Load balancing in distributed computing over wireless LAN: effects of network delay

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

Pezoa

et al. 2006

Self Cite

In many distributed computing systems that are prone to either induced or spontaneous node failures, the number of available computing resources is dynamically changing in a random fashion. A load-balancing (LB) policy for such systems should therefore be robust, in terms of workload re-allocation and effectiveness in task completion, with respect to the random absence and re-emergence of nodes as well as random delays in the transfer of workloads among nodes. In this paper two LB policies for such computing environments are presented: The first policy takes an initial LB action to preemptively counteract the consequences of random failure and recovery of nodes. The second policy compensates for the occurrence of node failure dynamically by transferring loads only at the actual failure instants. A probabilistic model, based on the concept of regenerative processes, is presented to assess the overall performance of the system under these policies. Optimal performance of both policies is evaluated using analytical, experimental and simulation-based results. The interplay between node-failure/recovery rates and the mean load-transfer delay are highlighted.

Section: Resultsmentioning

confidence: 99%

Section: Policy Lbp-2mentioning

confidence: 86%

Section: Policy Lbp-2mentioning

confidence: 99%

Section: Policy Lbp-2mentioning

confidence: 99%

See 2 more Smart Citations

Load balancing in the presence of random node failure and recovery

Dhakal

Proceedings 20th IEEE International Parallel &Amp; Distributed Processing Symposium

Pezoa

et al. 2006

Self Cite

“…The occurrence of load-balancing instances, i.e. when to trigger the load distribution strategy, is not discussed in this paper since it is possible to employ one-shot load-balancing, multiple balancing instances, or any other scheme that would be suitable for the system at hand (Ghanem, 2004;Ghanem et al, 2004b;Dhakal et al, 2005;Dhakal et al, 2004;Dhakal, 2003). Furthermore, exchanged information about the state of the nodes is still assumed to occur frequently with additional information to be discussed subsequently.…”

Section: Introductionmentioning

confidence: 99%

Distributed Load Balancing in the Presence of Node Failure and Network Delays

Ghanem

Abdallah

IFAC Proceedings Volumes

et al. 2005

Self Cite

Dynamic Load Balancing for Robust Distributed Computing in the Presence of Topological Impairments

Wiley Handbook of Science and Technology for Homeland Security

Pezoa

Dietz

et al. 2008

The purpose of any distributed computing system (DCS) is to offer a flexible, reliable and powerful computing platform. With the advances in mobile computing, wireless communications and sensor networks, DCSs have emerged in new applications such as wireless sensor networks (WSNs), military battlefield awareness, surveillance and threat detection, to name a few. These new application areas introduce new challenges to DCSs when operated or deployed in harsh or threat-prone environments. For instance, in WSNs deployed in a military battlefield, the computing elements (CEs) of a DSC join and leave the DCS at any time in a stochastic fashion. More generally, factors such as limited or intermittent communication resources, CEs' power constraints or long-term physical damage of the CEs, can result in random topological changes in the DCS, which, in turn, can severely degrade their performance and reliability. Many of these factors can be attributable to physical attacks on our information infrastructure, of which weapons of mass destruction (WMD) is an important example. This observation has triggered government agencies, such as the Defense Threat Reduction Agency, to launch research initiatives in network science to understand the extent of damage that can be inflicted upon networks in the event of attacks and also to develop strategies to increase the robustness of networks when threat is present. In this article, we review modern dynamic load balancing (DLB) techniques and their mathematical stochastic models that can be exploited by DCS developers to increase the DCS's robustness to random topological changes, and at the same time, to efficiently use the available computing resources of the system in the presence of communication uncertainty and CE dysfunction. Two scenarios are considered: one where CEs can fail and recover at random instants and another where CEs can fail permanently. Under the first scenario we look for minimizing the average response time of a given application. In the second scenario the goal is to maximize the probability of successfully running an entire application. DLB policies are tested using a small-scale DCS environment and compared to