4,129 research outputs found
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
OpenPING: A Reflective Middleware for the Construction of Adaptive Networked Game Applications
The emergence of distributed Virtual Reality (VR) applications
that run over the Internet has presented networked game
application designers with new challenges. In an environment
where the public internet streams multimedia data and is
constantly under pressure to deliver over widely heterogeneous
user-platforms, there has been a growing need that distributed VR
applications be aware of and adapt to frequent variations in their
context of execution. In this paper, we argue that in contrast to
research efforts targeted at improvement of scalability, persistence
and responsiveness capabilities, much less attempts have been
aimed at addressing the flexibility, maintainability and
extensibility requirements in contemporary distributed VR
platforms. We propose the use of structural reflection as an
approach that not only addresses these requirements but also
offers added value in the form of providing a framework for
scalability, persistence and responsiveness that is itself flexible,
maintainable and extensible. We also present an adaptive
middleware platform implementation called OpenPING1 that
supports our proposal in addressing these requirements
A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Nowadays many companies have available large amounts of raw, unstructured
data. Among Big Data enabling technologies, a central place is held by the
MapReduce framework and, in particular, by its open source implementation,
Apache Hadoop. For cost effectiveness considerations, a common approach entails
sharing server clusters among multiple users. The underlying infrastructure
should provide every user with a fair share of computational resources,
ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
this paper we consider two mathematical programming problems that model the
optimal allocation of computational resources in a Hadoop 2.x cluster with the
aim to develop new capacity allocation techniques that guarantee better
performance in shared data centers. Our goal is to get a substantial reduction
of power consumption while respecting the deadlines stated in the SLAs and
avoiding penalties associated with job rejections. The core of this approach is
a distributed algorithm for runtime capacity allocation, based on Game Theory
models and techniques, that mimics the MapReduce dynamics by means of
interacting players, namely the central Resource Manager and Class Managers
On Consistency and Network Latency in Distributed Interactive Applications: A Survey—Part I
This paper is the first part of a two-part paper that documents a detailed survey
of the research carried out on consistency and latency in distributed interactive applications
(DIAs) in recent decades. Part I reviews the terminology associated with DIAs and offers
definitions for consistency and latency. Related issues such as jitter and fidelity are also
discussed. Furthermore, the various consistency maintenance mechanisms that researchers
have used to improve consistency and reduce latency effects are considered. These
mechanisms are grouped into one of three categories, namely time management,
Information management and system architectural management. This paper presents the
techniques associated with the time management category. Examples of such mechanisms
include time warp, lock step synchronisation and predictive time management. The
remaining two categories are presented in part two of the survey
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Analysis, classification and comparison of scheduling techniques for software transactional memories
Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches
Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed
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