78 research outputs found
Towards Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems using an AOSE Methodology
Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based SelfManagement. We look at ways to achieve this, and in particular focus on Agent-Oriented Software Engineering. We propose utilizing an AOSE methodology for specifying autonomic and autonomous properties of the system independently, and later, by means of composition of these specifications, to construct a specification for the policy and its subsequent deployment
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
Productive Efficiency of Energy-Aware Data Centers
Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes
and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case.
This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables
data-center managers to detect inefficiencies and to implement further corrective actions.Universidad de Sevilla 2018/0000052
Elaborating a decentralized market information system
A Decentralized Market Information System (DMIS) that aggregates and provides information about markets is an important component for achieving markets in Grid and Peer-to-Peer systems. The proposed work is the development of a framework for the DMIS, which fulfils the economic provision within the main technical requirements like scalability towards nodes and data attributes and robustness against failures. The proposed work also allows obtaining results concerning the trade-off between economic benefits and technical costs. Introducing dynamic adaptive processes promises improvements in efficiency with regards to distributed queries and routing structures. This research proposal presents and discusses the research questions and challenges, the current knowledge and the research methodology proposed for the development of the DMIS framework.Peer Reviewe
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Deux: Autonomic Testing System for Operating System Upgrades
Operating system upgrades and patches sometimes break applications that worked fine on the older version. We present an autonomic approach to testing of OS updates while minimizing downtime, usable without local regression suites or IT expertise. Deux utilizes a dual-layer virtual machine architecture, with lightweight application process checkpoint and resume across OS versions, enabling simultaneous execution of the same applications on both OS versions in different VMs. Inputs provided by ordinary users to the production old version are also fed to the new version. The old OS acts as a pseudo-oracle for the update, and application state is automatically re-cloned to continue testing after any output discrepancies (intercepted at system call level) - all transparently to users. If all differences are deemed inconsequential, then the VM roles are switched with the application state already in place. Our empirical evaluation with both LAMP and standalone applications demonstrates Deux's efficiency and effectiveness
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Auto-scaling to minimize cost and meet application deadlines in cloud workflows"
ABSTRACT A goal in cloud computing is to allocate (and thus pay for) only those cloud resources that are truly needed. To date, cloud practitioners have pursued schedule-based (e.g., time-of-day) and rule-based mechanisms to attempt to automate this matching between computing requirements and computing resources. However, most of these "auto-scaling" mechanisms only support simple resource utilization indicators and do not specifically consider both user performance requirements and budget concerns. In this paper, we present an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost. We accomplish our goal by dynamically allocating/deallocating VMs and scheduling tasks on the most cost-efficient instances. We evaluate our approach in four representative cloud workload patterns and show cost savings from 9.8% to 40.4% compared to other approaches
An adaptive trust based service quality monitoring mechanism for cloud computing
Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This
has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous
improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and
ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing.
The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected
outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and
QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant
in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection
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