Abstract: Many of the network security protocols employed today utilize symmetric block ciphers (DES, AES and CAST etc). The majority of the symmetric block ciphers implement the crucial substitution operation using look up tables, called substitution boxes. These structures should be highly nonlinear and have bit dispersal, i.e. avalanche, properties in order to render the cipher with resistant to cryptanalysis attempts, such as linear and differential cryptanalysis. Highly secure substitution boxes can be constructed using particular Boolean functions as components that have certain mathematical properties which enhance the robustness of the whole cryptoalgorithm. However, enforcing these properties on SBoxes is a highly computationally intensive task. In this paper, we present a distributed algorithm and its implementation on a computingcluster that accelerates the construction of secure substitution boxes with good security properties. It is fully parametric since it can employ any class of Boolean functions with algorithmically definable properties and can construct SBoxes of arbitrary sizes. We demonstrate the efficiency of the distributed algorithm implementation compared to its sequential counterpart, in a number of experiments.
Abstract: Wireless Sensor Networks are comprised of a vast number of ultra-small, autonomous computing and communication devices, with restricted energy, that co-operate to accomplish a large sensing task. In this work: a) We propose extended versions of two data propagation protocols for such networks: the Sleep-Awake Probabilistic Forwarding Protocol (SW-PFR) and the Hierarchical Threshold sensitive Energy Efficient Network protocol (H-TEEN). These non-trivial extensions improve the performance of the original protocols, by introducing sleep-awake periods in the PFR protocol to save energy, and introducing a hierarchy of clustering in the TEEN protocol to better cope with large networks, b) We implemented the two protocols and performed an extensive simulation comparison of various important measures of their performance with a focus on energy consumption, c) We investigate in detail the relative advantages and disadvantages of each protocol, d) We discuss a possible hybrid combination of the two protocols towards optimizing certain goals.
Abstract: Wireless sensor networks are composed of a vast number of ultra-small, fully autonomous computing, communication, and sensing devices, with very restricted energy and computing capabilities, that cooperate to accomplish a large sensing task. Such networks can be very useful in practice. The authors propose extended versions of two data propagation protocols: the Sleep-Awake Probabilistic Forwarding (SW-PFR) protocol and the Hierarchical Threshold-Sensitive Energy-Efficient Network (H-TEEN) protocol. These nontrivial extensions aim at improving the performance of the original protocols by introducing sleep-awake periods in the PFR case to save energy and introducing a hierarchy of clustering in the TEEN case to better cope with large network areas. The authors implemented the two protocols and performed an extensive comparison via simulation of various important measures of their performance with a focus on energy consumption. Data propagation under this approach exhibits high fault tolerance and increases network lifetime.
Abstract: Wireless sensor networks are composed of a vast number of ultra-small, fully autonomous computing, communication, and sensing devices, with very restricted energy and computing capabilities, that cooperate to accomplish a large sensing task. Such networks can be very useful in practice. The authors propose extended versions of two data propagation protocols: the Sleep-Awake Probabilistic Forwarding (SW-PFR) protocol and the Hierarchical Threshold-Sensitive Energy-Efficient Network (H-TEEN) protocol. These nontrivial extensions aim at improving the performance of the original protocols by introducing sleep-awake periods in the PFR case to save energy and introducing a hierarchy of clustering in the TEEN case to better cope with large network areas. The authors implemented the two protocols and performed an extensive comparison via simulation of various important measures of their performance with a focus on energy consumption. Data propagation under this approach exhibits high fault tolerance and increases network lifetime.
Abstract: The existence of good probabilistic models for the job
arrival process and job characteristics is important for
the improved understanding of grid systems and the
prediction of their performance. In this study, we
present a thorough analysis of the job inter-arrival
times, the waiting times at the queues, the execution
times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is part of the
EGEE Grid infrastructure. By computing the Hurst
parameter of the inter-arrival times we find that the
job arrival process exhibits self-similarity/long-range
dependence. We also propose simple and intuitive
models for the job arrival process and the job
execution times. The models proposed were validated
and were found to be in very good agreement with our
empirical measurements.
Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.
Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.
Abstract: The existence of good probabilistic models
for the job arrival process and the delay components
introduced at different stages of job processing in a
Grid environment is important for the improved
understanding of the Grid computing concept. In this
study, we present a thorough analysis of the job
arrival process in the EGEE infrastructure and of the
time durations a job spends at different states in the
EGEE environment. We define four delay compo-
nents of the total job delay and model each compo-
nent separately. We observe that the job inter-arrival
times at the Grid level can be adequately modelled by
a rounded exponential distribution, while the total job
delay (from the time it is generated until the time it
completes execution) is dominated by the computing
element’s register and queuing times and the worker
node’s execution times. Further, we evaluate the
efficiency of the EGEE environment by comparing
the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we
would obtain similar performance if we submitted the
same workload to a super-cluster of size equal to 34%
of the total average number of CPUs participating in
the EGEE infrastructure. We also analyze the job
inter-arrival times, the CE’s queuing times, the WN’s
execution times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is node in the
EGEE infrastructure. In contrast to the Grid level, we
find that at the cluster level the job arrival process
exhibits self-similarity/long-range dependence. Final-
ly, we propose simple and intuitive models for the job
arrival process and the execution times at the cluster
level.