Scheduling Real-Time Jobs in Distributed Systems - Simulation and Performance Analysis

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.One of the major challenges in ultrascale systems is the effective scheduling of complex jobs within strict timing constraints. The distributed and heterogeneous system resources constitute another critical issue that must be addressed by the employed scheduling strategy. In this paper, we investigate by simulation the performance of various policies for the scheduling of real-time directed acyclic graphs in a heterogeneous distributed environment. We apply bin packing techniques during the processor selection phase of the scheduling process, in order to utilize schedule gaps and thus enhance existing list scheduling methods. The simulation results show that the proposed policies outperform all of the other examined algorithms.The work presented in this paper has been partially supported by EU under the COST program Action IC1305, “Network for Sustainable Ultrascale Computing (NESUS)”

A Gossip-based optimistic replication for efficient delay-sensitive streaming using an interactive middleware support system

While sharing resources the efficiency is substantially degraded as a result of the scarceness of availability of the requested resources in a multiclient support manner. These resources are often aggravated by many factors like the temporal constraints for availability or node flooding by the requested replicated file chunks. Thus replicated file chunks should be efficiently disseminated in order to enable resource availability on-demand by the mobile users. This work considers a cross layered middleware support system for efficient delay-sensitive streaming by using each device's connectivity and social interactions in a cross layered manner. The collaborative streaming is achieved through the epidemically replicated file chunk policy which uses a transition-based approach of a chained model of an infectious disease with susceptible, infected, recovered and death states. The Gossip-based stateful model enforces the mobile nodes whether to host a file chunk or not or, when no longer a chunk is needed, to purge it. The proposed model is thoroughly evaluated through experimental simulation taking measures for the effective throughput Eff as a function of the packet loss parameter in contrast with the effectiveness of the replication Gossip-based policy.Comment: IEEE Systems Journal 201

Performance Evaluation of a Resource Discovery Scheme in a Grid Environment Prone to Resource Failures

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Different aspects of workflow scheduling in large-scale distributed systems

As large-scale distributed systems gain momentum, the scheduling of workflow applications with multiple requirements in such computing platforms has become a crucial area of research. In this paper, we investigate the workflow scheduling problem in large-scale distributed systems, from the Quality of Service (QoS) and data locality perspectives. We present a scheduling approach, considering two models of synchronization for the tasks in a workflow application: (a) communication through the network and (b) communication through temporary files. Specifically, we investigate via simulation the performance of a heterogeneous distributed system, where multiple soft real-time workflow applications arrive dynamically. The applications are scheduled under various tardiness bounds, taking into account the communication cost in the first case study and the I/O cost and data locality in the second.The work presented in this paper has been partially supported by EU, under the COST program Action IC1305, “Network for Sustainable Ultrascale Computing (NESUS)”, and by the Ministerio de Economía y Competitividad, Spain, under the project TIN2013-41350-P, “Scalable Data Management Techniques for High-End Computing Systems”

Exascale machines require new programming paradigms and runtimes

Extreme scale parallel computing systems will have tens of thousands of optionally accelerator-equiped nodes with hundreds of cores each, as well as deep memory hierarchies and complex interconnect topologies. Such Exascale systems will provide hardware parallelism at multiple levels and will be energy constrained. Their extreme scale and the rapidly deteriorating reliablity of their hardware components means that Exascale systems will exhibit low mean-time-between-failure values. Furthermore, existing programming models already require heroic programming and optimisation efforts to achieve high efficiency on current supercomputers. Invariably, these efforts are platform-specific and non-portable. In this paper we will explore the shortcomings of existing programming models and runtime systems for large scale computing systems. We then propose and discuss important features of programming paradigms and runtime system to deal with large scale computing systems with a special focus on data-intensive applications and resilience. Finally, we also discuss code sustainability issues and propose several software metrics that are of paramount importance for code development for large scale computing systems