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Evaluating weighted round robin load balancing for cloud web services
- In Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014 16th International Symposium on
, 2014
"... Abstract—Weighted round robin load balancing is a common routing policy offered in cloud load balancers. However, there is a lack of effective mechanisms to decide the weights assigned to each server to achieve an overall optimal revenue of the system. In this paper, we first experimentally explore ..."
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Abstract—Weighted round robin load balancing is a common routing policy offered in cloud load balancers. However, there is a lack of effective mechanisms to decide the weights assigned to each server to achieve an overall optimal revenue of the system. In this paper, we first experimentally explore the relation between probabilistic routing and weighted round robin load balancing policies. From the experiment a similar behavior is found between these two policies, which makes it possible to assign the weights according to the routing probability estimated from queueing theoretic heuristic and optimization algorithms studied in the literature. We focus in particular on algorithms based on closed queueing networks for multi-class workloads, which can be used to describe application with service level agreements differentiated across users. We also compare the efficiency of queueing theoretic methods with simple heuristics that do not require to specify a stochastic model of the application. Results indicate that queueing theoretical algorithms yield significantly better results than than routings proportional to the VM capacity with respect to throughput maximization. I.
Force-directed Geographical Load Balancing and Scheduling for Batch Jobs in Distributed Datacenters
"... Abstract — This work focuses on the load balancing and scheduling problem for batch jobs considering a cloud system comprised of geographically dispersed, heterogeneous datacenters. Each batch job is modeled using a directed acyclic graph of heterogeneous tasks. Load balancing and scheduling of batc ..."
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Abstract — This work focuses on the load balancing and scheduling problem for batch jobs considering a cloud system comprised of geographically dispersed, heterogeneous datacenters. Each batch job is modeled using a directed acyclic graph of heterogeneous tasks. Load balancing and scheduling of batch jobs with loose deadlines results in operational cost reduction in the cloud system due to availability of renewable energy sources in datacenters’ site and time of use dependent energy pricing in utility companies. A solution for load balancing and scheduling problem based on the force-directed scheduling approach is presented that considers the online application workload and limited resource and peak power capacity in each datacenter. The simulation results demonstrate significant operational cost decrease (up to 40%) using the proposed
Improving Hadoop Service Provisioning in A Geographically Distributed Cloud
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