Probabilistic-based workload forecasting and service redeployment for multi-tenant services

Abstract

This paper presents a two-stage service migration decision method which combines business workload forecasting with real-time load sensing, and thus adds business forecasting to previous load balancing approaches that rely solely upon real-time load sensing. The migration decision procedure and the detailed causal analysis algorithms based on Bayesian networks are also given. After the critical business indicators have been obtained from causal analysis, business fluctuation related with the critical indicators can be forecasted by using Markov chain method. And then, the migration decision can be made based on the forecasting results and the real-time load information together. We evaluate the migration decision method through three sets of experiments. We found that by migrating service on a shared multi-tenant service environment, the QoS requirement can be assured dynamically and the capability of workloads increases under same resource cost, which is helpful in optimised deploying for multi-tenant applications. include service computing and data engineering. This paper is a revised and expanded version of a paper entitled 'Probabilistic-based workload forecasting and service redeployment for multi-tenant services' presented at The

Load-balanced overlay recovery scheme with delay minimisation

“…Many researches on load-balanced routing have been conducted (Hussein et al, 2015;Liu et al, 2016). Hussein et al (2015) propose a weighted throttled load balancing algorithm by assigning a weight to each virtual machine (VM) in cloud environment.…”

“…Hussein et al (2015) propose a weighted throttled load balancing algorithm by assigning a weight to each virtual machine (VM) in cloud environment. Liu et al (2016) use Bayesian probabilistic inference to forecast the load information in multi-tenant service environment and proposed a service migration method for load balancing. Chen et al (2016) propose a novel classification method of peer-to-peer network traffic identification based on machine learning for balancing the link load and improving QoS.…”

Load-balanced overlay recovery scheme with delay minimisation

“…Many researches on load-balanced routing have been conducted (Hussein et al, 2015;Liu et al, 2016). Hussein et al (2015) propose a weighted throttled load balancing algorithm by assigning a weight to each virtual machine (VM) in cloud environment.…”

“…Hussein et al (2015) propose a weighted throttled load balancing algorithm by assigning a weight to each virtual machine (VM) in cloud environment. Liu et al (2016) use Bayesian probabilistic inference to forecast the load information in multi-tenant service environment and proposed a service migration method for load balancing. Chen et al (2016) propose a novel classification method of peer-to-peer network traffic identification based on machine learning for balancing the link load and improving QoS.…”