Sporadic cloud-based mobile augmentation on the top of a virtualization layer: a case study of collaborative downloads in VANETs

Current approaches to Cloud-based Mobile Augmentation (CMA) leverage (cloud-based) resources to meet the requirements of rich mobile applications, so that a terminal (the so-called application node or AppN) can borrow resources lent by a set of collaborator nodes (CNs). In the most sophisticated approaches proposed for vehicular scenarios, the collaborators are nearby vehicles that must remain together near the application node because the augmentation service is interrupted when they move apart. This leads to disruption in the execution of the applications and consequently impoverishes the mobile users’ experience. This paper describes a CMA approach that is able to restore the augmentation service transparently when AppNs and CNs separate. The functioning is illustrated by a NaaS model where the AppNs access web contents that are collaboratively downloaded by a set of CNs, exploiting both roadside units and opportunistic networking. The performance of the resulting approach has been evaluated via simulations, achieving promising results in terms of number of downloads, average download times, and network overheadMinisterio de Educación y Ciencia | Ref. TIN2017-87604-

Rapid Behavioral and Genomic Responses to Social Opportunity

From primates to bees, social status regulates reproduction. In the cichlid fish Astatotilapia (Haplochromis) burtoni, subordinate males have reduced fertility and must become dominant to reproduce. This increase in sexual capacity is orchestrated by neurons in the preoptic area, which enlarge in response to dominance and increase expression of gonadotropin-releasing hormone 1 (GnRH1), a peptide critical for reproduction. Using a novel behavioral paradigm, we show for the first time that subordinate males can become dominant within minutes of an opportunity to do so, displaying dramatic changes in body coloration and behavior. We also found that social opportunity induced expression of the immediate-early gene egr-1 in the anterior preoptic area, peaking in regions with high densities of GnRH1 neurons, and not in brain regions that express the related peptides GnRH2 and GnRH3. This genomic response did not occur in stable subordinate or stable dominant males even though stable dominants, like ascending males, displayed dominance behaviors. Moreover, egr-1 in the optic tectum and the cerebellum was similarly induced in all experimental groups, showing that egr-1 induction in the anterior preoptic area of ascending males was specific to this brain region. Because egr-1 codes for a transcription factor important in neural plasticity, induction of egr-1 in the anterior preoptic area by social opportunity could be an early trigger in the molecular cascade that culminates in enhanced fertility and other long-term physiological changes associated with dominance

Cooperative Processing: An Agenda for Research

The purpose of this paper is to explore the potential research agenda for Cooperative Processing (COP). COP is a method of processing in which communications is an integral part of the process of executing an application. Numerous innovative products that support COP are starting to appear on the market. However, as is usually the case with any new technology, many organizations have not yet implemented COP. They have embraced a wait and see attitude. Such a stance can be attributed to the newness of the applications operating in COP mode and to the lack of data demonstrating COP\u27S uses and benefits. For example, no data, at present, demonstrates basic COP efficacy. Among many possible research areas this paper suggests topics such as: potential COP users, types of applications benefiting from the COP processing mode, and organizational and technological factors involved in COP implementation

Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-Max Fairness Guarantee

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power and radio bandwidth, while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC and the number of beneficial UEs over existing algorithms

Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-Max Fairness Guarantee

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power and radio bandwidth, while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC and the number of beneficial UEs over existing algorithms

Derivation of the required elements for a definition of the term middleware

Thirteen contemporary definitions of Middleware were analyzed. The definitions agree that any software that can do the following should be classified as Middleware (1) provide service that provides transparent application-to-application interaction across the network, (2) act as a service provider for distributed applications, and (3) provide services that are primarily used by distributed applications (e.g., RPCs, ORBs, Directories, name-resolution services, etc.) Most definitions agree that Middleware is that level of software required to achieve platform, location, and network transparency. There is some discrepancy about the OSI levels at which middleware operates. The majority of definitions limit it to levels 5, 6, and 7. Additionally, almost half of the definitions do not include database transparency as something achieved by Middleware, perhaps due to the ambiguous classification of ODBC and JDBC as software. Assuming that the number of times a service is mentioned, the majority of the definitions rank services associated with legal access to an application as core to Middleware, along with valid, standardized APIs for application development as core to the definition of middleware

Online Reinforcement Learning for Dynamic Multimedia Systems

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori. In practice, however, these dynamics are unknown a priori and therefore must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner, and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and inter-layer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.Comment: 35 pages, 11 figures, 10 table

Sharing e-Health information through ontological layering

e-Health information, including patient clinical and demographic data, is very often dispersed across various environments, which either generate them or retrieve them from different sources. Healthcare professionals often need related e-health information in order to obtain a more comprehensive picture of a patient's health status. There are many obstacles to retrieving information and data from heterogeneous sources. In this paper we show that our ontological layering helps in (a) classifying requests imposed by healthcare professionals when retrieving e-health information from heterogeneous sources and (b) resolving semantic heterogeneities across repositories and composing an adequate answer to issued requests. We use a layered software architectural model based on Generic ontology for Context-aware, Interoperable and Data sharing (Go- CID) software applications, applicable to e-Health environments. Ontological layering and reasoning have been demonstrated with semantic web technologies