3 research outputs found
HPS-HDS:High Performance Scheduling for Heterogeneous Distributed Systems
Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, data-aware service allocation, Big Data management and processing, performance analysis and optimization
On Packet Scheduling with Adversarial Jamming and Speedup
In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes
arrive over time to be transmitted over a channel in which instantaneous
jamming errors occur at times chosen by the adversary and not known to the
algorithm. The transmission taking place at the time of jamming is corrupt, and
the algorithm learns this fact immediately. An online algorithm maximizes the
total size of packets it successfully transmits and the goal is to develop an
algorithm with the lowest possible asymptotic competitive ratio, where the
additive constant may depend on packet sizes.
Our main contribution is a universal algorithm that works for any speedup and
packet sizes and, unlike previous algorithms for the problem, it does not need
to know these properties in advance. We show that this algorithm guarantees
1-competitiveness with speedup 4, making it the first known algorithm to
maintain 1-competitiveness with a moderate speedup in the general setting of
arbitrary packet sizes. We also prove a lower bound of on
the speedup of any 1-competitive deterministic algorithm, showing that our
algorithm is close to the optimum.
Additionally, we formulate a general framework for analyzing our algorithm
locally and use it to show upper bounds on its competitive ratio for speedups
in and for several special cases, recovering some previously known
results, each of which had a dedicated proof. In particular, our algorithm is
3-competitive without speedup, matching both the (worst-case) performance of
the algorithm by Jurdzinski et al. and the lower bound by Anta et al.Comment: Appeared in Proc. of the 15th Workshop on Approximation and Online
Algorithms (WAOA 2017