Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Multi-Agent Protocol for Load Balancing (Mapload)
Evaluation and Empirical Results
Related Work
Conclusion and Future Work
References
All Topics
Computer Science
Distributed Systems

Agent-based load balancing in Cloud data centers

Profile image of Adrian Ramirez-nafarrateAdrian Ramirez-nafarrate

2015, Cluster Computing

https://doi.org/10.1007/S10586-015-0460-X
visibility

…

description

24 pages

link

1 file

Abstract

Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Related papers

A Software Agent Based Technique for Load Balancing in Partitioned Cloud
Rajni Mohana

International Journal of Engineering & Technology, 2018

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud is increasing day by day. Public clouds considered and is the most convenient platform for common cloud users with generic needs and lesser security concerns. Public cloud can cater to the needs of a large group of users and provide a variety of services. Lower cost and timely availability are the other advantages one expects from public clouds. These features make it very much convenient and attractive choice. But on the other hand, handling public cloud become unmanageable in comparison to other counterparts. Monitoring so many users, tasks and resources are difficult task. Sometimes public clouds are divided on geographically. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. But, partitioned clouds introduce different ends for submission and operations of cloudlets and ...

View PDFchevron_right
Multi-Agent based Architecture for Dynamic VM Consolidation in Cloud Data Centers
Fahimeh Farahnakian, Pasi Liljeberg, Juha Plosila, Hannu Tenhunen

As the scale of cloud data centers becomes larger and larger, the energy consumption of data centers also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off idle or under-utilized Physical Machines (PMs) in data centers. In this paper, we present a multi-agent based architecture for performing dynamic VM consolidation task. The architecture uses a local agent in each PM to decide when a PM becomes overloaded using reinforcement learning approach. Moreover, a global agent is proposed as a supervisor to dynamically optimize the VM placement based on the local agents’ decisions. Therefore, agents cooperate together to minimize the number of active PMs according to the current resource requirements. Experimental results on the real workload traces from more than a thousand PlanetLab virtual machines show that the proposed architecture can reduce the energy consumption and maintains the required performance level in a large-scale data center.

View PDFchevron_right
A low-level resource allocation in an agent-based Cloud Computing platform
javier Bajo, Juan M Corchado

The distribution of computational resources in a Cloud Computing platform is a complex process with several parameters to consider such as the demand for services, available computational resources and service level agreements with end users. Currently, the state-of-the-art presents centralized approaches derived from previous technologies related to cluster of servers. These approaches allocate computational resources by means of the addition/removal of (physical/virtual) computational nodes. However, virtualization technology currently allows for research into new techniques, which makes it possible to allocate at a lower level. In other words, not only is it possible to add/remove nodes, but also to modify the resources of each virtual machine (low level resource allocation). Thus, agent theory is a key technology in this field, allowing decentralized resource allocation. This innovative approach has undeniable improvements such us computational load distribution and reduced computation time. The evaluation was carried out through experiments in a real Cloud environment, thus proving the validity of the proposed approach.

View PDFchevron_right
Multi-resource Workload Consolidation in Cloud Data Centers
Giuseppe Papuzzo

The success of Cloud computing has led to the establishment of large data centers to serve the increasing need for on-demand computational power, but data centers consume a huge amount of electrical power. The problem can be alleviated by mapping virtual machines, VMs, which run client applications, on as few servers as possible, so that some servers with low traffic can be put in low consuming sleep modes. This paper presents a new approach for the adaptive assignment of VMs to servers and their dynamic migration, with a twofold goal: reduce the energy consumption and meet the Service Level Agreements established with users. The approach, based on ant-inspired algorithms, founds on statistical processes: the mapping and migration of VMs are driven by Bernoulli trials whose success probability depends on the utilization of single servers. Experiments highlight the two main advantages with respect to the state of the art: the approach is self-organizing and mostly decentralized, since each server locally decides whether or not a new VM can be served, and the migration process is continuous and adaptive, thus avoiding the need for the simultaneous reassignment of many VMs.

