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

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective