas at these centers to research projects requiring substantial computer time. Our system, the SweGrid Accounting System (SGAS), addresses the need for soft real-time allocation enforcement on SweGrid for cross-domain job submission. The SGAS framework is based on state-of-the-art Web and Grid services technologies. The openness and ubiquity of Web services combined with the fine-grained resource control and cross-organizational security models of Grid services proved to be a perfect match for the SweGrid needs. Extensibility and customizability of policy implementations for the three different parties the system serves (the user, the resource manager, and the allocation authority) are key design goals. Another goal is end-to-end security and single sign-on, to allow resources-selected based on client policies-to act on behalf of the user when negotiating contracts with the bank in an environment where the six centers would continue to use their existing accounting policies and tools. We conclude this paper by showing the feasibility of SGAS, which is currently being deployed at the production sites, using simulations of reservation streams. The reservation streams are shaped using soft computing and policy-based algorithms.