Monitoring large cloud-based systems

Large scale cloud-based services are built upon a multitude of hardware and software resources, disseminated in one or multiple data centers. Controlling and managing these resources requires the integration of several pieces of software that may yield a representative view of the data center status. Today’s both closed and open-source monitoring solutions fail in different ways, including the lack of scalability, scarce representativity of global state conditions, inability in guaranteeing persistence in service delivery, and the impossibility of monitoring multi-tenant applications. In this paper, we present a novel monitoring architecture that addresses the aforementioned issues. It integrates a hierarchical scheme to monitor the resources in a cluster with a distributed hash table (DHT) to broadcast system state information among different monitors. This architecture strives to obtain high scalability, effectiveness and resilience, as well as the possibility of monitoring services spanning across different clusters or even different data centers of the cloud provider. We evaluate the scalability of the proposed architecture through a bottleneck analysis achieved by experimental results

Overview of the software architecture and data flow for the J-PET tomography device

Modern TOF-PET scanner systems require high-speed computing resources for efficient data processing, monitoring and image reconstruction. In this article, we present the data flow and software architecture for the novel TOF-PET scanner developed by the J-PET Collaboration. We discuss the data acquisition system, reconstruction framework and image reconstruction software. Also, the concept of computing outside hospitals in the remote centers such as Świerk Computing Centre in Poland is presented

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Unit Testing of Energy Consumption of Software Libraries

International audienceThe development of energy-efficient software has become a key requirement for a large number of devices, from smartphones to data centers. However, measuring accurately this consumption is a major challenge that state-of-the-art approaches have tried to tackle with a limited success. While monitoring applications' consumption offers a clear insight on where the energy is being spent, it does not help in understanding how the energy is consumed. In this paper, we therefore introduce Jalen Unit, a software framework that infers the energy consumption model of software libraries from execution traces. This model can then be used to diagnose application code for detecting energy bugs, understanding energy distribution, establishing energy profiles and classifications, and comparing software libraries against their energy consumption

A scalable architecture for real-time monitoring of large information systems

Data centers supporting cloud-based services are characterized by a huge number of hardware and software resources often cooperating in complex and unpredictable ways. Understanding the state of these systems for reasons of management and service level agreement requires scalable monitoring architectures that should gather and evaluate continuosly large flows in almost real-time periods. We propose a novel monitoring architecture that, by combining a hierarchical approach with decentralized monitors, addresses these challenges. In this context, fully centralized systems do not scale to the required number of flows, while pure peer-to-peer architectures cannot provide a global view of the system state. We evaluate the monitoring architecture for computational units of gathering and evaluation in real contexts that demonstrate the scalability potential of the proposed system

Towards an Energy-Aware Cloud Architecture for Smart Grids

Energy consumption in Cloud computing is a significant issue in regards to aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Smart grids offers the prospect of dynamic costs for a data center’s energy usage. These dynamic costs can be passed on to Cloud users providing incentives for users to moderate their load while also ensuring the Cloud providers are insulated from fluctuations in the cost of energy. The first step towards this is an architecture that focuses on energy monitoring and usage prediction. We provide such an architecture at both the PaaS and IaaS layers, resulting in energy metrics for applications, VMs and physical hosts, which is key to enabling active demand in cloud data centers. This architecture is demonstrated through our initial results utilising a generic use case, providing energy consumption information at the PaaS and IaaS layers. Such monitoring and prediction provides the groundwork for providers passing on energy consumption costs to end users. It is envisaged that the resulting varying price associated with energy consumption can help motivate the formation of methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint of Cloud applications

Creation and promotion Iranian fisheries research institute laboratories database

Creating a database of affiliated laboratories of the institute is organized with the aim of integrating information related to laboratories of research centers and their subsidiaries. The main objective of conducting this project in this stage is to upgrade it, establish and run one software system based on up-to-date networking technology. For this purpose organizing the centers database, a periodic report on various aspects can be done which help for implementing appropriate monitoring and management. Among the sectors that are designed and upgraded for this system include: Portal, information bank, advanced possibilities for inputting data, searching and reporting on laboratory. The advantages of this precise and updated reports can be collected easily from the general information of research institutes and centers, reports of the number of lab experts with different educational levels in affiliated centers, awareness of numbers and status of the chemical materials in the laboratories of each center, and the significant and important point is about economizing equipment, chemical materials and on time calibration