View PDFchevron_right
A Comprehensive Survey of Load Balancing Strategies using Hadoop Queue Scheduling and Virtual Machine Migration
Niladri Dey

IEEE Access

The recent growth in the demand for scalable applications from the consumers of the services have motivated the application development community to build and deploy the applications on cloud in form of services. The deployed applications have significant dependency on the infrastructure available with the application providers. Bounded by the limitations of available resource pools on premises, many of the application development companies have migrated the applications to third party cloud environments called data centers. The data center owners or the cloud service providers are entitled to ensure high performance, high availability of the applications and at the same time the desired scalability for the applications. Also, the cloud service providers are also challenged in terms of cost reduction and energy consumption reductions for better manageability of the data center without degrading the performance of the deployed applications. It is to be noted that, the performance of the application does not only depend on the responsiveness of the applications, rather also must be measured in terms of service level agreements. Violation of the service level agreements or SLA can easily disprove the purpose of application deployments on cloud-based data centers. Thus, the data center owners apply multiple load balancing strategies for maintaining the desired outcomes from the application owners at the minimized cost of data center maintainability. Hence, the demand of the research is to thoroughly study and identify the scopes for improvements in the parallel research outcomes. As the number of applications ranging from small data centric applications coming with the demand of frequent updates with higher computational capabilities to the big data centric application as big data analytics applications coming with efficient algorithms for data and computation load managements, the data center owners are forced to think for efficient algorithms for load managements. The algorithms presented by various research attempts have engrossed on application specific demands for load balancing using virtual machine migrations and the solution as the proposed algorithms have became application problem specific. Henceforth, the further demand of the research is a guideline for selecting the appropriate load balancing algorithm via virtual machine migration for characteristics based specific applications. Hence, this work presents a comprehensive survey on existing virtual machine migration and selection processes to understand the specific application-oriented capabilities of these strategies with the advantages and bottlenecks. Also, with the understanding of the existing measures for load balancing, it is also important to furnish the further improvement strategies, which can be made possible with detailed understanding of the parallel research outcomes. Henceforth, this work also equips the study with guidelines for improvements and for further researches. Nonetheless, the study cannot be completed without the mathematical analysis for better understanding and experimental analysis on different standards of datasets for better conclusive decisions. Hence, this work also presents the discussion on mathematical models and experimental result analysis for conclusive decision on the improvement factors and the usability of the migration methods for various purposes. Finally, this work is a comprehensive survey on the background of the research, recent research outcomes using mathematical modelling and experimental researches on various available datasets and finally identify the scopes of improvements considering various aspects such as execution time, mean time before a VM migration, mean time before a host shutdown, number of node shutdowns, SLA perf degradation, VM migrations, energy Consumption This work is licensed under a Creative Commons Attribution 4.

View PDFchevron_right
Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment
Dr. Ayman khedr

Computers, materials & continua, 2023

According to the advances in users' service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources. Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload. A load balancing algorithm is developed to serve users' requests to improve the solution of workload problems with an efficient distribution. The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability of CC. Then, the server's availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance. Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and (low resources and large number) of IoT.

View PDFchevron_right
A Multi-Agent based Load balancing System in IaaS Cloud Environment
Behnam Faghih

International Robotics & Automation Journal, 2016

Infrastructure as a service (IaaS) is a type of cloud computing in which a third-party provider hosts virtualized computing resources over the Internet for executing tasks in the cloud computing. Whenever some VMs are overloaded and some VMs are under loaded, this situation may cause to SLA violation and leads to the reduction of customer satisfaction level and further affects the cloud provider leading to penalty. However, in this study, we propose a Multiple Agent-based Load Balancing Algorithm (MA) in which shift the load in the IaaS to achieve well dynamic load balancing across virtual machines for maximizing the utilization. The proposed algorithm with regard to changing environment and characteristics of the VMs, perform both of senderinitiated and receiver-initiated approach to balances the load of an IaaS in such a way that the amount of waiting time of the tasks in the queue is minimal and at the same time the SLA is guaranteed. We have compared the proposed algorithm with existing load balancing and scheduling algorithms via simulation. The simulation results show that the proposed algorithm is more effective and there is a good improvement in the load-balance, response time and makespan.

