Nazaraf Shah

Virtual machine consolidated placement based on multi-objective biogeography-based optimization

Virtual machine placement (VMP) is an important issue in selecting most suitable set of physical machines (PMs) for a set of virtual machines (VMs) in cloud computing environment. VMP problem consists of two sub problems: incremental placement (VMiP) problem and consolidated placement (VMcP) problem. The goal of VMcP is to consolidate the VMs to more suitable PMs. The challenge in VMcP problem is how to find optimal solution effectively and efficiently especially when VMcP is a kind of NP-hard problem. In this paper, we present a novel solution to the VMcP problem called VMPMBBO. The proposed VMPMBBO treats VMcP problem as a complex system and utilizes the biogeography-based optimization (BBO) technique to optimize the virtual machine placement that minimizes both the resource wastage and the power consumption at the same time. Extensive experiments have been conducted using synthetic data from related literature and data from two real datasets. First of all, the necessity of VMcP has been proved by experimental results obtained by applying VMPMBBO. Then, the proposed method is compared with two existing multi-objective VMcP optimization algorithms and it is shown that VMPMBBO has better convergence characteristics and is more computationally efficient as well as robust. And then, the issue of parameter setting of the proposed method has been discussed. Finally, adaptability and extensibility of VMPMBBO have also been proved through experimental results. To the best of our knowledge, this work is the first approach that applies biogeography-based optimization (BBO) to virtual machine placement. Clarify problems of incremental placement and consolidated placement of virtual machine.Build a optimization model of power consumption, resource wastage, server loads, inter-VM and storage network traffic.Firstly apply the BBO meta heuristic to virtual machine consolidated placement problem.Adopt a new strategy about migration rate generation, which beats original and other three strategies.Experimental results verified the robustness, adaptability and extensibility of the proposed method.

Exception representation and management in open multi-agent systems

One of the major issues in dealing with exceptions in open multi-agent systems (MAS) is lack of uniform representation of exceptions and their shared semantics. In the absence of a uniform framework different business organizations may use different representations for the same exception or may interpret the same exception in different ways. In order to address this issue we apply an ontological approach as a uniform way of representing and interpreting exceptions in cross-organizational settings. This helps agents from different organizations interpret exceptional situations in an unambiguous way and exchange exception related information using standard structures. We believe that an exception ontology along with a domain ontology increases the open MAS reliability and also enhances its fault tolerance capability. The proposed ontology is built using the ontological support provided by the JADE agent framework and exception diagnoses agents are implemented using the JACK(TM) agent framework.

Exception diagnosis in open multi-agent systems

Open multi-agent systems (MAS) are decentralized and highly distributed systems that consist of a large number of loosely coupled autonomous agents. Diagnosing exceptions in such systems is a complex task due to the distributed nature of their data and their control. This complexity is exacerbated in open environments where independently developed autonomous agents interact with each other in order to achieve their goals. Inevitably, exceptions will occur in such MAS and these exceptions can arise at one of three levels, namely environmental, knowledge and social levels. In this paper we propose a novel exception diagnosis system that is able to analyse and detect exceptions effectively. The proposed architecture consists of specialised exception diagnosis agents called sentinel agents. The sentinel agents are equipped with knowledge of observable abnormal situations, and their underlying causes.

A Profile Based Energy Management System for Domestic Electrical Appliances

Climate change is one of the driving forces behind a new wave of energy management systems. Most of the currently available energy management systems in domestic environment are concerned with real-time energy consumption monitoring, and display of statistical and real time data of energy consumption. Although these systems play a crucial role in providing a detailed picture of energy consumption in home environment and contribute towards influencing the energy consumption behavior of household, they all leave it to households to take appropriate measures to reduce their energy consumption. Some energy management systems do provide general energy saving tips but they do not consider the household profiles and energy consumption profiles of home appliances. The proposed system attempts to address this issue by taking into account household profiles and energy consumption profiles of electrical appliances. The motivation behind this approach is to provide households effective advice on their energy consumption by enabling them to take focused and effective actions towards efficient energy use.

