作者：
Aljohani, MahaUniv Jeddah
Coll Comp Sci & Engn Software Engn Dept Jeddah Saudi Arabia Univ Jeddah
Coll Comp Sci & Engn Software Engn Dept POB 16679 Jeddah Saudi Arabia
The data-intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big-data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links d...
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The data-intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big-data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow execution landscape, there is a rising emphasis on resolving Quality of Service (QoS) and security concerns within homogeneous execution settings. Workflow execution on diverse computing platforms has received limited research. We are developing an edge-cloud-specific multi-Layer Security and Quality-Aware (MLSQA) framework to solve research issues in this area. Node attacks are detected via reputation-based security features in the MLSQA framework, whereas link attacks are detected using machine learning. A unique trade-off metrics technique optimizes workflow security and QoS parameters, reducing energy usage and makespan. In addition, a fault-tolerant, energy-efficient job offloading technique is described in detail to improve the system's resilience to errors. The experiment is conducted using complex (i.e., memory and CPU intensive) scientific procedures with intermediate job dependencies, namely Inspiral workflow. The MLSQA framework excels in threat detection, demonstrating lower false alarms, reduced energy consumption, and quicker implementation than the recent secure workflow execution architecture.
SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on complex ordinary differential equations that ...
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SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on complex ordinary differential equations that can be assembled by alternative process equations for algal growth and grazing as well as zooplankton growth and mortality. It requires 128 constant parameters that are causally related to the metabolic, chemical and transport processes in lakes either estimated from laboratory and field experiments or adopted from the literature. An evolutionary algorithm (EA) was integrated into SALMO-OO in order to facilitate multi-objective optimization for selected parameters and to substitute them by optimum temperature and phosphate functions. The parameters were related to photosynthesis, respiration and grazing of the three algal groups diatoms, green algae and blue-green algae. The EA determined specific temperature and phosphate functions for same parameters for 3 lake categories that were validated by ecological data of six lakes from Germany and South Africa. The results of this study have demonstrated that: (1) the hybridization of ordinary differential equations by EA provide a sophisticated approach to fine-tune crucial parameters of complex ecological models, and (2) the multi-objective parameter optimization of SALMO-OO by EA has significantly improved the accuracy of simulation results for three different lake categories. (C) 2008 Elsevier B.V. All rights reserved.
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