View PDFchevron_right
Empirical analysis of dynamic load balancing techniques in cloud computing
Priyanka Maharana

International journal of smart sensors and ad hoc networks, 2022

Virtualization, dispersed registration, systems administration, programming, and web administrations are all examples of "distributed computing." Customers, datacenters, and scattered servers are just a few of the components that make up a cloud. It includes things like internal failure adaption, high accessibility, flexibility, adaptability, lower client overhead, lower ownership costs, on-demand advantages, and so on. The basis of a feasible load adjusting computation is key to resolving these challenges. CPU load, memory limit, deferral, and system load are all examples of heaps. Burden adjustment is a method for distributing the load across the many hubs of a conveyance framework in order to optimize asset utilization and employment response time while avoiding a situation where some hubs are heavily loaded while others are idle or performing little work. Burden adjustment ensures that at any one time, each processor in the framework or each hub in the system does about the same amount of work. This method may be initiated by the sender, the collector, or the symmetric sort (the blend of sender-started and recipient started types). With some example data center loads, the goal is to create several dynamic load balancing techniques such as Round Robin, Throttled, Equally Spread Current Execution Load, and Shortest Job First algorithms.

View PDFchevron_right
Multi-Agent Approach for Dynamic Elasticity of Virtual Machines Provisioning in Heterogeneous Distributed Computing Environment
Роман Костромин

2018 International Conference on High Performance Computing & Simulation (HPCS), 2018

We address dynamic elasticity issues of VM provisioning in a heterogeneous distributed computing environment that integrates resources of a data center. We consider the scenario when the center includes dedicated resources (private cloud) for providing virtualized service and non-dedicated resources for Grid-computing. Existing platforms for the resource virtualization does not support management of such an infrastructure. We propose an approach to a job management based on the dynamic elasticity of virtual machines provisioning using resources of both types. We develop the multiagent job scheduler for dedicated resources and hypervisor shell to launch virtual machines through queues of resource management systems in non-dedicated resources. The scheduler provides a dynamic elasticity of virtual machine provisioning. Advantages of the offered approach to the resource virtualization are demonstrated by an example of a job flow management for a scalable application to solve the complicated practical problem. It is related to the energy security of Vietnam. Provided experiments show that using the developed tools together with the platform for the resource virtualization enables agents to significantly speed up the problem-solving process.

View PDFchevron_right
Agent Based Load Balancing in Grid Computing
ali wided

2020

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

View PDFchevron_right

References (50)

