No abstract

No abstract

No abstract

We study queueing systems where customers have strict deadlines until the end of their service. An analytic method is given for the analysis of a class of such queues, namely, M(n)/M/1 + G models with ordered service. These are single-server queues with state-dependent Poisson arrival process, exponential service times, FCFS service discipline, and general customer impatience. We derive a closed-form solution for the conditional probability density function of the offered sojourn time, given the number of customers in the system. This is a novel result that has not been known before. Using this result, we show how the probability measure induced by the offered sojourn time is computed, and consequently how performance measures such as the probability of missing deadline and the probability of blocking are obtained. This is further illustrated through a numerical example.

No abstract

IntroductionDistributed and parallel processing is one of the best intelligent ways to store and compute big data [1]. Most definitions defined big data as characterized by the 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. MapReduce [2] is a programming model for big data processing. MapReduce programs are intrinsically parallel [3,4]. MapReduce executes the programs in two phases, map and reduce, so that each phase is defined by a function called mapper and reducer. A MapReduce framework consists of a master and multiple slaves. The master is responsible for the management of the framework, including user interaction, job queue organization and task scheduling. Each slave has a fixed number of map and reduce slots to perform tasks. The job scheduler located in the master assigns tasks according to the number of free task slots AbstractDue to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. MapReduce has been introduced to solve largedata computational problems. It is specifically designed to run on commodity hardware, and it depends on dividing and conquering principles. Nowadays, the focus of researchers has shifted towards Hadoop MapReduce. One of the most outstanding characteristics of MapReduce is data locality-aware scheduling. Data locality-aware scheduler is a further efficient solution to optimize one or a set of performance metrics such as data locality, energy consumption and job completion time. Similar to all situations, time and scheduling are the most important aspects of the MapReduce framework. Therefore, many scheduling algorithms have been proposed in the past decades. The main ideas of these algorithms are increasing data locality rate and decreasing the response and completion time. In this paper, a new hybrid scheduling algorithm has been proposed, which uses dynamic priority and localization ID techniques and focuses on increasing data locality rate and decreasing completion time. The proposed algorithm was evaluated and compared with Hadoop default schedulers (FIFO, Fair), by running concurrent workloads consisting of Wordcount and Terasort benchmarks. The experimental results show that the proposed algorithm is faster than FIFO and Fair scheduling, achieves higher data locality rate and avoids wasting resources.

No abstract

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three wellknown real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-ofthe-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction.