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The federated cloud paradigm is getting popular for its dynamic allocation of resources, processing power, improved performance, and scalability. A federated cloud lets multiple cloud service providers collaborate and delegate services. However, this delegation requires pre-approval from domain administrators which can create an administrative bott...
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... is more suitable, because it provides a sense of authority from an organization. Also, it can help in keeping track of delegatees and any organizational proxy can revoke the delegation, if some misuse is observed. Different delegation characteristics and their selected parametric values for federated cloud are presented in the framework (Fig. 1). Temporary delegation was selected in the permanence characteristic so the delegator can delegate his/ her rights for a defined time period. Further, monotonic delegation was selected because the resource owner should continue to exercise the delegated rights. In totality characteristic, partial delegation was selected because the ...
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... Due to the proliferative usage, cloud federation is the future of CC, Internet of Things (IoT), mobile CC and big data applications [7][8][9][10][11]. The federated resources enhance CSPs' service quality, reliability and cost benefits [12]. ...
A Federated cloud is a composition of several clouds where a single federated cloud manager (FCM) is responsible for communication with the cloud service providers (CSPs) of associated clouds to accomplish the task of resource management. Finding optimal schedules for the incoming workloads from users is a highly complex task, as these workloads are expected to be completed under different deadlines. The existing frameworks for workload scheduling in federated cloud faced serious drawbacks such as inefficiency, unable to meet the deadlines assigned by users, etc. To overcome such drawbacks, this work introduces a new and effective strategy based on an optimization algorithm to achieve optimal scheduling of workloads. The proposed architecture involves a single FCM to identify the load in different clouds through communication with CSPs. A load level calculator (LLC) and a workload partitioning module (WPM) are maintained by the FCM to analyze the load in each cloud. Further, based on the load factor (LF) computed, the incoming workloads are partitioned into sub-queues of different sizes and are forwarded to the main clouds. The respective CSPs of the clouds maintain a cloud workload queue (CWQ) to locate the workloads and analyze the resource requirements. The CSP executes a scheduler based on hybrid flow-directed whale optimization (HFDWO) to find the optimal VMs in the data centre (DC) that can run the incoming workloads in the queue. The workloads are scheduled accordingly, and evaluations are conducted through simulations in the CloudSim tool. The performance of the approach is analyzed using the GWA T-12 Bitbrains dataset under different metrics. The overall improvement attained by the proposed approach compared to the existing frameworks is 25% in terms of SLA violation rate, 12% in terms of execution cost, 11% in terms of resource utilization, 33% in terms of makespan, 19% in terms of throughput and 28% in terms of response time.
... Only the federation of clouds can provide practically limitless processing capacity, parallel storage, and scale economies said . The restricted physical resources within a single provider's resource pool are the reason for cloud federation [11], [12]. Any cloud computing framework is composed of three main components: client, datacenters, and distributed servers. ...
With the development of cloud computing, load balancing issues have substantially become prominent, which is of concern as well to the industry as to academia. Load balancing contributes to a high degree of customer satisfaction and the use of resources in the cloud by verifying an accurate, secure, and equitable allocation of all computing resources. In this paper, we review the research progress on load balancing issues from the perspective of several incremental and dynamic algorithms in federated cloud computing. First, we present some obstacles to load balancing algorithms in cloud computing and suggest a corresponding incremental algorithm for load balancing in a federated cloud. Last, we offer current challenges and highlight possible future directions for research in this field.KeywordsFederated cloudCloud computingAnt colony optimizationLoad balancing
Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring.
Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management. © 2022, Telkomnika (Telecommunication Computing Electronics and Control). All rights reserved.
The fifth generation (5G) communication technology is a standard for the broadband communication applicable for mobile communications. The entire network for any application is subdivided into many sub-geographical areas termed as cells. All these cells are connected through radio waves using internet or telecommunication network with the deployment of antennas in each cell. The antennas used for the 5G communications are controlled by special hardware implementation that increases the complexity of the network. To reduce the complexity of this hardware implementation, it is necessary to replace the hardware control by a pre-programmed software control.
