Immune genetic algorithm for scheduling service workflows with QoS constraints in cloud computing

Resources allocation and scheduling of service workflows is an important challenge in distributed computing. This is particularly true in a cloud computing environment, where many computer resources may be available at specified locations, as and when required. Quality-of-service (QoS) issues such as execution time and running costs must also be considered. Meeting this challenge requires that two classic computational problems be tackled. The first problem is allocating resources to each of the tasks in the composite web services or workflow. The second problem involves scheduling resources when each resource may be used by more than one task, and may be needed at different times. Existing approaches to scheduling workflows or composite web services in cloud computing focus only on reducing the constraint problem - such as the deadline constraint, or the cost constraint (bi-objective optimisation). This paper proposes a new genetic algorithm that solves a scheduling problem by considering more than two constraints (multi-objective optimisation). Experimental results demonstrate the effectiveness and scalability of the proposed algorithm. In verdeelde rekenaarverwerking is hulpbrontoewysing en skedulering van diens werkstrome 'n belangrike uitdaging, veral in 'n wolkrekenaar omgewing waar daar baie rekenaarhulpbronne beskikbaar is op bepaalde plekke. Daarbenewens moet die kwaliteit van die diens kwessies, soos uitvoertyd en bedryfskoste, in ag geneem word. Om hierdie uitdaging suksesvol te adresseer moet twee klassieke rekenaarverwerking probleme aangepak word: eerstens, die toekenning van hulpbronne aan elk van die take in die saamgestelde webdienste of werkstrome en tweedens die skedulering van hulpbronne wanneer elke hulpbron gebruik kan word deur meer as een taak en op verskillende tye nodig mag wees. Die bestaande benaderings vir die skedulering van werkstrome of saamgestelde webdienste in wolkverwerking fokus op die vermindering van die beperkingsprobleme, soos die sperdatum of die kostebeperking. Hierdie artikel stel 'n nuwe genetiese algoritme, wat die hulpbrontoewysing en skedulering probleem deur die oorweging van meer as twee beperkings oplos, voor. Eksperimentele resultate demonstreer die doeltreffendheid en skaalbaarheid van die voorgestelde algoritme.