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Group-based adaptive result certification mechanism in Desktop Grids
Choi, S.,Buyya, R. North-Holland ; Elsevier Science Ltd 2010 Future generations computer systems Vol.26 No.5
In Desktop Grids, volunteers (i.e, resource providers) have heterogeneous properties and dynamically join and leave during execution. Moreover, some volunteers may behave erratically or maliciously. Thus, it is important to detect and tolerate erroneous results (i.e., result certification) in order to guarantee reliable execution, considering volatility and heterogeneity in a scheduling procedure. However, existing result certification mechanisms do not adapt to such a dynamic environment. As a result, they undergo high overhead and performance degradation. To solve the problems, we propose a new Group-based Adaptive Result Certification Mechanism (GARCM). GARCM applies different result certification and scheduling algorithms to volunteer groups that are constructed according to their properties such as volunteering service time, availability and credibility.
Batch Resizing Policies and Techniques for Fine-Grain Grid Tasks: The Nuts and Bolts
Muthuvelu, Nithiapidary,Chai, Ian,Chikkannan, Eswaran,Buyya, Rajkumar Korea Information Processing Society 2011 Journal of information processing systems Vol.7 No.2
The overhead of processing fine-grain tasks on a grid induces the need for batch processing or task group deployment in order to minimise overall application turnaround time. When deciding the granularity of a batch, the processing requirements of each task should be considered as well as the utilisation constraints of the interconnecting network and the designated resources. However, the dynamic nature of a grid requires the batch size to be adaptable to the latest grid status. In this paper, we describe the policies and the specific techniques involved in the batch resizing process. We explain the nuts and bolts of these techniques in order to maximise the resulting benefits of batch processing. We conduct experiments to determine the nature of the policies and techniques in response to a real grid environment. The techniques are further investigated to highlight the important parameters for obtaining the appropriate task granularity for a grid resource.
SLA-Based Scheduling of Bag-of-Tasks Applications on Power-Aware Cluster Systems
KIM, Kyong Hoon,LEE, Wan Yeon,KIM, Jong,BUYYA, Rajkumar The Institute of Electronics, Information and Comm 2010 IEICE transactions on information and systems Vol.93 No.12
<P>Power-aware scheduling problem has been a recent issue in cluster systems not only for operational cost due to electricity cost, but also for system reliability. In this paper, we provide SLA-based scheduling algorithms for bag-of-tasks applications with deadline constraints on power-aware cluster systems. The scheduling objective is to minimize power consumption as long as the system provides the service levels of users. A bag-of-tasks application should finish all the sub-tasks before the deadline as the service level. We provide the power-aware scheduling algorithms for both time-shared and space-shared resource sharing policies. The simulation results show that the proposed algorithms reduce much power consumption compared to static voltage schemes.</P>
A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition
( Li Liu ),( Shuxian Gu ),( Dongmei Fu ),( Miao Zhang ),( Rajkumar Buyya ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.1
Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.