CLOSURE: A cloud scientific workflow scheduling algorithm based on attack-defense game model

The multi-tenant coexistence service mode makes the cloud-based scientific workflow encounter the risks of being intruded. For this problem, we propose a CLoud scientific wOrkflow SchedUling algoRithm based on attack-defensE game model (CLOSURE). In the algorithm, attacks based on different operating system vulnerabilities are regarded as different “attack” strategies; and different operating system distributions in a virtual machine cluster executing the workflows are regarded as different “defense” strategies. The information of the attacker and defender is not balanced. In other words, the defender cannot obtain the information about the attacker’s strategies, while the attacker can acquire information about the defender’s strategies through a network scan. Therefore, we propose to dynamically switch the defense strategies during the workflow execution, which can weaken the network scan effects and transform the workflow security problem into an attack-defense game problem. Then, the probability distribution of the optimal mixed defense strategies can be achieved by calculating the Nash Equilibrium in the attack-defense game model. Based on this probability, diverse VMs are provisioned for workflow execution. Furthermore, a task-VM mapping algorithm based on dynamic Heterogeneous Earliest Finish Time (HEFT) is presented to accelerate the defense strategy switching and improve workflow efficiency. The experiments are conducted on both simulation and actual environment, experimental results demonstrate that compared with other algorithms, the proposed algorithm can reduce the attacker’s benefits by around 15.23%, and decrease the time costs of the algorithm by around 7.86%

Towards mobile cloud computing with single sign-on access

This is a post-peer-review, pre-copyedit version of an article published in Journal of Grid Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10723-017-9413-3The low computing power of mobile devices impedes the development of mobile applications with a heavy computing load. Mobile Cloud Computing (MCC) has emerged as the solution to this by connecting mobile devices with the “infinite” computing power of the Cloud. As mobile devices typically communicate over untrusted networks, it becomes necessary to secure the communications to avoid privacy-sensitive data breaches. This paper presents work on implementing MCC applications with secure communications. For that purpose, we built on COMPSs-Mobile, a redesigned implementation of the COMP Superscalar (COMPSs) framework aiming to MCC platorms. COMPSs-Mobile automatically exploits the parallelism inherent in an application and orchestrates its execution on loosely-coupled distributed environment. To avoid a vendor lock-in, this extension leverages on the Generic Security Services Application Program Interface (GSSAPI) (RFC2743) as a generic way to access security services to provide communications with authentication, secrecy and integrity. Besides, GSSAPI allows applications to take profit of more advanced features, such as Federated Identity or Single Sign-On, which the underlying security framework could provide. To validate the practicality of the proposal, we use Kerberos as the security services provider to implement SSO; however, applications do not authenticate themselves and require users to obtain and place the credentials beforehand. To evaluate the performance, we conducted some tests running an application on a smartphone offloading tasks to a private cloud. Our results show that the overhead of securing the communications is acceptable.This work has been supported by the Spanish Government (contracts TIN2012-34557, TIN2015-65316-P and grants BES-2013-067167, EEBB-I-15-09808 of the Research Training Program and SEV-2011-00067 of Severo Ochoa Program), by Generalitat de Catalunya (contract 2014-SGR-1051) and by the European Commission (ASCETiC project, FP7-ICT-2013.1.2 contract 610874). The second author was partially supported by the European Commission's Horizon2020 programme under grant agreement 653965 (AARC).Peer ReviewedPostprint (author's final draft

Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Resource management for data streaming applications

This dissertation investigates novel middleware mechanisms for building streaming applications. Developing streaming applications is a challenging task because (i) they are continuous in nature; (ii) they require fusion of data coming from multiple sources to derive higher level information; (iii) they require efficient transport of data from/to distributed sources and sinks; (iv) they need access to heterogeneous resources spanning sensor networks and high performance computing; and (v) they are time critical in nature. My thesis is that an intuitive programming abstraction will make it easier to build dynamic, distributed, and ubiquitous data streaming applications. Moreover, such an abstraction will enable an efficient allocation of shared and heterogeneous computational resources thereby making it easier for domain experts to build these applications. In support of the thesis, I present a novel programming abstraction, called DFuse, that makes it easier to develop these applications. A domain expert only needs to specify the input and output connections to fusion channels, and the fusion functions. The subsystems developed in this dissertation take care of instantiating the application, allocating resources for the application (via the scheduling heuristic developed in this dissertation) and dynamically managing the resources (via the dynamic scheduling algorithm presented in this dissertation). Through extensive performance evaluation, I demonstrate that the resources are allocated efficiently to optimize the throughput and latency constraints of an application.Ph.D.Committee Chair: Ramachandran, Umakishore; Committee Member: Chervenak, Ann; Committee Member: Cooper, Brian; Committee Member: Liu, Ling; Committee Member: Schwan, Karste

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Multi-criteria decision support for energy-efficient IoT edge computing offloading

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions (e.g. process- ing and memory). In the past, offloading would always utilise remote cloud infrastruc- tures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network, closer to the IoT devices. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage, based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time conditions of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. This thesis presents a dynamic computation offloading mechanism, which improves the performance (i.e. in terms of response time) and energy consumption of IoT de- vices in a decentralised and autonomous manner. We initially propose the Multi-critEria DecIsion support meChanism for IoT offloading(MEDICI), which runs independently on an IoT device, enabling it to make offloading decisions dynamically, based on multiple criteria, such as the state of the IoT, edge or cloud devices and the conditions of the net- work connecting them. It provides mathematical models of the expected time and energy costs for the different options of offloading a task (i.e. to the edge or the cloud or the IoT device itself). To evaluate its effectiveness, we provide simulation results, by extending the EdgeCloudSim simulator, comparing it against previous families of approaches used in the literature. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, such as IoT cyber intrusion detection, and the energy consumption not only of the individual IoT devices but also of the system as a whole. Furthermore, we present an enhancement of our MEDICI mechanism, the ProbeLess Multi-critEria DecIsion support meChanism for IoT offloading (PL-MEDICI), which en- ables MEDICI to operate in real IoT environments without the need for probing or having pre-defined parameters in order to estimate or model the network conditions or the com- putation capabilities of the different devices involved. This is the first probeless dynamic and decentralised offloading decision support mechanism for IoT environments. The probeless property is achieved by combining lightweight statistical techniques with the concept of age of knowledge (AoK) to allow us to have accurate enough information to use for our estimations. We provide experimental results performed in a real IoT testbed with three real IoT applications, showcasing that PL-MEDICI outperforms existing techniques in terms of both response time and energy consumption. Finally, in order to further evaluate our PL-MEDICI mechanism, we formulate a mixed- integer linear program optimisation problem that provides the theoretical optimal cen- tralised solution to our problem. This is used to compare our PL-MEDICI against the theoretical optimum, given the same estimated input. Our results showed that our of- floading mechanism is close to the obtained optimal solution in terms of both the re- sponse time and energy consumptio