GUISET: A CONCEPTUAL DESIGN OF A GRID-ENABLED PORTAL FOR E-COMMERCE ON-DEMAND SERVICES

Conventional grid-enabled portal designs have been largely influenced by the usual functional requirements such as security requirements, grid resource requirements and job management requirements. However, the pay-as-you-use service provisioning model of utility computing platforms mean that additional requirements must be considered in order to realize effective grid-enabled portals design for such platforms. This work investigates those relevant additional requirements that must be considered for the design of grid-enabled portals for utility computing contexts. Based on a thorough review of literature, we identified a number of those relevant additional requirements, and developed a grid-enabled portal prototype for the Grid-based Utility Infrastructure for SMME-enabling Technology (GUISET) initiative – a utility computing platform. The GUISET portal was designed to cater for both the traditional grid requirements and some of the relevant additional requirements for utility computing contexts. The result of the evaluation of the GUISET portal prototype using a set of benchmark requirements (standards) revealed that it fulfilled the minimum requirements to be suitable for the utility context

Speeding up systems biology simulations of biochemical pathways using condor

This is the accepted version of the following article: Speeding up Systems Biology Simulations of Biochemical Pathways using Condor". Concurrency and Computation: Practice and Experience Volume 26, Issue 17, pages 2727–2742, 10 December 2014 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cpe.3161/abstractSystems biology is a scientific field that uses computational modelling to study biological and biochemical systems. The simulation and analysis of models of these systems typically explore behaviour over a wide range of parameter values; as such, they are usually characterised by the need for nontrivial amounts of computing power. Grid computing provides access to such computational resources. In previous research, we created the grid-enabled biochemical networks simulation environment to attempt to speed up system biology simulations over a grid (the UK National Grid Service and ScotGrid). Following on from this work, we have created the simulation modelling of the epidermal growth factor receptor microtubule-associated protein kinase pathway utility, a standalone simulation tool dedicated to the modelling and analysis of the epidermal growth factor receptor microtubule-associated protein kinase pathway. This builds on experiences from biochemical networks simulation environment by decoupling the simulation modelling elements from the Grid middleware. This new utility enables us to interface with different grid technologies. This paper therefore describes the new SIMAP utility and an empirical investigation of its performance when deployed over a desktop grid based on the high throughput computing middleware Condor. We present our results based on a case study with a model of the mammalian ErbB signalling pathway, a pathway strongly linked to cance

A non-additive negotiation model for utility computing markets

Market-based resource allocation is a promising model for dealing with the growing Utility Computing environments, such as Grid or Cloud Computing. Agents that represent both service clients and providers meet in a market to negotiate the terms of the sale of resources. Additive negotiation models are extended because they are simple, but they are not valid for negotiations whose terms are not independent between them. This paper proposes a simple non-additive model for performing negotiations and demonstrates its validity through simulation experiments.Postprint (published version

Service Quality and Profit Control in Utility Computing Service Life Cycles

Utility Computing is one of the most discussed business models in the context of Cloud Computing. Service providers are more and more pushed into the role of utilities by their customer's expectations. Subsequently, the demand for predictable service availability and pay-per-use pricing models increases. Furthermore, for providers, a new opportunity to optimise resource usage offers arises, resulting from new virtualisation techniques. In this context, the control of service quality and profit depends on a deep understanding of the representation of the relationship between business and technique. This research analyses the relationship between the business model of Utility Computing and Service-oriented Computing architectures hosted in Cloud environments. The relations are clarified in detail for the entire service life cycle and throughout all architectural layers. Based on the elaborated relations, an approach to a delivery framework is evolved, in order to enable the optimisation of the relation attributes, while the service implementation passes through business planning, development, and operations. Related work from academic literature does not cover the collected requirements on service offers in this context. This finding is revealed by a critical review of approaches in the fields of Cloud Computing, Grid Computing, and Application Clusters. The related work is analysed regarding appropriate provision architectures and quality assurance approaches. The main concepts of the delivery framework are evaluated based on a simulation model. To demonstrate the ability of the framework to model complex pay-per-use service cascades in Cloud environments, several experiments have been conducted. First outcomes proof that the contributions of this research undoubtedly enable the optimisation of service quality and profit in Cloud-based Service-oriented Computing architectures

Automated Negotiations under User Preference Uncertainty: A Linear Programming Approach

Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback

RSCCGA: Resource Scheduling for Cloud Computing by Genetic Algorithm

Cloud computing, also known as on-the-line computing, is a kind of Internet-based computing that provides shared processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources, which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in third-party data centers. It relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over a network. the scheduling problem is an important issue in the management of resources in the cloud, because despite many requests the data center there is the possibility of scheduling manually. Therefore, the scheduling algorithms play an important role in cloud computing, because the goal of scheduling is to reduce response times and improve resource utilization. The computing resources, either software or hardware, are virtualized and allocated as services from providers to users. The computing resources can be allocated dynamically upon the requirements and preferences of consumers. Traditional system-centric resource management architecture cannot process the resource assignment task and dynamically allocate the available resources in a cloud computing environment. This paper proposed a resource scheduling model for cloud computing based on the genetic algorithm. Experiments show that proposed method has more performance than other methods.Keywords: Cloud Computing, Resource Management, Scheduling, Bandwidth Consumption, Waiting Time, Genetic algorith

Decentralized vs. centralized economic coordination of resource allocation in grids

Application layer networks are software architectures that allow the provisioning of services requiring a huge amount of resources by connecting large numbers of individual computers, like in Grid or Peer-to-Peer computing. Controlling the resource allocation in those networks is nearly impossible using a centralized arbitrator. The network simulation project CATNET will evaluate a decentralized mechanism for resource allocation, which is based on the economic paradigm of the Catallaxy, against a centralized mechanism using an arbitrator object. In both versions, software agents buy and sell network services and resources to and from each other. The economic model is based on self-interested maximization of utility and self-interested cooperation between agents. This article describes the setup of money and message flows both for centralized and decentralized coordination in comparison.Peer Reviewe