Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams

Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets

Developing Real-Time Emergency Management Applications: Methodology for a Novel Programming Model Approach

The last years have been characterized by the arising of highly distributed computing platforms composed of a heterogeneity of computing and communication resources including centralized high-performance computing architectures (e.g. clusters or large shared-memory machines), as well as multi-/many-core components also integrated into mobile nodes and network facilities. The emerging of computational paradigms such as Grid and Cloud Computing, provides potential solutions to integrate such platforms with data systems, natural phenomena simulations, knowledge discovery and decision support systems responding to a dynamic demand of remote computing and communication resources and services. In this context time-critical applications, notably emergency management systems, are composed of complex sets of application components specialized for executing specific computations, which are able to cooperate in such a way as to perform a global goal in a distributed manner. Since the last years the scientific community has been involved in facing with the programming issues of distributed systems, aimed at the definition of applications featuring an increasing complexity in the number of distributed components, in the spatial distribution and cooperation between interested parties and in their degree of heterogeneity. Over the last decade the research trend in distributed computing has been focused on a crucial objective. The wide-ranging composition of distributed platforms in terms of different classes of computing nodes and network technologies, the strong diffusion of applications that require real-time elaborations and online compute-intensive processing as in the case of emergency management systems, lead to a pronounced tendency of systems towards properties like self-managing, self-organization, self-controlling and strictly speaking adaptivity. Adaptivity implies the development, deployment, execution and management of applications that, in general, are dynamic in nature. Dynamicity concerns the number and the specific identification of cooperating components, the deployment and composition of the most suitable versions of software components on processing and networking resources and services, i.e., both the quantity and the quality of the application components to achieve the needed Quality of Service (QoS). In time-critical applications the QoS specification can dynamically vary during the execution, according to the user intentions and the Developing Real-Time Emergency Management Applications: Methodology for a Novel Programming Model Approach Gabriele Mencagli and Marco Vanneschi Department of Computer Science, University of Pisa, L. Bruno Pontecorvo, Pisa Italy 2 2 Will-be-set-by-IN-TECH information produced by sensors and services, as well as according to the monitored state and performance of networks and nodes. The general reference point for this kind of systems is the Grid paradigm which, by definition, aims to enable the access, selection and aggregation of a variety of distributed and heterogeneous resources and services. However, though notable advancements have been achieved in recent years, current Grid technology is not yet able to supply the needed software tools with the features of high adaptivity, ubiquity, proactivity, self-organization, scalability and performance, interoperability, as well as fault tolerance and security, of the emerging applications. For this reason in this chapter we will study a methodology for designing high-performance computations able to exploit the heterogeneity and dynamicity of distributed environments by expressing adaptivity and QoS-awareness directly at the application level. An effective approach needs to address issues like QoS predictability of different application configurations as well as the predictability of reconfiguration costs. Moreover adaptation strategies need to be developed assuring properties like the stability degree of a reconfiguration decision and the execution optimality (i.e. select reconfigurations accounting proper trade-offs among different QoS objectives). In this chapter we will present the basic points of a novel approach that lays the foundations for future programming model environments for time-critical applications such as emergency management systems. The organization of this chapter is the following. In Section 2 we will compare the existing research works for developing adaptive systems in critical environments, highlighting their drawbacks and inefficiencies. In Section 3, in order to clarify the application scenarios that we are considering, we will present an emergency management system in which the run-time selection of proper application configuration parameters is of great importance for meeting the desired QoS constraints. In Section 4we will describe the basic points of our approach in terms of how compute-intensive operations can be programmed, how they can be dynamically modified and how adaptation strategies can be expressed. In Section 5 our approach will be contextualize to the definition of an adaptive parallel module, which is a building block for composing complex and distributed adaptive computations. Finally in Section 6 we will describe a set of experimental results that show the viability of our approach and in Section 7 we will give the concluding remarks of this chapter

A Decision-Theoretic Approach to Resource Allocation in Wireless Multimedia Networks

The allocation of scarce spectral resources to support as many user applications as possible while maintaining reasonable quality of service is a fundamental problem in wireless communication. We argue that the problem is best formulated in terms of decision theory. We propose a scheme that takes decision-theoretic concerns (like preferences) into account and discuss the difficulties and subtleties involved in applying standard techniques from the theory of Markov Decision Processes (MDPs) in constructing an algorithm that is decision-theoretically optimal. As an example of the proposed framework, we construct such an algorithm under some simplifying assumptions. Additionally, we present analysis and simulation results that show that our algorithm meets its design goals. Finally, we investigate how far from optimal one well-known heuristic is. The main contribution of our results is in providing insight and guidance for the design of near-optimal admission-control policies.Comment: To appear, Dial M for Mobility, 200

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

Motorized cart

Motorized cart is known as an effective tool and timeless that help people carry heavy loads. For farmers, it has an especially vital tool for moving goods. Oil palm farmers typically uses the wheelbarrow to move the oil palm fruit (Figure 10.1). However, there is a lack of equipment that should be further enhanced in capabilities. Motorized carts that seek to add automation to wheelbarrow as it is to help people save manpower while using it. At present, oil palm plantation industry is among the largest in Malaysia. However, in an effort to increase the prestige of the industry to a higher level there are challenges to be faced. Shortage of workers willing to work the farm for harvesting oil palm has given pain to manage oil palm plantations. Many have complained about the difficulty of hiring foreign workers and a high cost. Although there are tools that can be used to collect or transfer the proceeds of oil palm fruits such as carts available. However, these tools still have the disadvantage that requires high manpower to operate. Moreover, it is not suitable for all land surfaces and limited cargo space. Workload and manpower dependence has an impact on farmers' income

Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author