Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address  large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably

Autonomic system for optimal resource management in cloud environments

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud computing is a large-scale distributed computing paradigm driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Considering the lack of resources in cloud environments and fluctuating customer demands, cloud providers require to balance their resource load and utilization, and automatically allocate scarce resources to the services in an optimal way to deliver high performance physical and virtual resources and meet Service Level Agreement (SLA) criteria while minimizing their cost. This study proposes an Autonomic System for Optimal Resource Management (AS-ORM) that addresses three main topics of resource management in the cloud environment including: (1) resource estimation, (2) resource discovery and selection, and (3) resource allocation. A fuzzy Workload Prediction (WP) sub-system and a Multi-Objective Task Scheduling optimization (MOTS) sub-system are developed to cover the first two aforementioned topics. The WP sub-systems estimates Virtual Machines’ (VMs’) workload and resource utilization, and predicts Physical Machines’ (PMs) hotspots. The MOTS sub-system determines the optimal pattern to schedule tasks over VMs considering task transfer time, task execution cost/time, the length of the task queue of VMs and power consumption. To optimize the third topic in resource management, resource allocation, VM migration that is the current solution for optimizing physical resources allocation to VMs and load balancing among PMs, is investigated in this study. VM migration has been applied to system load balancing in cloud environments by memory transfer, suspend/resume migration, or live migration for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time- and cost-consuming as it requires large size files or memory pages to be transferred, and consumes a huge amount of power and memory for the origin and destination PMs especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, a Fuzzy Predictable Task-based System Load Balancing (FP-TBSLB) sub-system is developed that avoids VM migration and achieves system load balancing by transferring extra workload from a poorly performing VM to other compatible VMs with more capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, FP-TBSLB sub-system applies WP sub-system to not only predict the performance of VMs, but also determine a set of appropriate VMs that have the potential to execute the extra workload imposed on the poorly performing VMs. In addition, FP-TBSLB sub-system employs the MOTS sub-system to migrate the extra workload of poorly performing VMs to the compatible VMs. The AS-ORM system is evaluated using a VMware-vSphere based private cloud environment with VMware ESXi hypervisor. The evaluation results show the benefit of the AS-ORM in reducing the time taken for the load balancing process compared to traditional approaches. The application of this system has the added advantage that the VMs will not be slowed down during the migration process. The system also achieves significant reduction in memory usage, execution time, job makespan and power consumption. Therefore, the AS-ORM dramatically increases VM performance and reduces service response time. The AS-ORM can be applied in the hypervisor layer to optimize resource management and load balancing which boosts the Quality of Service (QoS) expected by cloud customers

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications