Springer Science and Business Media LLC

With the increasing utilization of cloud computing and container technologies, orchestration is becoming an important area on both cloud and container levels. Beyond resource allocation, deployment and configuration, scaling is a key functionality in orchestration in terms of policy, description and flexibility. This paper presents an approach where the aim is to provide a high degree of flexibility in terms of available monitoring metrics and in terms of the definition of elasticity rules to implement practically any possible business logic for a given application. The aim is to provide a general interface for supporting programmable scaling policies utilizing monitoring metrics originating from infrastructure, application or any external components. The paper introduces a component, called Policy Keeper performing the auto-scaling based on user-defined rules, details how this component is operating in the auto-scaling framework, called MiCADO and demonstrates a deadline-based scaling use case.

Internet of Things (IoT) has been widely applied in various domains, e.g. environmental monitoring, intelligent transport system, video surveillance, etc. In most of the IoT applications, the IoT data is generated from a number of data sources, not just only one source. In addition, IoT data has various types with different processing requirements. The high-priority IoT data should have better storage and processing manners than the low-priority IoT data. The objective of this paper is to propose an efficient cloud storage approach for considering the multi-aspect requirements of IoT data. In the approach, a light-weight data structure is used to depict the distribution and calculate the size of each IoT subset (type) in all data sources. Then, we form a number of storage-locality groups from cloud storage blocks. However, the storage-locality groups have different storage sizes and locality capabilities. We would like to place the high-priority IoT subset in the storage-locality group with a strong locality capability. Therefore, there is the placement-combinational problem between IoT subsets and the storage-locality groups. To efficiently solve the IoT placement problem, we propose a bottom-up tree based approach associated with the solution of the well-known combinatorial problem: knapsack. Considering the knapsack problem with the NP-hard computational complexity, we also propose a heuristic placement approach.

Virtualization tools are becoming popular in the context of Grid Computing because they allow running multiple operating systems on a single host and provide a confined execution environment. In several Grid projects, virtualization tools are envisioned to run many virtual machines per host. This immediately raises the issue of virtualization scalability. In this paper, we compare the scalability merits of Four virtualization tools. First, from a simple experiment, we motivate the need for simple microbenchmarks. Second, we present a set of metrics and related methodologies. We propose four microbenchmarks to measure the different scalability parameters for the different machine resources (CPU, memory disk and network) on three scalability metrics (overhead, linearity and isolation). Third, we compare four virtual machine technologies (Vserver, Xen, UML and VMware). The results of this study demonstrate that all the compared tools present different behaviors with respect to scalability, in terms of overhead, resource occupation and isolation. Thus this work will help user to select tools according to their application characteristics.

While new infrastructures for large computational challenges begin to be widely accessible to researchers, computational codes need to be re-designed to exploit new facilities. The Grid and the cloud computing concepts are changing the computational resource distribution and availability, and much effort start to be made to develop new codes for a better exploitation of new resources. This paper presents an example of the use of Grid resources, based on gLite middleware, to run cosmological simulations, that, up to now, are normally executed on Supercomputers. We have also used the Grid to explore and visualize the dataset. We discuss non particular the performance of FLY a parallel code implementing the octal-tree algorithm introduced by J. Barnes and P. Hut to compute the gravitational field efficiently. It simulates the evolution of the collisionless component of the material content of our Universe. FLY was originally developed to run on mainframe systems using the one-side communication paradigm, but we are now presenting a modified version of the computational algorithm to exploit the Grid environment. We also integrated the data exploration and visualization process on the Grid, to obtain preliminary results using the distributed facilities.

Smart cities cannot function without autonomous devices that connect wirelessly and enable cellular connectivity and processing. Edge computing bridges mobile devices and the cloud, giving mobile devices access to computing, memory, and communication capabilities via vehicular ad hoc networks (VANET). VANET is a time-constrained technology that can handle requests from vehicles in a shorter amount of time. The most well-known problems with edge computing and VANET are latency and delay. Any congestion or ineffectiveness in this network can result in latency, which affects its overall efficiency. The data processing in smart city affected by latency can produce irregular decision making. Some data, like traffics, congestions needs to be addressed in time. Delay decision making can make application failure and results in wrong information processing. In this study, we created a probability-based hybrid Whale -Dragonfly Optimization (p–H-WDFOA) edge computing model for smart urban vehicle transportation that lowers the delay and latency of edge computing to address such issues. The 5G localized Multi-Access Edge Computing (MEC) servers were additionally employed, significantly reducing the wait and the latency to enhance the edge technology resources and meet the latency and Quality of Service (QoS) criteria. Compared to an experiment employing a pure cloud computing architecture, we reduced data latency by 20%. We also reduced processing time by 35% compared to cloud computing architecture. The proposed method, WDFO-VANET, improves energy consumption and minimizes the communication costs of VANET.

The workflow paradigm has become the standard to represent processes and their execution flows. With the evolution of e-Science, workflows are becoming larger and more computational demanding. Such e-Science necessities match with what computational Grids have to offer. Grids are shared distributed platforms which will eventually receive multiple requisitions to execute workflows. With this, there is a demand for a scheduler which deals with multiple workflows in the same set of resources, thus the development of multiple workflow scheduling algorithms is necessary. In this paper we describe four different initial strategies for scheduling multiple workflows on Grids and evaluate them in terms of schedule length and fairness. We present results for the initial schedule and for the makespan after the execution with external load. From the results we conclude that interleaving the workflows on the Grid leads to good average makespan and provides fairness when multiple workflows share the same set of resources.

With the increasing demand for dynamic and customised resource provisioning for computational experiments in e-Science, solutions are required to mediate different participants’ varied demands for such resource provision. This paper presents a novel negotiation protocol based on a new collaboration model. The protocol allows e-Scientists, the manager of an e-Scientist’s collaboration, and resource providers to reach resource provisioning agreements. By considering the manager of an e-Scientist collaboration for negotiation decisions, the protocol enables fine-grained accountable resource provision on a per job basis for e-Scientist collaborations, without binding the e-Scientist collaboration to resource providers. A testbed built with the protocol is also presented, making use of a production e-Science gateway, use cases, and infrastructures. The testbed is experimentally evaluated, via designed scenarios and comparison with existing production tools. It demonstrates that the proposed negotiation protocol can facilitate accountable resource provision per job, based on resource sharing rules defined and managed by e-Scientist collaborations.

The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.

The human-machine interface (HMI) collects electrophysiology signals incoming from the patient and utilizes them to operate the device. However, most applications are currently in the testing phase and are typically unavailable to everyone. Developing wearable HMI devices that are intelligent and more comfortable has been a focus of study in recent times. This work developed a portable, eight-channel electromyography (EMG) signal-based device that can distinguish 21 different types of motion. To identify the EMG signals, an analog front-end (AFE) integrated chip (IC) was created, and an integrated EMG signal acquisition device combining a stretchy wristband was made. Using the EMG movement signals of 10 volunteers, a SIAT database of 21 gestures was created. Using the SIAT dataset, a lightweight 2D CNN-LSTM model was developed and specialized training was given. The signal recognition accuracy is 96.4%, and the training process took a median of 14 min 13 s. The model may be used on lower-performance edge computing devices because of its compact size, and it is anticipated that it will eventually be applied to smartphone terminals.

In this paper, we describe the graphical abstractions for Grids and services that have been implemented within the Triana problem solving environment. We provide an overview of the ways in which Triana interacts with services (e.g., Web and P2P services) and then how we interact with core Grid components, such as resource managers and data management systems through the extensive use of the GridLab GAT interface. We describe in detail the GAT philosophy and implementation and then show how the various GAT primitives can be represented in an intuitive fashion within a Triana workflow. This approach, which we refer to as the Visual GAT, differs substantially from other approaches because we do not tie our implementation to any specific underlying Grid middleware technologies; rather, we base our implementation on application level requirements and model such primitives from a user’s perspective by hiding as much complexity as possible without undermining the core capabilities required. We provide a use case to demonstrate the Visual GAT implementation and show how legacy applications can seamlessly be distributed and integrated in a dynamic fashion within complex data-driven workflow scenarios.