Thar Baker

GreeDi: An energy efficient routing algorithm for big data on cloud

Abstract The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology.

An energy-aware service composition algorithm for multiple cloud-based IoT applications

There has been a shift in research towards the convergence of the Internet-of-Things (IoT) and cloud computing paradigms motivated by the need for IoT applications to leverage the unique characteristics of the cloud. IoT acts as an enabler to interconnect intelligent and self-configurable nodes things to establish an efficient and dynamic platform for communication and collaboration. IoT is becoming a major source of big data, contributing huge amounts of streamed information from a large number of interconnected nodes, which have to be stored, processed, and presented in an efficient, and easily interpretable form. Cloud computing can enable IoT to have the privilege of a virtual resources utilization infrastructure, which integrates storage devices, visualization platforms, resource monitoring, analytical tools, and client delivery. Given the number of things connected and the amount of data generated, a key challenge is the energy efficient composition and interoperability of heterogeneous things integrated with cloud resources and scattered across the globe, in order to create an on-demand energy efficient cloud based IoT application. In many cases, when a single service is not enough to complete the business requirement; a composition of web services is carried out. These composed web services are expected to collaborate towards a common goal with large amount of data exchange and various other operations. Massive data sets have to be exchanged between several geographically distributed and scattered services. The movement of mass data between services influences the whole application process in terms of energy consumption. One way to significantly reduce this massive data exchange is to use fewer services for a composition, which need to be created to complete a business requirement. Integrating fewer services can result in a reduction in data interchange, which in return helps in reducing the energy consumption and carbon footprint.This paper develops a novel multi-cloud IoT service composition algorithm called (E2C2) that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements. A formal user requirements translation and transformation modelling and analysis is adopted for the proposed algorithm. The algorithm was evaluated against four established service composition algorithms in multiple cloud environments (All clouds, Base cloud, Smart cloud, and COM2), with the results demonstrating the superior performance of our approach.

Energy Efficient Cloud Computing Environment via Autonomic Meta-director Framework

The ever-increasing density in cloud computing users, services, and data centres has led to significant increases in network traffic and the associated energy consumed by its huge infrastructure, e.g. extra servers, switches, routers, which is required to respond quickly and effectively to users requests. Transferring data, via a high bandwidth connection between data centres and cloud users, consumes even larger amounts of energy than just processing and storing the data on a cloud data centre, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. This paper proposes a high-end autonomic meta-director framework to find the most energy efficient route to the green data centre by utilising the linear programming approach. The framework is, first, formalised by the situation calculus, and then evaluated against shortest path algorithm with minimum number of nodes traversed.

Towards Autonomic Cloud Services Engineering via Intention Workflow Model

In recent years, the rise and rapid adoption of cloud computing has acted as a catalyst for research in related fields: virtualization, distributed and service-oriented computing to name but a few. Whilst cloud computing technology is rapidly maturing, many of the associated long-standing socio-technical challenges including the dependability of cloud-based service composition, services manageability and interoperability remain unsolved. These can be argued to slow down the migration of serious business critical applications to the cloud model. This paper reports on progress towards the development of a method to generate cloud-based service compositions from requirements metadata. The paper presents a formal approach that uses Situation Calculus to translate service requirements into an Intention Workflow Model (IWM). This IWM is then used to generate autonomic cloud service composition. The Petshop benchmark is used to illustrate and evaluate the proposed method.

Security-Oriented Cloud Platform for SOA-Based SCADA

During the last 10 years, experts in critical infrastructure security have been increasingly directing their focus and attention to the security of control structures such as Supervisory Control and Data Acquisition (SCADA) systems in the light of the move toward Internet-connected architectures. However, this more open architecture has resulted in an increasing level of risk being faced by these systems, especially as they became offered as services and utilised via Service Oriented Architectures (SOA). For example, the SOA-based SCADA architecture proposed by the AESOP project concentrated on facilitating the integration of SCADA systems with distributed services on the application layer of a cloud network. However, whilst each service specified various security goals, such as authorisation and authentication, the current AESOP model does not attempt to encompass all the necessary security requirements and features of the integrated services. This paper presents a concept for an innovative integrated cloud platform to reinforce the integrity and security of SOA-based SCADA systems that will apply in the context of Critical Infrastructures to identify the core requirements, components and features of these types of system. The paper uses the SmartGrid to highlight the applicability and importance of the proposed platform in a real world scenario.

Security Support for Intention Driven Elastic Cloud Computing

Cloud computing had enabled many companies to drive efficient and better utilization of computational resources, this by ensuring better empowerment of objectives and user policies over these resources. Various security aspects such as privacy, accountability, authentication, auditing, role-based access control and others, depict several desired policies that need to be available in cloud-based applications for two main reasons: (i) to promote secured cloud usability (ii) and to protect users information/application. This paper presents Secured Objective-Driven programming model created automatically at runtime by PAA Cloud Engine along with the XACML security annotation representation. The later provides a secured separated abstraction layer for the cloud users sits on top of the programming model.

Eternal Cloud Computation Application Development

The migration from traditional system designs to full dynamic/elastic cloud systems raises several interesting issues, particularly surrounding the co-ordination and management of the emerging (new) system structures. Existing and emerging Service Oriented Architecture (SOA) web standards and technologies such as WSDL, BPEL and WS-BPEL are generally promoted as facilitating the design of fully adaptive and scalable enterprise, and particularly, cloud applications. However, from the end-user point of view, the functionality to provide and manage these fully dynamic cloud systems is still in its early stages and requires significant efforts to be fully achieved. Adhering strictly to software engineering concepts such as high cohesion and low coupling results in a cloud application architecture that promotes component (i.e. Service) reuse and lends itself to scalability. Equally, late runtime-binding, re-binding and fail over systems clearly highlight the flexibility of these architectures, yet their lack of adaptability is apparent when higher-level runtime alterations would be beneficial. Current efforts towards this goal, such as DADL and DURRA, still require human input at each change in order to facilitate true runtime adaptation, rendering it impractical for all but the most trivial of adaptations. This paper discusses the current cloud architectures shortcomings along with a proposal for a new approach that is compatible with existing SOA methodologies. This approach supports XML abstraction flexibility via a new intermediary Cloud-Intention Layer, providing separation between the cloud application source code and the cloud services themselves.

Programming Support and Governance for Process-Oriented Software Autonomy

Business Process models seek to orchestrate business functions through the development of automated task completion, which is becoming increasingly used for Service-Oriented Architectures. This had led to many advances in the methods and tools available for software and language support in process modelling and enactment. Recent development in Business Process Execution languages, such as WS-BPEL 2.0 has widened the scope of process modelling to encompass cross-enterprise and inter-enterprise processes with a wide spread of often heterogonous business processes together with a range of associated modules for enactment, governance and assurance, to name but a few, to address non-functional requirements. Hence, the task of provisioning and managing such systems far outstrips the capabilities of human operatives, with most adaptations to operational circumstances requiring the system to be taken offline reprogrammed, recompiled and redeployed. This work focuses on the application of recent developments in language support for software autonomy whilst guaranteeing autonomic software behaviour. The issues to be addressed are stated with a supporting framework and language, Neptune. This is illustrated through a representative example with a case study evaluation reported upon.

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active.

Comparison Data Traffic Scheduling Techniques for Classifying QoS over 5G Mobile Networks

Enhancing Quality of Service (QoS) in mobilenetworks is the key aim for mobile operators. Mobile networkstransport several forms of data traffic for real-time applications(i.e., video monitoring). These applications need to get theadvantage of QoS adaptation. Numerous scheduling techniquesare utilized at the router to assure the QoS of the mobilenetworks. Upcoming 5G mobile networks will be launched, hence, Human-Type-Communication (HTC) and Machine-to-Machine (M2M) data traffic are expected to increasedramatically over mobile networks, which results in growing thecapacity and raising high data rates. These networks areexpected to face challenges in cases of Radio Access Network(RAN) overload and congestion due to the massive smart devicesdata traffic with various QoS requirements. This paper presentsa comparison for data traffic scheduling techniques, which arePriority Queuing (PQ), First-In-First-Out (FIFO) and WeightedFair Queuing (WFQ). We consider to select a suitable data trafficscheduling technique in terms of QoS provisioning and helping5G network, also we propose models and algorithms forefficiently utilized the smallest unit of a RAN in a relay node byaggregating and slicing the data traffic of several M2M devices.