e-Science: the added value for modern discovery

e-Science, a new approach based on computer simulation, is increasingly being adopted as one of the most successful modern methods for experimental scientific discovery

Security as a service in smart clouds: opportunities and concerns

The cloud computing paradigm emerged shortly after the introduction of the "invisible" grid concepts but it has taken only a few years for cloud computing to gain enormous momentum within industry and academia alike. However, providing adequate security support by those complex distributed systems is of primary importance for the wide adoption of cloud computing by the end users. This paper gives an overview of the main cloud security issues and challenges. Existing and proposed solutions are also presented with particular attention to the security as a service approach. Some of the available directions for future work are also discussed

Integrated framework for development and execution of component-based Grid applications

Component-based software technologies have emerged as a modern approach to software development for distributed and grid applications. However, the lack of longer-term experience and the complexity of the target systems demand more research results in the field. This paper provides an overview of three different approaches to developing component- based grid applications. In order to re-use legacy codes, the wrapper software approach can be adopted in its two flavours - hand-written or automatically generated wrapper code. Another approach applicable to existing object-oriented software is to componentise the code by introducing appropriate modifications. The third approach is component-oriented development from scratch. We compare and contrast the three approaches and highlight their advantages and weaknesses

Insights Into the Origins of the IEEE Computer Society and the Invention of Electronic Digital Computing

Using some nearly forgotten facts, this article reviews the origins of the IEEE Computer Society and the first Technical Committee on Electronic Computers, established about 10 years after the revolutionary invention of electronic digital computing

Proactive Energy-Efficiency: Evaluation of Duty-Cycled MAC Protocols in Wireless Sensor Networks

Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty-cycled protocols – Sensor-MAC (SMAC), Timeout-MAC (TMAC) and TunableMAC. Although other duty-cycled protocols are reviewed, the aforementioned three protocols are observed in OMNET++ simulator via the Castalia framework. Graphical results are produced which show the energy consumption and throughput as the duty cycle is varied and the variations in results for each of the three protocols are analyzed. The results provide insight into how to ensure ‘proactive energy-efficiency’ whereby the impact of denial-of-sleep attacks can be minimized while throughput is maximized

Context-Aware Framework for Performance Tuning via Multi-action Evaluation

Context-aware systems perform adaptive changes in several ways. One way is for the system developers to encompass all possible context changes in a context-aware application and embed them into the system. However, this may not suit situations where the system encounters unknown contexts. In such cases, system inferences and adaptive learning are used whereby the system executes one action and evaluates the outcome to self-adapts/self-learns based on that. Unfortunately, this iterative approach is time-consuming if high number of actions needs to be evaluated. By contrast, our framework for context-aware systems finds the best action for unknown context through concurrent multi-action evaluation and self-adaptation which reduces significantly the evolution time in comparison to the iterative approach. In our implementation we show how the context-aware multi-action system can be used for a context-aware evaluation for database performance tuning

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms

A metadata extracting tool for software components in grid applications

Component-based programming aims at producing higher quality software, increasing the reuse of components and permitting late composition. In the context of component-based programming, applications are treated as composition of components. Given an application composition, some of the components might have been developed outside the context of the application or its domain. As a result, the overall efficiency of the composition, in terms of cost and performance, becomes non-deterministic - may not be guaranteed to be efficient enough, even if the individual components have been proven to be efficient. In other words, two primary goals of software practice, efficiency and quality, do conflict with each other. In this paper, we argue that, this problem can partly be overcome by paying more attention to component-specific information, component metadata, during composition. We describe a possible means of extracting and organising the metadata and formats for specifying the metadata. Our scheme is independent of component- and programming-models and extensible. We see our work as a precursor to a possible runtime scheme, where we intend to facilitate extraction, maintenance and usage of component metadata at runtime

System evolution for unknown context through multi-action evaluation

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular context change. One way is for the system developers to encompass all possible context changes in the domain knowledge. Then, the system matches a context change to that in the domain knowledge and chooses the corresponding action. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, there are situations where a system encounters unknown contexts. In such cases, instead of one action being implemented and evaluated, multiple actions could be implemented concurrently. This parallel evaluation of actions could quicken the evolution time taken to select the best action suited to unknown context compared to the iterative approach. This paper proposes a framework for context-aware systems that finds the best action for unknown context through multi-action evaluation and self-adaptation. In a case study, we show how our multi-action evaluation system can be implemented for a hypothetical hotelier who uses the name-your-own-price mechanism to sell his perishable inventory

Workload Schedulers - Genesis, Algorithms and Comparisons

In this article we provide brief descriptions of three classes of schedulers: Operating Systems Process Schedulers, Cluster Systems, Jobs Schedulers and Big Data Schedulers. We describe their evolution from early adoptions to modern implementations, considering both the use and features of algorithms. In summary, we discuss differences between all presented classes of schedulers and discuss their chronological development. In conclusion, we highlight similarities in the focus of scheduling strategies design, applicable to both local and distributed systems