Incentive-compatible route coordination of crowdsourced resources

Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments

A Feedback Control Approach to Mitigating Mistreatment

Abstract. We consider distributed collaborative caching groups where individual members are autonomous and self-aware. Such groups have been emerging in many new overlay and peer-to-peer applications. In a recent work of ours, we considered distributed caching protocols where group members (nodes) cooperate to satisfy requests for information objects either locally or remotely from the group, or otherwise from the origin server. In such setting, we identified the problem of a node being mistreated, i.e., its access cost for fetching information objects becoming worse with cooperation than without. We identified two causes of mistreatment: (1) the use of a common caching scheme which controls whether a node should not rely on other nodes in the group by keeping its own local copy of the object once retrieved from the group; and (2) the state interaction that can take place when the miss-request streams from other nodes in the group are allowed to affect the state of the local replacement algorithm. We also showed that both these issues can be addressed by introducing two simple additional parameters that affect the caching behavior (the reliance and the interaction parameters). In this paper, we argue against a static rule-of-thumb policy of setting these parameters since the performance, in terms of average object access cost, depends on a multitude of system parameters (namely, group size, cache sizes, demand skewness, and distances). We then propose a feedback control approach to mitigating mistreatment in distributed caching groups. In our approach, a node independently emulates its performance as if it were acting selfishly and then adapts its reliance and interaction parameters in the direction of reducing its measured access cost below its emulated selfish cost. To ensure good convergence and stability properties, we use a (Proportional-Integral-Differential) PID-style controller. Our simulation results show that our controller adapts to the minimal access cost and outperforms static-parameter schemes

A Domain-Specific Language for Incremental and Modular Design of Large-Scale Verifiably-Safe Flow Networks (Preliminary Report)

We define a domain-specific language (DSL) to inductively assemble flow networks from small networks or modules to produce arbitrarily large ones, with interchangeable functionally-equivalent parts. Our small networks or modules are "small" only as the building blocks in this inductive definition (there is no limit on their size). Associated with our DSL is a type theory, a system of formal annotations to express desirable properties of flow networks together with rules that enforce them as invariants across their interfaces, i.e, the rules guarantee the properties are preserved as we build larger networks from smaller ones. A prerequisite for a type theory is a formal semantics, i.e, a rigorous definition of the entities that qualify as feasible flows through the networks, possibly restricted to satisfy additional efficiency or safety requirements. This can be carried out in one of two ways, as a denotational semantics or as an operational (or reduction) semantics; we choose the first in preference to the second, partly to avoid exponential-growth rewriting in the operational approach. We set up a typing system and prove its soundness for our DSL.Comment: In Proceedings DSL 2011, arXiv:1109.032

Akita: a CPU scheduler for virtualized clouds

Clouds inherit CPU scheduling policies of operating systems. These policies enforce fairness while leveraging best-effort mechanisms to enhance responsiveness of all schedulable entities, irrespective of their service level objectives (SLOs). This leads to unpredictable performance that forces cloud providers to enforce strict reservation and isolation policies to prevent high-criticality services (e.g., Memcached) from being impacted by low-criticality ones (e.g., logging), which results in low utilization. In this paper, we present Akita, a hypervisor CPU scheduler that delivers predictable performance at high utilization. Akita allows virtual machines (VMs) to be categorized into high- and low-criticality VMs. Akita provides strong guarantees on the ability of cloud providers to meet SLOs of high-criticality VMs, by temporarily slowing down low-criticality VMs if necessary. Akita, therefore, allows the co-existence of high and low-criticality VMs on the same physical machine, leading to higher utilization. The effectiveness of Akita is demonstrated by a prototype implementation in the Xen hypervisor. We present experimental results that show the many advantages of adopting Akita as the hypervisor CPU scheduler. In particular, we show that high-criticality Memcached VMs are able to deliver predictable performance despite being co-located with low-criticality CPU-bound VMs.First author draf

A taxonomy of web prediction algorithms

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. © 2012 Elsevier Ltd. All rights reserved.This work has been partially supported by Spanish Ministry of Science and Innovation under Grant TIN2009-08201, Generalitat Valenciana under Grant GV/2011/002 and Universitat Politecnica de Valencia under Grant PAID-06-10/2424.Domenech, J.; De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Gil Salinas, JA.; Pont Sanjuan, A. (2012). A taxonomy of web prediction algorithms. Expert Systems with Applications. 39(9):8496-8502. https://doi.org/10.1016/j.eswa.2012.01.140S8496850239

Crowdcloud: A Crowdsourced System for Cloud Infrastructure

The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of wellconnected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their “canonical” architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/cocreated systems