PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids

In recent years, grid technology has had such a fast growth that it has been used in many scientific experiments and research centers. A large number of storage elements and computational resources are combined to generate a grid which gives us shared access to extra computing power. In particular, data grid deals with data intensive applications and provides intensive resources across widely distributed communities. Data replication is an efficient way for distributing replicas among the data grids, making it possible to access similar data in different locations of the data grid. Replication reduces data access time and improves the performance of the system. In this paper, we propose a new dynamic data replication algorithm named PDDRA that optimizes the traditional algorithms. Our proposed algorithm is based on an assumption: members in a VO (Virtual Organization) have similar interests in files. Based on this assumption and also file access history, PDDRA predicts future needs of grid sites and pre-fetches a sequence of files to the requester grid site, so the next time that this site needs a file, it will be locally available. This will considerably reduce access latency, response time and bandwidth consumption. PDDRA consists of three phases: storing file access patterns, requesting a file and performing replication and pre-fetching and replacement. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid projects. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, effective network usage, total number of replications, hit ratio and percentage of storage filled.     

Bacteria foraging optimization for protein sequence analysis on the grid

Scientific applications such as protein sequence analysis require a coordination of resources. This is due to hundreds and hundreds of protein sequences being deposited into data banks by the research community which results in an extensive database search when one wants to find a similar protein sequence. This search becomes easier and the time taken is reduced when it is conducted in a grid environment implemented using the Globus tool kit. This paper proposes the use of Bacteria Foraging Optimization (BFO) for finding similar protein sequences in the existing databases. Usage of BFO further reduces the time taken by a resource to execute the user’s requests. Also, the resources utilized in the proposed method are better balanced compared to the existing scheduling algorithms. Also, it is found that the number of tasks executed is more compared to the existing algorithms even though there is a fall in the execution of tasks as the number of resources increases which might be due to network failure etc. The proposed BFO has been compared with the existing First Come First Serve (FCFS) and Minimum Execution Time (MET) scheduling algorithms and it has been found that the proposed BFO performs well compared to the existing algorithms in terms of makespan, resource utilization and minimization in the case of non-execution of client requests.     

A structured marketplace for arbitrary services

Creating simple marketplaces with common rules, that enable the dynamic selection and consumption of functionality, is the missing link to allow small businesses to enter the cloud, not only as consumers, but also as vendors. In this paper, we present the concepts behind a hybrid service and process repository that can act as the foundation for such a marketplace, as well as a prototype that allowed us to test various real-world scenarios. The advantage of a hybrid service and process repository is that, it not only holds a flat list of services, but also exposes a generic set of use cases, that it obtains information on how specific services can be used to implement the use cases as well as information to select services at run-time according to customer’s goal functions.     

Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods

Due to the large variety in computing resources and, consequently, the large number of different types of service level agreements (SLAs), computing resource markets face the problem of a low market liquidity. Restricting the number of different resource types to a small set of standardized computing resources seems to be the appropriate solution to counteract this problem. Standardized computing resources are defined through an SLA template. An SLA template defines the structure of an SLA, the service attributes, the names of the service attributes, and the service attribute values. However, since existing research results have only introduced static SLA templates so far, the SLA templates cannot reflect changes in user needs and market structures. To address this shortcoming, we present a novel approach of adaptive SLA matching. This approach adapts SLA templates based on SLA mappings of users. It allows Cloud users to define mappings between a public SLA template, which is available in the Cloud market, and their private SLA templates, which are used for various in-house business processes of the Cloud user. Besides showing how public SLA templates are adapted to the demand of Cloud users, we also analyze the costs and benefits of this approach. Costs are incurred every time a user has to define a new SLA mapping to a public SLA template due to its adaptation. In particular, we investigate how the costs differ with respect to the public SLA template adaptation method. The simulation results show that the use of heuristics within adaptation methods allows balancing the costs and benefits of the SLA mapping approach.     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

Fighting identity theft: The coping perspective

The proliferation of the online business transaction has led to a large number of incidents of identity theft, which have incurred expensive costs to consumers and e-commerce industries. Fighting identity theft is important for both online business and consumers. Although the practical significance of fighting identity theft has been of great interest, empirical studies on identity theft are very limited. Drawing upon coping behavior theories, this study examines two types of coping behaviors to fight identity theft (i.e., conventional coping and technological coping). Following structural equation modeling approach, we test the model using data collected from 117 subjects through a survey. The results reveal that both conventional coping and technological coping are effective to defend against identity theft. Technological coping is determined by an individual's conventional coping, self-efficacy, perceived effectiveness of coping, and social influence. This study presents a timely empirical study on identity theft, and provides valuable insights for consumers, government agencies, and e-commerce industries.     

udpSkeduler: A Web architecture based decision support system for course and classroom scheduling

A key process for post-secondary educational institutions is the definition of course timetables and classroom assignments. Manual scheduling methods require enormous amounts of time and resources to deliver results of questionable quality, and multiple course and classroom conflicts usually occur. This article presents a scheduling system implemented in a Web environment. This system generates optimal schedules via an integer-programming model. Among its functionalities, this system enables direct interaction with instructors in order to gather data on their time availability for teaching courses. The results demonstrate that significant improvements over the typical fully manual process were obtained.     

A consistency and consensus based decision support model for group decision making with multiplicative preference relations

In group decision making (GDM) with multiplicative preference relations (also known as pairwise comparison matrices in the Analytical Hierarchy Process), to come to a meaningful and reliable solution, it is preferable to consider individual consistency and group consensus in the decision process. This paper provides a decision support model to aid the group consensus process while keeping an acceptable individual consistency for each decision maker. The concept of an individual consistency index and a group consensus index is introduced based on the Hadamard product of two matrices. Two algorithms are presented in the designed support model. The first algorithm is utilized to convert an unacceptable preference relation to an acceptable one. The second algorithm is designed to assist the group in achieving a predefined consensus level. The main characteristics of our model are that: (1) it is independent of the prioritization method used in the consensus process; (2) it ensures that each individual multiplicative preference relation is of acceptable consistency when the predefined consensus level is achieved. Finally, some numerical examples are given to verify the effectiveness of our model.     

Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.     

Distributed computing for carbon footprint reduction by exploiting low-footprint energy availability

Low carbon footprint energy sources such as solar and wind power typically suffer from unpredictable or limited availability. By globally distributing a number of these renewable sources, these effects can largely be compensated for. We look at the feasibility of this approach for powering already distributed data centers in order to operate at a reduced total carbon footprint. From our study we show that carbon footprint reductions are possible, but that these are highly dependent on the approach and parameters involved. Especially the manufacturing footprint and the geographical region are critical parameters to consider. Deploying additional data centers can help in reducing the total carbon footprint, but substantial reductions can be achieved when data centers with nominal capacity well below maximum capacity redistribute processing to sites based on renewable energy availability.