An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

A novel datacenter-oriented data placement strategy of scientific workflow in hybrid cloud

Scientific workflow is a complicated data intensive application. How to achieve an effective data placement schema in hybrid cloud environment has become a crucial issue nowadays, especially with the new challenges brought by the security issues. Traditional data placement strategies usually adopt load balancing-based partition model to allocate datasets. Although these data placement schemas can have good performance in load balancing, their data transfer time may not be optimal. In contrast to traditional strategies, this paper focuses on the hybrid cloud environment and proposes a data dependency destruction-based partition model to achieve the minimal data dependency destruction partition. In addition, it presents a novel datacenter-oriented data placement strategy. This strategy allocates high dependency datasets to one datacenter according to the new partition model and thus significantly reduces data transfer time between datacenters. Experimental results show that the proposed strategy can effectively reduce data transfer time during workflow's execution.

Enhanced playback of automated service emulation models using entropy analysis

Service virtualisation is a supporting tool for DevOps to generate interactive service models of dependency systems on which a system-under-test relies. These service models allow applications under development to be continuously tested against production-like conditions. Generating these virtual service models requires expert knowledge of the service protocol, which may not always be available. However, service models may be generated automatically from network traces. Previous work has used the Needleman-Wunsch algorithm to select a response from the service model to play back for a live request. We propose an extension of the Needleman-Wunsch algorithm, which uses entropy analysis to automatically detect the critical matching fields for selecting a response. Empirical tests against four enterprise protocols demonstrate that entropy weighted matching can improve response accuracy.

Internal market orientation and market/oriented behaviours

Purpose – The purpose of this empirical paper is to investigate internal marketing from a behavioural perspective. The impact of internal marketing behaviours, operationalised as an internal market orientation (IMO), on employees' marketing and other in/role behaviours (IRB) were examined. Design/methodology/approach – Survey data measuring IMO, market orientation and a range of constructs relevant to the nomological network in which they are embedded were collected from the UK retail managers. These were tested to establish their psychometric properties and the conceptual model was analysed using structural equations modelling, employing a partial least squares methodology. Findings – IMO has positive consequences for employees' market/oriented and other IRB. These, in turn, influence marketing success. Research limitations/implications – The paper provides empirical support for the long/held assumption that internal and external marketing are related and that organisations should balance their external focus with some attention to employees. Future research could measure the attitudes and behaviours of managers, employees and customers directly and explore the relationships between them. Practical implications – Firm must ensure that they do not put the needs of their employees second to those of managers and shareholders; managers must develop their listening skills and organisations must become more responsive to the needs of their employees. Originality/value – The paper contributes to the scarce body of empirical support for the role of internal marketing in services organisations. For researchers, this paper legitimises the study of internal marketing as a route to external market success; for managers, the study provides quantifiable evidence that focusing on employees' wants and needs impacts their behaviours towards the market. © 2010, Emerald Group Publishing Limited

Code pattern analysis of object-oriented programming languages

Code patterns, including programming patterns and design patterns, are good references for programming language feature improvement and software re-engineering. However, to our knowledge, no existing research has attempted to detect code patterns based on code clone detection technology. In this study, we build upon the previous work and propose to detect and analyze code patterns from a collection of open source projects using NiPAT technology. Because design patterns are most closely associated with object-oriented languages, we choose Java and Python projects to conduct our study. The tool we use for detecting patterns is NiPAT, a pattern detecting tool originally developed for the TXL programming language based on the NiCad clone detector. We extend NiPAT for the Java and Python programming languages. Then, we try to identify all the patterns from the pattern report and classify them into several different categories. In the end of the study, we analyze all the patterns and compare the differences between Java and Python patterns.

Intelligent internet computing: applications and security systems

Intelligent Internet Computing (IIC) is emerging rapidly as an exciting new paradigm including pervasive, grid, and peer-to-peer computing to provide computing and communication services any time and anywhere. IIC paradigm foresees seamless integration of communicating and computational devices and applications embedded in all parts of our environment, from our physical selves, to our homes, our offices, our streets and so on. Although IIC presents exciting enabling opportunities, the benefits will only be realized if application and security issues can be appropriately addressed. This special issue is intended to foster the dissemination of state-of-the-art research in the area of IIC, including novel applications associated with its utilization, security systems and services, security models. We plan to publish high quality manuscripts, which cover the various practical applications and related security theories of IIC. The papers should not be submitted simultaneously for publication elsewhere. Submissions of high quality papers describing mature results or on-going work are invited. Selected high-quality papers from “the Eleventh IEEE International Conference on High Performance Computing and Communications (HPCC-09) and the Third International Conference on Information Security and Assurance (ISA-09),” will be published in this special issue of Journal of Internet Technology on "Intelligent Internet Computing".

Cloud computing adoption by SMEs in Australia.

This study aims to examine the important factors that influence SMEs’ adoption of cloud computing technology. The results showing that SMEs were influenced by factors related to advantaging their organizational capability rather than risk-related factors. The findings are useful to SMEs owners, Cloud service providers and government in establishing Cloud computing adoption strategies for SMEs.

Combating cyber attacks in cloud computing using machine learning techniques.

An extensive investigative survey on Cloud Computing with the main focus on gaps that is slowing down Cloud adoption as well as reviewing the threat remediation challenges. Some experimentally supported thoughts on novel approaches to address some of the widely discussed cyber-attack types using machine learning techniques. The thoughts have been constructed in such a way so that Cloud customers can detect the cyber-attacks in their VM without much help from Cloud service provider

Do we need to handle every temporal violation in scientific workflow systems?

Scientific processes are usually time constrained with overall deadlines and local milestones. In scientific workflow systems, due to the dynamic nature of the underlying computing infrastructures such as grid and cloud, execution delays often take place and result in a large number of temporal violations. Temporal violation handling is to execute violation handling strategies which can compensate for the occurring time deficit but would impose some additional cost. Generally speaking, the two fundamental requirements for delivering satisfactory temporal QoS in scientific workflow systems are temporal conformance and cost effectiveness. Every task for workflow temporal management incurs some cost. Take a single temporal violation handling as an example, its cost can be primarily referred to monetary costs and time overheads of violation handling strategies which are normally nontrivial in scientific workflow systems.