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We consider a distributed server system consisting of a large number of servers, each with limited capacity on multiple resources (CPU, memory, disk, etc.). Jobs with different rewards arrive over time and require certain amounts of resources for the duration of their service. When a job arrives, the system must decide whether to admit it or reject it, and if admitted, in which server to schedule the job. The objective is to maximize the expected total reward received by the system. This problem is motivated by control of cloud computing clusters, in which, jobs are requests for Virtual Machines or Containers that reserve resources for various services, and rewards represent service priority of requests or price paid per time unit of service by clients. We study this problem in an asymptotic regime where the number of servers and jobs' arrival rates scale by a factor L, as L becomes large. We propose a resource reservation policy that asymptotically achieves at least 1/2, and under certain monotone property on jobs' rewards and resources, at least 11/4 of the optimal expected reward. The policy automatically scales the number of VM slots for each job type as the demand changes, and decides in which servers the slots should be created in advance, without the knowledge of traffic rates. It effectively tracks a low-complexity greedy packing of existing jobs in the system while maintaining only a small number, g(L) = w(logL), of reserved VM slots for high priority jobs that pack well.

We consider a natural scheduling problem which arises in many distributed computing frameworks. Jobs with diverse resource requirements (e.g. memory requirements) arrive over time and must be served by a cluster of servers, each with a finite resource capacity. To improve throughput and delay, the scheduler can pack as many jobs as possible in the servers subject to their capacity constraints. Motivated by the ever-increasing complexity of workloads in shared clusters, we consider a setting where the jobs’ resource requirements belong to a very large number of diverse types or, in the extreme, even infinitely many types, e.g. when resource requirements are drawn from an unknown distribution over a continuous support. The application of classical scheduling approaches that crucially rely on a predefined finite set of types is discouraging in this high (or infinite) dimensional setting. We first characterize a fundamental limit on the maximum throughput in such setting, and then develop oblivious scheduling algorithms that have tow complexity and can achieve at least 1/2 and 2/3 of the maximum throughput, without the knowledge of traffic or resource requirement distribution. Extensive simulation results, using both synthetic and real traffic traces, are presented to verify the performance of our algorithms.

Evolution of mobile technologies and their rapid penetration into people's daily lives, especially in the developing countries, have highlighted mobile health, or m-health, as a promising solution to improve health outcomes. Several studies have been conducted that characterize the impact of m-health solutions in resource-limited settings and assess their potential to improve health care. The aim of this review is twofold: 1) to present an overview of the background and significance of m-health and 2) to summarize and discuss the existing evidence for the effectiveness of m-health in the developing world. A systematic search in the literature was performed in Pubmed, Scopus, as well as reference lists, and a broad sample of 98 relevant articles was identified, which were then categorized into five wider m-health categories. Although statistically significant conclusions cannot be drawn since the majority of studies relied on small-scale trials and limited assessment of long-term effects, this review provides a systematic and extensive analysis of the advantages, disadvantages, and challenges of m-health in developing countries in an attempt to determine future research directions of m-health interventions.

We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service \textit{cannot} be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio $r$, our scheduling algorithm can provide $r\beta$ fraction of the throughput region for $\beta < r$. In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to $\beta, Comment: 29 pages

This paper presents a technique for admission control of a set of horizontally scalable services, and their optimal placement, into a federated Cloud environment. In the proposed model, the focus is on hosting elastic services whose resource requirements may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. The request may also be partially accommodated in federated external providers, if needed or more convenient. In finding the optimum allocation, the presented mechanism uses a probabilistic optimization model, which takes into account eco-efficiency and cost, as well as affinity and anti-affinity rules possibly in place for the components that comprise the services. In addition to modelling and solving the exact optimization problem, we also introduce a heuristic solver that exhibits a reduced complexity and solving time. We show evaluation results for the proposed technique under various scenarios.

We consider the fully automated behavior understanding through visual cues in industrial environments. In contrast to most existing work, which relies on domain knowledge to construct complex handcrafted features from inputs, we exploit a Convolutional Neural Network (CNN), which is a type of deep model and can act directly on the raw inputs, to automate the process of feature construction. Although such models are limited to handle still 2D inputs, in this paper we appropriately transform video input to incorporate temporal information into each frame. This way our model hierarchically constructs features from both spatial and temporal dimensions. We apply our model in real-world environment, on data taken from Nissan factory, and it achieves superior performance without relying on handcrafted features.

