482 research outputs found
DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling
The dynamic provisioning of virtualized resources offered by cloud computing
infrastructures allows applications deployed in a cloud environment to
automatically increase and decrease the amount of used resources. This
capability is called auto-scaling and its main purpose is to automatically
adjust the scale of the system that is running the application to satisfy the
varying workload with minimum resource utilization. The need for auto-scaling
is particularly important during workload peaks, in which applications may need
to scale up to extremely large-scale systems.
Both the research community and the main cloud providers have already
developed auto-scaling solutions. However, most research solutions are
centralized and not suitable for managing large-scale systems, moreover cloud
providers' solutions are bound to the limitations of a specific provider in
terms of resource prices, availability, reliability, and connectivity.
In this paper we propose DEPAS, a decentralized probabilistic auto-scaling
algorithm integrated into a P2P architecture that is cloud provider
independent, thus allowing the auto-scaling of services over multiple cloud
infrastructures at the same time. Our simulations, which are based on real
service traces, show that our approach is capable of: (i) keeping the overall
utilization of all the instantiated cloud resources in a target range, (ii)
maintaining service response times close to the ones obtained using optimal
centralized auto-scaling approaches.Comment: Submitted to Springer Computin
Self-management for large-scale distributed systems
Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management.
In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving
self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research
on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying
the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed
by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic
managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic
and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a
management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by
presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control
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
Analysis of current middleware used in peer-to-peer and grid implementations for enhancement by catallactic mechanisms
This deliverable describes the work done in task 3.1, Middleware analysis: Analysis of current middleware used in peer-to-peer and grid implementations for enhancement by catallactic mechanisms from work package 3, Middleware Implementation. The document is divided in four parts: The introduction with application scenarios and middleware requirements, Catnets middleware architecture, evaluation of existing middleware toolkits, and conclusions. -- Die Arbeit definiert Anforderungen an Grid und Peer-to-Peer Middleware Architekturen und analysiert diese auf ihre Eignung fĂĽr die prototypische Umsetzung der Katallaxie. Eine Middleware-Architektur fĂĽr die Umsetzung der Katallaxie in Application Layer Netzwerken wird vorgestellt.Grid Computing
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