A Near Optimal Approach in Choosing The Appropriate Physical Machines for Live Virtual Machines Migration in Cloud Computing

Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM migration problem is selecting an appropriate PM as a destination for a migrating VM. To face this issue, several approaches are proposed that focus on ranking potential destination PMs by addressing migration objectives. In this paper we propose a new hierarchal fuzzy logic system for ranking potential destination PMs for a migrating VM by considering following parameters: Performance efficiency, Communication cost between VMs, Power consumption, Workload, Temperature efficiency and Availability. Using hierarchal fuzzy logic systems which consider the mentioned six parameters which have great role in ranking of potential destination PMs for a migrating VM together, the accuracy of PMs ranking approach is increased, furthermore the number of fuzzy rules in the system are reduced, thereby reducing the computational time (which is critical in cloud environment). In our experiments, we compare our proposed approach that is named as (HFLSRPM: Hierarchal Fuzzy Logic Structure for Ranking potential destination PMs for a migrating VM) with AppAware algorithm in terms of communication cost and performance efficiency. The results demonstrate that by considering more effective parameters in the proposed PMs ranking approach, HFLSRPM outperforms AppAware algorithm

A Fuzzy Logic Based Approach in Choosing the Appropriate Physical Machines for Live Virtual Machines Migration in Cloud Computing

Migration of Virtual Machine (VM) has become a critical issue in modern data centers that are working based on virtualization. Among VM Migration challenges, choosing an appropriate Physical Machine (PM) is an important issue. To choose a place for VM several parameters, such as physical topology, migration duration, power consumption, service continuity, and price must be considered. Finding a near optimal place for VM migration that trades-off between some or all of these features is a challenging problem. In this paper, we propose three aspects for ranking potential destination PMs to find the most appropriate PM as VM host. In the first aspect, PMs are ranked in terms of servicing condition using Fuzzy logic technique according to three parameters: workload, performance efficiency and availability. In the second aspect, PMs are ranked in terms of power consuming condition using Fuzzy logic technique according to power, temperature efficiency and power efficiency metrics. In the third aspect, the output of two fuzzy logic engines with communication cost metric is used as the third fuzzy logic engine inputs that rank PMs. The proposed technique has been compared with AppAware algorithm in terms of communication cost and performance efficiency. Experimental results demonstrate that the proposed technique has appropriate improvement in these metrics and outperforms AppAware algorith

Intelligent adaptive multi-parameter migration model for load balancing virtualized cluster of servers

Najvažnija korist virtualizacije je dobivanje okruženja s ujednačenim opterećenjem kroz prenošenje (migraciju) virtualnim strojem (VM). Djelovanje usluga u skupinama (klasterima), kao što je prosječno vrijeme reakcije - Average Response Time - reducirano je inteligentnom odlukom VM o prenošenju. Prenošenje ovisi o nizu kriterija poput korištenja resursa (uporaba CPU, korištenje RAMa, korištenje mreže, itd.) i potrebe za strojevima (fizičkim (PM) i virtualnim (VM)). To je više- kriterijski problem prenošenja koji procjenjuje, komparira i sortira niz fizičkih i virtualnih strojeva (PM i VM) na osnovu parametara istaknutih u procesu prenošenja. Ali, koji parametar (parametri) ima dominantnu ulogu nad djelovanjem klastera u određenom vremenskom odjeljku? Kako možemo odrediti težinu parametara u nadolazećim vremenskim razmacima? Postojeći algoritmi prenošenja (migration algorithms) ne uzimaju u obzir težine parametara koje se mijenjaju ovisno o vremenu. Te analize pretpostavljaju fiksnu težinu za svaki parametar kroz široki raspon vremenskih intervala. To dovodi do netočnog predviđanja o traženju rješenja za svaki server. U našem se radu predstavlja novi Inteligentni i Adaptivni Multi Parametarski (IAMP) upravljač resursima na bazi prenošenja (migracije) za virtualizirane centre podataka i klastere s novom na umjetnoj neuronskoj mreži (ANN) temeljenoj analizi težina nazvanoj Error Number of Parameter Omission (ENPO). U svakom se vremenskom razmaku težina parametara ponovo izračunava te će nevažni parametri biti oslabljeni u postupku rangiranja. Obilježili smo parametre koji utječu na performansu klastera i koristili hot migration s naglaskom na skupini servera u XEN platformi virtualizacije. Eksperimentalni rezultati temeljeni na radnim opterećenjima sastavljenim od stvarnih aplikacija pokazuju da je primjenom IAMP-a moguće poboljšati rad virtualnog klaster sustava do 23 % u usporedbi s postojećim algoritmima. Što više, on brže reagira i eliminira vruće točke zbog svog potpuno dinamičkog upravljačkog algoritma.The most important benefit of virtualization is to get a load balanced environment through Virtual Machine (VM) migration. Performance of clustered services such as Average Response Time is reduced through intelligent VM migration decision. Migration depends on a variety of criteria like resource usage (CPU usage, RAM usage, Network Usage, etc.) and demand of machines (Physical (PM) and Virtual (VM)). This is a multi-criteria migration problem that evaluates, compares and sorts a set of PMs and VMs on the basis of parameters affected on migration process. But, which parameter(s) has dominant role over cluster performance in each time window? How can we determine weight of parameters over oncoming time slots? Current migration algorithms do not consider time-dependent variable weights of parameters. These studies assume fixed weight for each parameter over a wide range of time intervals. This approach leads to imprecise prediction of recourse demand of each server. Our paper presents a new Intelligent and Adaptive Multi Parameter migration-based resource manager (IAMP) for virtualized data centres and clusters with a novel Artificial Neural Network (ANN)-based weighting analysis named Error Number of Parameter Omission (ENPO). In each time slot, weight of parameters is recalculated and non-important ones will be attenuated in ranking process. We characterized the parameters affecting cluster performance and used hot migration with emphasis on cluster of servers in XEN virtualization platform. The experimental results based on workloads composed of real applications, indicate that IAMP management framework is feasible to improve the performance of the virtualized cluster system up to 23 % compared to current algorithms. Moreover, it reacts more quickly and eliminates hot spots because of its full dynamic monitoring algorithm

Optimizing Virtual Resource Management in Cloud Datacenters

Datacenter clouds (e.g., Microsoft\u27s Azure, Google\u27s App Engine, and Amazon\u27s EC2) are emerging as a popular infrastructure for computing and storage due to their high scalability and elasticity. More and more companies and organizations shift their services (e.g., online social networks, Dropbox file hosting) to clouds to avoid large capital expenditures. Cloud systems employ virtualization technology to provide resources in physical machines (PMs) in the form of virtual machines (VMs). Users create VMs deployed on the cloud and each VM consumes resources (e.g., CPU, memory and bandwidth) from its host PM. Cloud providers supply services by signing Service Level Agreement (SLA) with cloud customers that serves as both the blueprint and the warranty for cloud computing. Under-provisioning of resources leads to SLA violations while over-provisioning of resources leads to resource underutilization and then revenue decrease for the cloud providers. Thus, a formidable challenge is effective management of virtual resource to maximize energy efficiency and resource utilization while satisfying the SLA. This proposal is devoted to tackle this challenge by addressing three fundamental and essential issues: i) initial VM allocation, ii) VM migration for load balance, and iii) proactive VM migration for long-term load balance. Accordingly, this proposal consists of three innovative components: (1) Initial Complementary VM Consolidation. Previous resource provisioning strategies either allocate physical resources to virtual machines (VMs) based on static VM resource demands or dynamically handle the variations in VM resource requirements through live VM migrations. However, the former fail to maximize energy efficiency and resource utilization while the latter produce high migration overhead. To handle these problems, we propose an initial VM allocation mechanism that consolidates complementary VMs with spatial/temporal-awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host\u27s capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the lifetime resource utilization patterns of short-term VMs or periodical resource utilization patterns of long-term VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service hence increases energy efficiency and resource utilization and also reduces the number of VM migrations and SLA violations. (2) Resource Intensity Aware VM Migration for Load Balance. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogenous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which leads to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. This proposed mechanism dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrate VMs to PMs with minimum VM performance degradation. (3) Proactive VM Migration for Long-Term Load Balance. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of SLA violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Finally, we conducted extensive experiments to evaluate the proposed three mechanisms. i) We conducted simulation experiments based on two real traces and real-world testbed experiments to show that the initial complementary VM consolidation mechanism significantly reduces the number of PMs used, SLA violations and VM migrations of the previous resource provisioning strategies. ii) We conducted trace-driven simulation and real-world testbed experiments to show that RIAL outperforms other load balancing approaches in regards to the number of VM migrations, VM performance degradation and VM communication cost. iii) We conducted trace-driven experiments to show that the MDP-based load balancing algorithm outperforms previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance