Scheduling of resources in cloud environments requires design of multiple pattern analysis models that include but are not limited to, inter‐task dependency pattern analysis, make‐span pattern analysis, virtual machine (VM or resource) based capacity analysis, deadline analysis, and VM‐to‐task compatibility analysis. Existing scheduling models are either highly complex, or do not integrate comprehensive analysis modules for efficient scheduling of resources to tasks. Moreover, some of these models showcase limited scalability when applied to large‐scale deployment scenarios. To overcome these issues, this text proposes design of a multimodal bioinspired model to improve resource‐scheduling efficiency with differential task‐level constraints. The proposed model initially collects multimodal information sets about tasks and underlying resources in order to augment analysis efficiency for different task types. These information sets are initially processed by a Grey Wolf Optimization (GWO)‐based scheduling model that assists in grouping tasks based on their make‐span, deadline and dependency levels. The grouped tasks are then scheduled via an incremental learning Elephant Herding Optimization (EHO) model that assists in assigning grouped tasks to capacity‐tuned resources (or VMs). Due to integration of these optimization methods, the proposed model is capable of improving the efficiency of resource scheduling by 8.5%, while reducing computational complexity by 4.3%, while improving the deadline hit ratio by 5.9%, and lowering energy consumption by 1.5% when compared with standard machine learning based scheduling techniques. Due to which the proposed model is capable of deployment for a wide variety of real‐time scheduling scenarios.
Optimal resource utilization and reduced energy consumption have been the primary objectives of cloud data centers as the dependency on cloud platforms is increasing day by day. Consolidating the virtual machines is a standard procedure for addressing the common issues and meeting the objectives. Though the approach seems viable for effective functionality, it is observed that consolidation performed over the permissible limit may result in violating the service level agreements in cloud service providers. When energy conservation is concentrated in the cloud platforms, multiple other factors are neglected or compromised. The supposed strategy for effective virtual machine consolidation must contemplate the parameters such as quality of service, service level agreements, reducing violations, resource distribution, load management, migration overheads, network resource management and other communication protocols. The proposed approach focusses on determining the dynamic load and resource management based on multiple objectives in order to reduce the power consumption. The dynamic load is derived based on a time-series analysis over the distributed load in different time zones. Increment in load distribution owing to virtual machine consolidation and selection is observed for improving the efficiency of consolidations. The load prediction approach along with current load detection has included multiple objectives as desired. The proposed approach, from the experimental analysis, has delivered a promising solution for load prediction, distribution and energy conservation in cloud service providers and optimized the functionalities of users. The energy efficiency was observed to be higher than existing virtual machine consolidation approaches along with effective load sequencing and maintaining the service level agreements.
Virtual machines are deployed to ease the performance, management overhead and access regularities of applications on cloud platforms. Virtual machines are often susceptible to overloading burdens, delays and other hurdles during server consolidation and migration processes. In order to regulate the energy dissipation, monitor the overloading and under loading issues, dynamic consolidation processes are introduced to distribute loads across virtual machines substantially. Consolidation process demands additional computations and resources to reallocate the services from one virtual machine to another, subjected to adherence of Service Level Agreements. The proposed methodology advocates the implementation of a novel architecture for consolidating virtual machines and thus balance the energy and performance parameters. Overall resource requirements, Performance to Power Ratio (PPR) are primary factors in design of proposed Dynamic Weightage algorithm, including the clustering approach with respect to reinforcement learning techniques. Cluster of optimal virtual machines are formed, resources are allocated based on optimal energy and performance expectations. Resource requests from virtual machines are derived into a matching relationship factor to represent the respective hosts with PPR considerations. These estimations also deliver the overall workload incurred during virtual machine consolidation. It is observed that the performance throughout the cluster is maintained to be a nominal with minimal trade-off to energy. The architecture is implemented over an offline platform, which are distributed environments, enabling scalability and improved efficiency of the entire system. The system is validated on CloudSim simulator with datasets retrieved from PlanetLab. From the results obtained, it is noted that energy conservation has yielded up to 47%, with promising quality of service parameters. Results are compared with other state of art algorithms for distributed architectures and heterogeneous environments to prove the efficiency of the system.
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