Tanveer Ahmed

Minimizing Waiting Time for Service Composition: A Frictional Approach

Dynamic Service composition is the art of composing a composite application from a set of service offerings from various providers at run time. Service composition is a complex task and the problem is with the construction of hierarchies of service providers where each service provider processes only a part of the application. This is further exacerbated by the fact that for every part of the application there are several potential service providers. Choosing the most appropriate service from functionally equivalent services is the issue being addressed here. In this paper, we focus on the waiting time of a service request as the criterion for selection. We present a technique borrowed and customized from the concept of friction in physics to select the best potential candidate service from a set of functionally equivalent ones, so that the waiting time for each service request is minimized. The technique not only facilitates the selection of the most appropriate service but also minimizes the waiting time of service requests. The model is validated through simulation and is shown to be able to equitably balance the load among similar service offering nodes.

Understanding and evaluating the behavior of technical users. A study of developer interaction at StackOverflow

This paper is focused on analyzing the details of one of the most widely used Q&A technical discussion forums on the Internet: StackOverflow. StackOverflow is a huge source of information for both academics and industry practitioners and its analysis can provide useful insights. This is evident by a growing body of work dedicated to the study of this platform. There are several papers that have taken the ‘platform’ as an analyzing point to gain a few useful insights into the practices adopted by software developers. However, different from current literature, the objective of this paper is to explore the habit of users, more specifically, we want to find out how do technical users behave online? In doing so, we attempt to present a new picture that focuses more on the human aspect of this online community. We neither focus on the technical aspects of the platform nor do we focus on the technical capabilities of developers, rather, the focus of this paper is to explore the humanistic point of view of technical users. To do that, we mine StackOverflow’s repositories to find a few patterns that raise several questions. The observations presented in this paper highlight real world implications of having open call based software engineering platforms. Further, we conduct a survey of StackOverflow users to find an answer to one of the highly talked about topics on Meta (a discussion forum for StackOverflow). The subject of the survey concerns changing participation habit of users. As the paper progresses forward, we discuss factors that raise the issue of professionalism and ethics at this online venue. The findings show several malpractices happening at StackOverflow. We present a detailed analysis and discuss these issues in this paper.

An automated approach to estimate human interest

Can we model and estimate interest? In general, when an individual engages with an object, say Facebook, Instagram, a Mobile game, or anything else, we know that there is some interest that the person has in the object. However, we do not have a procedure that can tell us by “how much” of a factor is the person interested. Simply put, can we find a “number” for someone’s interest? In this article, we propose the design of a framework that can handle this issue. We formulate the interest estimation problem as a state estimation problem and deduce interest indirectly from the activity. Activity, stimulated by interest, is measured via a subjective-objective weighted approach. Further, we present a novel continuous-time model for interest by drawing inspiration from Physics and Economics simultaneously. We model interest along the Ornstein-Uhlenbeck process in Physics and improve the performance by borrowing ideas from Stochastic Volatility Models in Economics. Subsequently, we employ particle filter to solve the interest estimation problem. To validate the feasibility of the proposed theory in practice, we investigate the model by conducting numerical simulations on real-world datasets. The results demonstrate good performance of the framework, and thus match the theoretical expectations from the method. Lastly, we implement the framework in practice and deploy it as a RESTful service, thereby providing a uniform interface for accessing the procedure via any remote or local application.

Choreographing Services over Mobile Devices

Owing to the proliferation of web services, service oriented architecture (SOA) is widely acknowledged as an ideal paradigm for both enterprise applications and compute intensive scientific processes. In todays world, the present scenario of conducting business has found a new inclination towards the Mobile Device. Mobile devices, however, are constrained by battery power, processing capability, availability and network outages. Achieving service composition in such dynamic environment is challenging. In this paper, we propose a technique inspired by Electromagnetism in Physics to enact service choreography over mobile devices. The focus of the work is to minimize the waiting time and to balance load between services of a similar kind, thereby preserving battery power. The technique is validated through a real prototype. We prove the model minimized battery consumption and achieved a reduction in the waiting time.

A Novel Physics Inspired Approach for Web Service Composition

Service oriented architecture has revolutionized the way a traditional business process is executed. The success of this architecture is Indue to the composition of multiple heterogeneous services at runtime. Web service composition is a mechanism where several web services are combined at runtime to build a complex application for a user. It is one of the most sought after processes in the context of semantic web. But, composition of web services at runtime is a difficult task owing to the availability of multiple service providers offering the same functionality. The process if exasperated by due conflicting preferences of a service consumer. In this paper, the authors address the issue of selecting a service based on Quality of Service (QoS) attributes. They utilize concepts customized from physics to create an environment that facilitates the selection of a best service from the set of similar services. The technique not only facilitates the selection of the service with the best QoS attributes, but distributes the load among expeditiously. Here in this paper, the authors concentrate on minimizing and equitably balancing the waiting time for a user. They conduct in silico experiments on multiple workflows to demonstrate the efficacy of the proposed technique to balance load efficiently among similar service offerings.

Analyzing crowdsourcing to teach mobile crowdsensing a few lessons

In recent years, mobile computing has shown so much potential that one can see a boundary-blurring expansion between the physical and the digital world. In this context, one of the most sought after research areas is mobile crowdsensing. To realize the vision of crowdsensing, there is a plethora of work in the literature that focuses on the technical capabilities of a mobile device. However, an important and a critical problem that eludes literature is the issue of human participation. We base this argument on a very simple, yet powerful fact that a mobile device is still a person’s private property. Therefore, considering human mentality, can we expect a person to contribute all the time? In this respect, it is not feasible for a human dependent computational system to ignore the inevitable human factor and focus only on the mechanical properties. In this paper, we look into the often ignored human aspects and will study the problem from a psychological perspective. We take inspiration from the mature paradigm of crowdsourcing and discuss the importance of a few human factors that could teach us how to encourage user participation in mobile crowdsensing. Further, we also explore a person’s habitual characteristics that could help answer the decade’s old question: How to get quality responses from the crowd? We use a psycho-technological approach to observe, understand, and find a few details regarding human behavior in online systems. Lastly, we take inspiration from the analysis to present a roadmap that aids in engineering a better and effective crowdsensing platform.

A Prototype Model to Predict Human Interest: Data Based Design to Combine Humans and Machines

In this paper, the possibility of quantifying a persons interest using data-driven algorithms is investigated. In doing so, interest estimation problem is formulated as a latent state estimation problem, and an answer is deduced via Bayesian Inference. First, a Subjective-Objective approach is used to measure activity. Through this calculated activity, the method indirectly infers human latent state values. A formulation of interest is then presented by drawing inspiration from the Ornstein-Uhlenbeck (OU) process in Physics. Moreover, concepts of stochastic volatility are employed to vary the instantaneous volatility of the OU process. This is done to further improve the performance. Subsequently, the convergence speed of the OU process is varied with time. A novel statistical framework is discussed that dynamically transforms interest into activity. Each of these individual contributions is combined to present a solution via Monte Carlo Simulations. To demonstrate the efficacy of the proposed method, numerical simulations are performed on real datasets. Lastly, a prototype is engineered and the method is implemented as a RESTful Web service. The prototype is hosted as a Web service on several Virtual Machines to demonstrate the practical feasibility of the framework in cloud-based deployment scenarios.

Membrane Computing Inspired Approach for Executing Scientific Workflow in the Cloud

The continuous expansion and appreciation of the service oriented architecture is due to the standards of loose-coupling and platform independence. Service-Oriented Architecture is the most commonly and effectively realized through web services, and their temporal collaboration commonly referred to as web service composition. In the present scenario, the most popular variant of composition is service orchestration. Orchestration is achieved through a centralized ‘heavyweight’ engine, the orchestrating agent, that makes the deployment configuration a massive ‘choke-point’. The issue achieves significance when data and compute intensive scientific applications rely on such a centralized scheme. Lately, a lot of research efforts are put in to deploy a scientific application on the cloud, thereby provisioning resources elastically at runtime. In this paper, we aim at eliminating this central ‘choke’ point by presenting a model inspired from ‘Membrane Computing’ that executes a scientific workflow in a decentralized manner. The benefit of this paradigm comes from the natural process of autonomy, where each cell provision resources and execute process-steps on its own. The approach is devised keeping in mind, the feasibility of deployment on a cloud based infrastructure. To validate the model, a prototype is developed and real scientific workflows are executed in-house (with-in the Intranet). Moreover, the entire prototype is also deployed on a virtualized platform with software defined networking, thereby studying the effects of a low bandwidth environment, and dynamic provisioning of resources.