Exception diagnosis in agent-based grid computing

Diagnosing exceptions in multi-agent systems (MAS) is a complex task due to the distributed nature of the data and control in such systems. This complexity is exacerbated in open environments where independently developed autonomous agents interact with each other in order to achieve their goals. Inevitably, exceptions would occur in such MAS and these exceptions can arise at one of three levels, namely environmental, knowledge and social levels. In this paper we propose a novel exception diagnosis system that is able to analyse and detect exceptions effectively. The proposed architecture consists of specialised exception diagnosis agents called sentinel agents. The sentinel agents are equipped with knowledge of observable abnormal situations, their underlying causes, and resolution strategies associated with these causes. The sentinel agent applies a heuristic classification approach to collect related data from affected agents in order to uncover the underlying causes of the observed symptoms. We illustrate and evaluate our proposed architecture using an agent-based grid computing case study.

Towards the development of an integrated framework for enhancing enterprise search using latent semantic indexing

While we have seen significant success in web search, enterprise search has not yet been widely investigated and as a result the benefits that can otherwise be brought to the enterprise are not fully realized. In this paper, we present an integrated framework for enhancing enterprise search. This framework is based on open source technologies which include Apache Hadoop, Tika, Solr and Lucene. Importantly, the framework also benefits from a Latent Semantic Indexing (LSI) algorithm to improve the quality of search results. LSI is a mathematical model used to discover the semantic relationship patterns in a documents collection. We envisage that the proposed framework will benefit various enterprises, improving their productivity by meeting information needs effectively.

E-Marketing Strategy for Businesses

The Internet provides an easy and uniform way for businesses to make their brands and products visible to their customers. Due to the vast number of companies that take advantage of the Internet to conduct their business, it is very challenging for them to increase their sales and market awareness. In this paper, we investigate two techniques which are used to improve the visibility of an e-business in order to generate more traffic and sales. We describe a process of capturing the Return on Investment (ROI) from Search Engine Marketing (SEM) and focus on two main techniques: Search Engine Optimization (SEO) and Pay Per Click campaign (PPC). The investigation is carried out based on two UK-based small and medium sized enterprises (SME). We describe the results of optimization and its impact on the companies’ future strategy in this paper.

On the partitioning of dynamic workforce scheduling problems

This problem is based on the British Telecom workforce scheduling problem, in which technicians (with different skills) are assigned to tasks (which require different skills) which arrive (partially) dynamically during the day. In order to manage their workforce, British Telecom divides the different regions into several areas. At the beginning of each day all the technicians in a region are assigned to one of these areas. During the day, each technician is limited to tasks within the assigned area.This effectively decomposes a large dynamic scheduling problem into smaller problems. On one hand, it makes the problem more manageable. On the other hand, it gives rise to, potentially, a mismatch between technicians and tasks within an area. Furthermore, it prevents technicians from being assigned a job which is just outside their area but happens to be close to where they are currently working.This paper studies the effect of the number of partitions on the expected objective (number of completed tasks) that a rule-based system (responsible for the dynamic assignment and reassignment of tasks to resources following dynamic events) can reach.

Integration, optimization and usability of enterprise applications

Many enterprises rely on a wide variety of collaborative applications in order to support their everyday activities and to share resources. The collaborative applications are typically designed from scratch if the existing applications do not meet the enterprises evolving needs. This incurs significant costs, and inconvenience. In this paper we present a case study of six applications (Sage 200, Gold-Vision CRM system, E-Commerce System, Gold-Vision Connect System, Realex Transaction and Spindle Document Automation Tool) within an enterprise. These applications are working in isolation. Therefore, sharing of information and data among these applications is carried out manually which imposes additional burden on their users and causes performance degradation. In this paper, we address this problem by integration and optimization of these applications. We also address the usability problems of these applications. We present comparative evaluation results that show significant improvement in ease and performance of user tasks using integrated applications.

Hybrid Recommendation System for Tourism

This paper adopts item-based collaborative filtering to predict the interests of an active tourist by collecting preferences or taste information from a number of other tourists. Our proposed mechanism is able to predict a set recommended tourism places of elicited rating places (e.g., ratings of 1 through 5 stars) for the active tourist pre-traveling places. Furthermore, giving restriction of traveling factors, such as budge and time, the recommendation system will refine the exact set of tourism places by applying genetic algorithm mechanism. Finally, the system is based on minimum cost to schedule traveling path from a set of selected places by the using genetic algorithm approach. Our proposed hybrid recommendation algorithm focuses on the refining efficiency and provides multi-functional tourism information.