The rapid penetration of social media in many aspects of today’s personal and professional life has created an environment where users within this emerging social cyberspace expect the kind of media experiences they are accustomed to in their daily lives with the community type of communications being at the central stage. In that context social networks and multimedia can be combined and instrumented by monitoring mechanisms to offer new experiences in many areas, among which the cultural and educational sector has a special position. This paper presents an innovative experimental system that enhances the experience of visitors in cultural centers. Apart from offering a modern, immersive, richer experience to the visitors, the proposed system involves significant benefits for the organizers as well, supplying them with valuable feedback extracted from the monitoring and analysis mechanisms. The experimental system has been successfully deployed and used in the Foundation of the Hellenic World cultural center.

The rapid growth of social media and the ubiquitous availability of audiovisual content have transformed virtually every aspect of our personal and professional lives. In Internet-based multimedia applications users with their activity (including social life) are both the source of data and the consumers of the applications, hence being involved in the creation of user-oriented business value. In this paper we present a model that blends video on demand technologies with social media into an innovative and practical distributed application to be used in situations whereby multimedia content needs to be reviewed by multiple individuals that may be geographically dispersed, and altered afterwards by other experts according to the received comments. The proposed system significantly speeds up such remote collaborations while at the same time significantly reducing their cost.

The advancement of ICT has brought about significant changes in the way the cultural and creative industries work as well as in the experience they offer. However there is still a lot of potential that can be leveraged to advance the state of the art in the way the public shares collective cultural experiences. In this paper, we employ different concepts and technologies, such as high quality multimedia content production and delivery, social media interaction, and methodologies for monitoring and analyzing social network activity, to revamp the experience offered in cultural and educational centres. In addition to an increased Quality of Experience, the proposed system involves significant benefits for the organizers as well, providing them with feedback based on social analytics. The experimental system has been successfully deployed and used in the "Hellenic Cosmos" cultural center.

The paper shows how different concepts and technologies, such as high quality multimedia content production and delivery, social media interaction, and methodologies for monitoring and analysing social network activity, can be practically combined with traditional virtual reality installations into an innovative experimental system that enhances the experience of the visitors in cultural educational centers. Apart from offering a modern, immersive, richer experience to the visitors, the proposed system involves significant benefits for the organizers as well, supplying them with valuable feedback extracted from the monitoring and analysis mechanisms. The experimental system has been successfully deployed and used in the Foundation of the Hellenic World cultural center.

The compound eyes of the fruit flies consist of the retina and 4 neuropils. The retina comprises about 700~800 ommatidia, each of which houses 8 photoreceptors, where the phototransduction takes place. Although seemingly simple, each photoreceptor can perform complex computation on their own [1]. Nevertheless, it is not yet clear how individual photoreceptors contribute to an overall spatiotemporal processing of visual scenes. Towards this goal, we constructed a full-scale simulation of the retina on a cluster of GPUs. The geometry of the eye was taken into account by building a hexagonal array of 721 ommatidia on a hemisphere and assigning a proper optic axis and an acceptance angle to each of the photoreceptors. The visual stimulus was presented on either a hemisphere or a cylinder surrounding the eye, as in many experimental settings (see Figure ​Figure11). Figure 1 A. Stimulus presented on a hemisphere screen. B. Inputs (Number of Photons) to R1 photoreceptors in all ommatidia. C. Geometric relation between eye and screen. D. Gamma corrected screen intensity. E. Gamma corrected inputs to R1 photoreceptors. F. Voltage ... The phototransduction process is based on a biochemical model of 30,000 microvilli followed by a conductance based biophysical model of the cell membrane [1]. Here we only consider monochromatic photoreceptors R1-R6 in each ommatidium. Each photoreceptor is described by ~ 450, 000 equations; the total number of equations simulated for the entire retina amounted to about 1.95 billion. All the simulations were performed on 4 Tesla K20X GPUs; it took approx. 7 minutes to finish 1 second of simulation. After constructing the detailed simulation model, we tested the processing of visual stimuli by the retina circuit. Furthermore, to test the effect of feedback originating in the lamina on the photoreceptors, we linked the retina model with a lamina model [2] in the Neurokernel environment [3]. An example of response of the circuit to natural stimuli is shown in Figure ​Figure1.1. The simulations reveal that composition rules in the lamina circuit affect the spatiotemporal processing carried out by the photoreceptors under the model parameters considered here. Finally, we scaled up the retina model of the fruit fly to the size of the blow fly to investigate the effect of visual acuity and noise.