Technical considerations towards mobile user QoE enhancement via Cloud interaction

This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Differentiated Predictive Fair Service for TCP Flows

The majority of the traffic (bytes) flowing over the Internet today have been attributed to the Transmission Control Protocol (TCP). This strong presence of TCP has recently spurred further investigations into its congestion avoidance mechanism and its effect on the performance of short and long data transfers. At the same time, the rising interest in enhancing Internet services while keeping the implementation cost low has led to several service-differentiation proposals. In such service-differentiation architectures, much of the complexity is placed only in access routers, which classify and mark packets from different flows. Core routers can then allocate enough resources to each class of packets so as to satisfy delivery requirements, such as predictable (consistent) and fair service. In this paper, we investigate the interaction among short and long TCP flows, and how TCP service can be improved by employing a low-cost service-differentiation scheme. Through control-theoretic arguments and extensive simulations, we show the utility of isolating TCP flows into two classes based on their lifetime/size, namely one class of short flows and another of long flows. With such class-based isolation, short and long TCP flows have separate service queues at routers. This protects each class of flows from the other as they possess different characteristics, such as burstiness of arrivals/departures and congestion/sending window dynamics. We show the benefits of isolation, in terms of better predictability and fairness, over traditional shared queueing systems with both tail-drop and Random-Early-Drop (RED) packet dropping policies. The proposed class-based isolation of TCP flows has several advantages: (1) the implementation cost is low since it only requires core routers to maintain per-class (rather than per-flow) state; (2) it promises to be an effective traffic engineering tool for improved predictability and fairness for both short and long TCP flows; and (3) stringent delay requirements of short interactive transfers can be met by increasing the amount of resources allocated to the class of short flows.National Science Foundation (CAREER ANI-0096045, MRI EIA-9871022

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems