scholarly journals A simple heuristic for load balancing in parallel processing networks with highly variable service time distributions

2009 ◽  
Vol 64 (2) ◽  
pp. 145-165
Author(s):  
Luz A. Caudillo-Fuentes ◽  
David L. Kaufman ◽  
Mark E. Lewis
Author(s):  
G. Soniya Priyatharsini ◽  
N. Malarvizhi

Cloud computing is a service model in internet that provides virtualized resources to its clients. These types of servicing give a lot of benefits to the cloud users where they can pay as per their use. Even though they have benefits, they also face some problems like receiving computing resources, which is guaranteed on time. This time delay may affect the service time and the makespan. Thus, to reduce such problems, it is necessary to schedule the resources and then allocate it to using an optimized hypervisor. Here, the proposed method is used to do the above-mentioned problem. First, the available resources are clustered with respect to their characteristics. Then the resources are scheduled using this method. Finally, with respect to that of the clients request the resources, the resources are allocated. Here, the cost is the fitness of the allocation.


2021 ◽  
Author(s):  
Erhun Özkan

We study scheduling control of parallel processing networks in which some resources need to simultaneously collaborate to perform some activities and some resources multitask. Resource collaboration and multitasking give rise to synchronization constraints in resource scheduling when the resources are not divisible, that is, when the resources cannot be split. The synchronization constraints affect the system performance significantly. For example, because of those constraints, the system capacity can be strictly less than the capacity of the bottleneck resource. Furthermore, the resource scheduling decisions are not trivial under those constraints. For example, not all static prioritization policies retain the maximum system capacity, and the ones that retain the maximum system capacity do not necessarily minimize the delay (or, in general, the holding cost). We study optimal scheduling control of a class of parallel networks and propose a dynamic prioritization policy that retains the maximum system capacity and is asymptotically optimal in diffusion scale and a conventional heavy-traffic regime with respect to the expected discounted total holding cost objective.


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