Adaptive prediction models for data center resources utilization estimation

Accurate estimation of data center resource utilization is a challenging task due to multi-tenant co-hosted applications having dynamic and time-varying workloads. Accurate estimation of future resources utilization helps in better job scheduling, workload placement, capacity planning, proactive auto-scaling, and load balancing. The inaccurate estimation leads to either under or over-provisioning of data center resources. Most existing estimation methods are based on a single model that often does not appropriately estimate different workload scenarios. To address these problems, we propose a novel method to adaptively and automatically identify the most appropriate model to accurately estimate data center resources utilization. The proposed approach trains a classifier based on statistical features of historical resources usage to decide the appropriate prediction model to use for given resource utilization observations collected during a specific time interval. We evaluated our approach on real datasets and compared the results with multiple baseline methods. The experimental evaluation shows that the proposed approach outperforms the state-of-the-art approaches and delivers 6% to 27% improved resource utilization estimation accuracy compared to baseline methods.This work is partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitiveness (TIN2015-65316-P and IJCI2016-27485), the Generalitat de Catalunya (2014-SGR-1051), and NPRP grant # NPRP9-224-1-049 from the Qatar National Research Fund (a member of Qatar Foundation) and University of the Punjab, Pakistan.Peer ReviewedPostprint (published version

Workload Analysis of Cloud Resources using Time Series and Machine Learning Prediction

© 2019 IEEE. Most of the businesses now-a-days have started using cloud platforms to host their software applications. A Cloud platform is shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user's requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Auto-scaling supports the cloud service providers achieve the goal of supplying the required resources automatically. It use methods that will calculate the number of requests and decides the resources to release based on workload. The workload consists of some quantity of application program running on the machine and usually some number of users connected to and communicating with the computer's applications. The researchers have used various approaches to perform autoscaling which is a process to predict the workload that is required to handle the end users request and provide required resources as Virtual Machines (VM) disruptively. Along with providing uninterrupted service, the businesses also only pay for the service they use, thus increasing the popularity of Cloud computing. Based on the workload identified the resources are provisioned. The resource provisioning techniques is a model used for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications, and services) required resources are released. In this regard, the aim of this paper is to develop a framework to predict the workload using deep learning which would be able to handle provisioning of cloud resources dynamically. This framework would handle the user request efficiently and allocate the required virtual machines. As a result, an efficient dynamic method of provisioning of cloud services would be implemented supporting both the cloud providers and users

A Smart Edge Computing Resource, formed by On-the-go Networking of Cooperative Nearby Devices using an AI-Offloading Engine, to Solve Computationally Intensive Sub-tasks for Mobile Cloud Services

The latest Mobile Smart Devices (MSDs) and IoT deployments have encouraged the running of “Computation Intensive Applications/Services” onboard MSDs to help us perform on-the-go sub-tasks required by these Apps/Services such as Analysis, Banking, Navigation, Social Media, Gaming, etc. Doing this requires that the MSD have powerful processing resources to reduce execution time, high connectivity throughput to minimise latency and high-capacity battery for power consumption so to not impact the MSD availability/usability in between charges. Offloading such Apps from the host-MSD to a Cloud server does help but introduces network traffic and connectivity overhead issues, even with 5G. Offloading to an Edge server does help, but Edge servers are part of a pre-planned overall computing resource infrastructure, that is tough to predict when demands/rollout is generated by a push from the MSDs/Apps makers and pull by users. To address this issue, this research work has developed a “Smart Edge Computing Resource”, formed on-the-go by the networking of cooperative MSDs/Servers in the vicinity of the host-MSD that is running the computing-intensive App. This solution is achieved by: Developing an intelligent engine, hosted in the Cloud, for profiling “computing-intensive Apps/Services” for appropriately partitioning the overall task into suitable sub-task-chunks so to be executed on the host-MSD together/in association with other available nearby computing resources. Nearby resources can include other MSDs, PCs, iPads and local servers. This is achieved by implementing an “Edge-side Computing Resource engine” that intelligently divides the processing of Apps/Services among several MSDs in parallel. Also, a second “Cloud-side AI-engine” to recruit any available cooperative MSDs and provide the host-MSD with decisions of the best scenario to partition and offload the overall App/Services. It uses a performance scoring algorithm to schedule the sub-tasks to execute on the assisting resource device that has a powerful processor and high-capacity battery power. We built a dataset of 600 scenarios to boost up the offloading decision for further executions, using a Deep Neural Network model. Dynamically forming the on-the-go resource network between the chosen assisting resource devices and the App/Service host-MSD based on the best wireless connectivity possible between them. This is achieved by developing an Importance Priority Weighting cost estimator to calculate the overhead cost and efficiency gain of processing the sub-tasks on the available assisting devices. A local peer-to-peer connectivity protocol is used to communicate, using “Nearby API and/or Post API”. Sub-tasks are offloaded and processed among the participating devices in parallel while results are retrieved upon completion. The results show that our solution has achieved, on average, 40.2% more efficient processing time, 28.8% less battery power consumption and 33% less latency than other methods of executing the same Apps/Services