  1. Amazon EC2: Elastic load balancing. http://aws.amazon.com/ elasticloadbalancing/ (2015). Accessed 1 Feb 2015
  2. Anderson, P., Bijani, S., Vichos, A.: Multi-agent negotiation of virtual machine migration using the lightweight coordination cal- culus. In: Jezic, G., Kusek, M., Nguyen, N.-T., Howlett, R.J., Jain, L.C. (eds.) 6th KES International Conference on Agent and Multi- agent Systems: Technologies and Applications. Lecture Notes in Computer Science, vol. 7327, pp. 124-133. Springer, Berlin (2012)
  3. Andreolini, M., Casolari, S., Colajanni, M., Messori, M.: Dynamic load management of virtual machines in cloud architectures. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) Cloud Computing. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineer- ing, vol. 34, pp. 201-214. Springer, Berlin (2010)
  4. Bansal, N., Lodi, A., Sviridenko, M.: A tale of two dimensional bin packing. In: Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science, pp. 657-666 (2005)
  5. Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE-a white paper. EXP Search Innov. 3(3), 6-19 (2003)
  6. Bobroff, N., Kochut, A., Beaty K.: Dynamic placement of virtual machines for managing sla violations. In: Proceedings of the 10th IFIP/IEEE International Symposium on Integrated Network Man- agement, pp. 119-128 (2007)
  7. Breitgand, D., Marashini, A., Tordsson, J.: Policy-driven service placement optimization in federated clouds. Technical Report, IBM Research Division (2011)
  8. Buyya, R., Beloglazov, A., Abawajy, J.: Energy-efficient man- agement of data center resources for cloud computing: a vision, architectural elements, and open challenges. In: Proceedings of the 2010 International Conference on Parallel and Distributed Process- ing Techniques and Application, pp. 12-15 (2010)
  9. Canturk, I., Chengwei, W., Bhatt, C., Shanmuganathan, G., Holler, A.M.: Process demand prediction for distributed power and resource management. U.S. Patent Application 13/281,234, 2011
  10. Chekuri, C., Khanna, S.: On multi-dimensional packing problems. SIAM J. Comput. 33(4), 837-851 (2014)
  11. Cisco systems: Cisco USB B440 M2 high-performance blade server data sheet. http://www.cisco.com/en/US/prod/collateral/ ps10265/ps10280/ps11584/data_sheet_c78-646960.html (2015). Accessed 1 Feb 2015
  12. Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A.: Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, USENIX Association, vol. 2, pp. 273-286 (2005)
  13. Coffman, E.G., Garey, M.R., Johnson, D.S., Tarjan, R.E.: Perfor- mance bounds for level-oriented two-dimensional packing algo- rithms. SIAM J. Comput. 9(4), 808-826 (1980)
  14. Daniels, J.: Server virtualization architecture and implementation. Crossroads 16(1), 8-12 (2009)
  15. Das, B., Bharghavan, V.: Routing in ad-hoc networks using min- imum connected dominating sets. In: Proceedings of the IEEE International Conference on Communications, pp. 376-380 (1997)
  16. Gutierrez-Garcia, J.O., Ramirez-Nafarrate, A.: Policy-based agents for virtual machine migration in cloud data centers. In: Proceedings of the IEEE International Conference on Services Computing, pp. 603-610 (2013)
  17. Gutierrez-Garcia, J.O., Sim, K.M.: A family of heuristics for agent- based elastic cloud bag-of-tasks concurrent scheduling. Future Gener. Comput. Sys. 29(7), 1682-1699 (2013)
  18. Gutierrez-Garcia, J.O., Sim, K.M.: Agent-based Cloud Service Composition. Appl. Intell. 38(3), 436-464 (2013)
  19. Hyser, C., Mckee, B., Gardner, R., Watson, B.J.: Autonomic virtual machine placement in the data center. Hewlett Packard Laborato- ries, Technical Report HPL-2007-189, 2007-189 (2007)
  20. Ji, M., Waldspurger, C.A., Zedlewski, J.: Method and system for determining a cost-benefit metric for potential virtual machine migrations. U.S. Patent No. 8,095,929, 2012. Washington, DC: U.S. Patent and Trademark Office
  21. Kerr, A., Diamos, G., Yalamanchili, S.: A characterization and analysis of GPGPU kernels. Technical Report GIT-CERCS-09-06, Georgia Institute of Technology (2009)
  22. Kleiminger, W., Kalyvianaki, E., Pietzuch, P.: Balancing load in stream processing with the cloud. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops, pp. 16-21 (2011)
  23. Lang, W., Patel, J.M., Naughton, J.F.: On energy management, load balancing and replication. ACM SIGMOD Rec. 38(4), 35-42 (2010)
  24. Lin, C.C., Liu, P., Wu, J.J.: Energy-efficient virtual machine pro- vision algorithms for cloud systems. In: Proceedings of the 2011
  25. Fourth IEEE International Conference on Utility and Cloud Com- puting, pp. 81-88 (2011)
  26. Liu, H., Xu, C.Z., Jin, H., Gong, J., Liao, X.: Performance and energy modeling for live migration of virtual machines. In: Proceedings of the 20th International Symposium on High Perfor- mance Distributed Computing, pp. 171-182 (2011)
  27. Metri, G., Srinivasaraghavan, S., Shi, W., Brockmeyer, M.: Exper- imental analysis of application specific energy efficiency of data centers with heterogeneous servers. In: Proceedings of IEEE 5th International Conference on Cloud Computing, pp. 786-793 (2012)
  28. Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2011)
  29. Patel, P., Bansal, D., Yuan, L., Murthy, A., Greenberg, A., Maltz, D. A., Kern, R., Kumar, H., Zikos, M., Wu, H., Kim, C., Karri, N.: Ananta: Cloud scale load balancing. In: Proceedings of the ACM Conference on SIGCOMM, pp. 207-218 (2013)
  30. Red Hat: Cluster general properties-administration guide. https://access.redhat.com/site/documentation/en-US/Red_ Hat_Enterprise_Virtualization/3.1/html/Administration_Guide/ Cluster_general_entities.html (2015). Accessed 1 Feb 2015
  31. Red Hat: Monitoring-administration guide. https://access.redhat. com/documentation/en-US/Red_Hat_Enterprise_Virtualization_ for_Servers/2.2/html/Administration_Guide/Administration_ Guide-Monitoring.html (2015). Accessed 1 Feb 2015
  32. Red Hat: Red Hat Enterprise Virtualization 3.2-explanation of set- tings and controls in the Edit cluster policy window. https://access. redhat.com/site/documentation/en-US/Red_Hat_Enterprise_ Virtualization/3.1/html/Administration_Guide/Cluster_general_ entities.html (2015). Accessed 1 Feb 2015
  33. Red Hat: Red Hat Enterprise Virtualization 3.2-technical reference guide. https://access.redhat.com/site/documentation/ en-US/Red_Hat_Enterprise_Virtualization/3.2/html/Technical_ Reference_Guide/index.html (2015). Accessed 1 Feb 2015
  34. Ren, G., Tune, E., Moseley, T., Shi, Y., Rus, S., Hundt, R.: Google-wide profiling: a continuous profiling infrastructure for data centers. IEEE Micro 30(4), 65-79 (2010)
  35. Ren, X., Lin, R., Zou, H.: A dynamic load balancing strategy for cloud computing platform based on exponential smoothing fore- cast. In: Proceedings of the 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 220-224 (2011)
  36. Siebert, E.: Understanding VMware ESX virtual machine physical and virtual hardware. http://searchvmware.techtarget.com/tip/Und erstanding-VMware-ESX-virtual-machine-physical-and-virtual- hardware (2015). Accessed 1 Feb 2015
  37. Sim, K.M.: Agent-based cloud computing. IEEE T. Serv. Comput. 5(4), 564-577 (2012)
  38. Skiena, S.: The Algorithm Design Manual: Text, vol. 1. Springer, Berlin (1998)
  39. SPEC: All published SPECvirt_sc2010 performance results SPEC. http://www.spec.org/virt_sc2010/results/specvirt_sc2010_ perf.html (2015). Accessed 1 Feb 2015
  40. Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data cen- ters. IEEE T. Serv. Comput. 3(4), 266-278 (2010)
  41. Tsai, Y.L., Huang, K.C., Chang, H.Y., Ko, J., Wang, E.T., Hsu, C.H.: Scheduling multiple scientific and engineering workflows through task clustering and best-fit allocation. In: Proceedings of the 2012 IEEE Eighth World Congress on Services, pp. 1-8 (2012)
  42. Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review 39(1), 50-55 (2008)
  43. VMware: Distributed resource scheduler-recommendation page. http://pubs.vmware.com/vsphere-4-esx-vcenter/index.jsp# viewing_drs_cluster_information/c_the_drs_recommendations_ page.html (2015). Accessed 1 Feb 2015
  44. VMware: VMware Infrastructure-Resource management with VMware distributed resource scheduler. http://www.vmware.com/ pdf/vmware_drs_wp.pdf (2015). Accessed 1 Feb 2015
  45. Von Laszewski, G., Diaz, J., Wang, F., Fox, G.C.: Comparison of multiple cloud frameworks. In: Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing, pp. 734-741 (2012)
  46. Voorsluys, W., Broberg, J., Venugopal, S., Buyya, R.: Cost of vir- tual machine live migration in clouds: a performance evaluation. Cloud Computing, pp. 254-265. Springer, Berlin (2009)
  47. Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Black-box and gray-box strategies for virtual machine migration. In: Proceed- ings of the Fourth USENIX Symposium on Networked Systems Design & Implementation, pp. 701-708 (2007)
  48. Wu, Y., Yuan, Y., Yang, G., Zheng, W.: Load prediction using hybrid model for computational grid. In: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 235- 242 (2007)
  49. Xing, L.: A self-management approach to service optimization and system integrity through multi-agent systems. Master's Thesis, University of Oslo (2008)
  50. Zhang, H., Jiang, F., Yoshihira, K., Chen, H., Saxena, A.: Intelligent workload factoring for a hybrid cloud computing model. In: Pro- ceedings of the 2009 World Conference on Services-I, pp. 701-708 (2009) 123

Related papers

A Multi-Agent-Based VM Migration for Dynamic Load Balancing in Cloud Computing Cloud Environment
Chandan Banerjee

International Journal of Cloud Applications and Computing

Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional clou...

View PDFchevron_right
Policy-Based Agents for Virtual Machine Migration in Cloud Data Centers
Adrian Ramirez-nafarrate

2013 IEEE International Conference on Services Computing, 2013

Cloud data centers are networked server farms commonly composed of heterogeneous servers with a wide variety of computing capacities. Virtualization technology, in Cloud data centers, has improved server utilization and server consolidation. However, virtual machines may require unbalanced levels of computing resources (e.g., a virtual machine running a compute-intensive application with low memory requirements) causing resource usage imbalances within physical servers. In this paper, an agent-based distributed approach capable of balancing different types of workloads (e.g., memory workload) by using virtual machine live migration is proposed. Agents acting as server managers are equipped with 1) a collaborative workload balancing protocol, and 2) a set of workload balancing policies (e.g., resource usage migration thresholds and virtual machine migration heuristics) to simultaneously consider both server heterogeneity and virtual machine heterogeneity. The experimental results show that policy-based workload balancing is effectively achieved despite dealing with server heterogeneity and heterogeneous workloads.

View PDFchevron_right
Distributed Agent-Based Load Balancer for Cloud Computing
Vladimir Getov

2015

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

View PDFchevron_right
Agent Based Dynamic Load Balancing in Cloud Computing
shivangi katiyar, Jitender Grover

Cloud Computing is one of the latest computing paradigms in IT sector today where applications, platforms, software and some other IT services are provided over the internet. It is also known as On Demand Computing. Cloud Computing considers shared pool of configurable computing resources which requires proper resource distribution among the tasks, otherwise in some situations resources may over-utilized or underutilized. For the sake of efficient resource utilization, Load Balancing System problem needs more attention in cloud computing. In This paper, we have used Agent Based Dynamic Load Balancing (ABDLB) approach in which mobile agent plays very important role, which is a software entity and usually defined as an independent software program that runs on behalf of a network administrator. It has ability to learn. After comparing the proposed method with traditional load balancing scheme we concluded that Agent Base load balancing scheme greatly reduces the communication cost of servers, accelerates the rate of load balancing which indirectly improves the Throughput and Response Time of the cloud.

View PDFchevron_right
Load balancing in distributed multi-agent computing systems
Salwa Nassar

Ain Shams Engineering Journal, 2012

In this paper, a dynamic load-balancing scheme is developed for implementation in an agent-based distributed system. Load balancing is achieved via agent migration from heavily loaded nodes to lightly loaded ones. The credit based concept is used for the dual objective of: (1) the selection of candidate agents for migration, and (2) the selection of destination nodes. This represents an elaboration of previous research work aimed at the selection of agents only. Multiple linear regression is used to achieve our dual objective. A complexity analysis for the proposed system operation is performed, the complete operation is O(n 2 ) where n is the number of agents on the local host.

View PDFchevron_right

Related topics

  • Cluster Computing

    • Cited by

    A clustering-based knowledge discovery process for data centre infrastructure management
    Jose Luis Bosque

    The Journal of Supercomputing, 2016

    Data Center Infrastructure Management (DCIM) is the integration of information technology and facility management disciplines to centralise monitoring and management in data centers. One of the most important problems of DCIM tools is the analysis of the huge amount of data obtained from the real time monitoring of thousands of resources. In this paper, an adaptation of the Knowledge Discovery Process (KDD) for dealing with the data analysis in DCIM tools is proposed. A case of study based on monitoring and labelling of nodes of a High Performance Computing (HPC) data center in real time is presented. This shows that characterising the state of the nodes according to a reduced and relevant set of metrics is feasible and its outcome directly usable, simplifying consequently the decision-making process in these complex infrastructures.

    View PDFchevron_right
    Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities
    Bhupesh Kumar Dewangan

    Applied Sciences

    The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce ...

    View PDFchevron_right
    Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm
    nitin thapliyal

    International Journal of Electrical and Electronics Research

    Cloud computing relies on the collection and distribution of services from internet-based data centers. With the large resource pool available in internet wide range of users are accessing the cloud. Load balance is important feature involving resource allocation to prevent overloading of any system or optimal use of resources. Major load in cloud network are concerned with CPU, memory and network. This cloud computing aspect has not yet earned too much coverage. Although load balancing is an important feature for cloud computing, concurrent computing etc. In these areas, several algorithms were suggested to solve load balance problem. However, it does recommend very few cloud computing algorithms. Given that cloud storage differs considerably from all other environments, particular load balancing algorithm should will built in sort to serve its needs. This work proposes novel load-balancing algorithm based on artificial bee colony algorithm and load balancing min-min scheduling alg...

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved