A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Mobile Cloud Computing Based Technologies for Enhancing E-learning Content Delivery and Sharing in Higher Learning Institutions in Tanzania using Learner-Centered Approach

Electronic learning (E-learning) in Higher Learning Institutions (HLIs) offers a cost-effective teaching and learning that support social interactivity, flexibility, context sensitivity, and active participation of learners in learning activities. The objective of this study was to investigate the challenges facing the traditional E-learning tools and leverage the advanced capacity of Mobile Cloud Computing (MCC) to enhance E-learning service delivery and sharing of learning resources focusing in learner-centered approach. Also, the evolvement of mobile computing devices such as smartphones, Personal Digital Assistance (PDA), and laptops owned by learners bring prospects in overcoming the inherent challenges facing HLIs in developing countries such as shortage of computer laboratories and network resources.Consequently, this study proposes MCC-based E-learning content delivery and sharing to augment higher learning institutions with limited resource setting in developing countries. The main benefits of MCC-based E-learning include, first, augment traditional LMS by provisioning abundant processing capacity and storage in the cloud that guarantee unlimited learning materials available for learners and instructors; Second, improves performance in local Learning Management System (LMS) servers by outsourcing execution and storage into the cloud especially when resource-intensive E-learning contents such as games, Virtual Reality (VR), and video streaming are used for learning; third, supports multi-platforms to execute the workload of various E-learning applications in the cloud which is potential for E-learning resource sharing; and fourth, guarantee cost-effective E-learning content delivery and sharing. Keywords: Mobile cloud computing, E-learning, content delivery, Learner-centered learning DOI: 10.7176/JIEA/13-2-03 Publication date:March 31st 202

Self-Adaptive Provisioning of Virtualized Resources in Cloud Computing

Abstract-Although cloud computing has gained sufficient popularity recently, there are still some key impediments to enterprise adoption. Cloud management is one of the top challenges. The ability of on-the-fly partitioning hardware resources into virtual machine(VM) instances facilitates elastic computing environment to users. But the extra layer of resource virtualization poses challenges on effective cloud management. The factors of time-varying user demand, complicated interplay between co-hosted VMs and the arbitrary deployment of multi-tier applications make it difficult for administrators to plan good VM configurations. In this paper, we propose a distributed learning mechanism that facilitates self-adaptive virtual machines resource provisioning. We treat cloud resource allocation as a distributed learning task, in which each VM being a highly autonomous agent submits resource requests according to its own benefit. The mechanism evaluates the requests and replies with feedbacks. We develop a reinforcement learning algorithm with a highly efficient representation of experiences as the heart of the VM side learning engine. We prototype the mechanism and the distributed learning algorithm in an iBalloon system. Experiment results on an Xen-based cloud testbed demonstrate the effectiveness of iBalloon. The distributed VM agents are able to reach near-optimal configuration decisions in 7 iteration steps at no more than 5% performance cost. Most importantly, iBalloon shows good scalability on resource allocation by scaling to 128 correlated VMs

Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

Efficient cloud computing system operation strategies

Cloud computing systems have emerged as a new paradigm of computing systems by providing on demand based services which utilize large size computing resources. Service providers offer Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) to users depending on their demand and users pay only for the user resources. The Cloud system has become a successful business model and is expanding its scope through collaboration with various applications such as big data processing, Internet of Things (IoT), robotics, and 5G networks. Cloud computing systems are composed of large numbers of computing, network, and storage devices across the geographically distributed area and multiple tenants employ the cloud systems simultaneously with heterogeneous resource requirements. Thus, efficient operation of cloud computing systems is extremely difficult for service providers. In order to maximize service providers\u27 profit, the cloud systems should be able to serve large numbers of tenants while minimizing the OPerational EXpenditure (OPEX). For serving as many tenants as possible tenants using limited resources, the service providers should implement efficient resource allocation for users\u27 requirements. At the same time, cloud infrastructure consumes a significant amount of energy. According to recent disclosures, Google data centers consumed nearly 300 million watts and Facebook\u27s data centers consumed 60 million watts. Explosive traffic demand for data centers will keep increasing because of expansion of mobile and cloud traffic requirements. If service providers do not develop efficient ways for energy management in their infrastructures, this will cause significant power consumption in running their cloud infrastructures. In this thesis, we consider optimal datasets allocation in distributed cloud computing systems. Our objective is to minimize processing time and cost. Processing time includes virtual machine processing time, communication time, and data transfer time. In distributed Cloud systems, communication time and data transfer time are important component of processing time because data centers are distributed geographically. If we place data sets far from each other, this increases the communication and data transfer time. The cost objective includes virtual machine cost, communication cost, and data transfer cost. Cloud service providers charge for virtual machine usage according to usage time of virtual machine. Communication cost and transfer cost are charged based on transmission speed of data and data set size. The problem of allocating data sets to VMs in distributed heterogeneous clouds is formulated as a linear programming model with two objectives: the cost and processing time. After finding optimal solutions of each objective function, we use a heuristic approach to find the Pareto front of multi-objective linear programming problem. In the simulation experiment, we consider a heterogeneous cloud infrastructure with five different types of cloud service provider resource information, and we optimize data set placement by guaranteeing Pareto optimality of the solutions. Also, this thesis proposes an adaptive data center activation model that consolidates adaptive activation of switches and hosts simultaneously integrated with a statistical request prediction algorithm. The learning algorithm predicts user requests in predetermined interval by using a cyclic window learning algorithm. Then the data center activates an optimal number of switches and hosts in order to minimize power consumption that is based on prediction. We designed an adaptive data center activation model by using a cognitive cycle composed of three steps: data collection, prediction, and activation. In the request prediction step, the prediction algorithm forecasts a Poisson distribution parameter lambda in every determined interval by using Maximum Likelihood Estimation (MLE) and Local Linear Regression (LLR) methods. Then, adaptive activation of the data center is implemented with the predicted parameter in every interval. The adaptive activation model is formulated as a Mixed Integer Linear Programming (MILP) model. Switches and hosts are modeled as M/M/1 and M/M/c queues. In order to minimize power consumption of data centers, the model minimizes the number of activated switches, hosts, and memory modules while guaranteeing Quality of Service (QoS). Since the problem is NP-hard, we use the Simulated Annealing algorithm to solve the model. We employ Google cluster trace data to simulate our prediction model. Then, the predicted data is employed to test adaptive activation model and observed energy saving rate in every interval. In the experiment, we could observe that the adaptive activation model saves 30 to 50% of energy compared to the full operation state of data center in practical utilization rates of data centers. Network Function Virtualization (NFV) emerged as a game changer in network market for efficient operation of the network infrastructure. Since NFV transforms the dedicated physical devices designed for specific network function to software-based Virtual Machines (VMs), the network operators expect to reduce a significant Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). Softwarized VMs can be implemented on any commodity servers, so network operators can design flexible and scalable network architecture through efficient VM placement and migration algorithms. In this thesis, we study a joint problem of Virtualized Network Function (VNF) resource allocation and NFV-Service Chain (NFV-SC) placement problem in Software Defined Network (SDN) based hyper-scale distributed cloud computing infrastructure. The objective of the problem is minimizing the power consumption of the infrastructure while enforcing Service Level Agreement (SLA) of users. We employ an M/G/1/K queuing network approximation analysis for the NFV-SC model. The communication time between VNFs is considered in the NFV-SC placement because it influences the performance of NFV-SC in the highly distributed infrastructure environment. The joint problem is modeled by a Mixed Integer Non-linear Programming (MINP) model. However, the problem is intractable in large size infrastructures due to NP-hardness of the problem. We therefore propose a heuristic algorithm which splits the problem into two sub-problems: resource allocation and the NFV-SC embedding. In the numerical analysis, we could observe that the proposed algorithm outperforms the traditional bin packing algorithms in terms of power consumption and SLA assurance. In this thesis, we propose efficient cloud infrastructure management strategies from a single data center point of view to hyper-scale distributed cloud computing infrastructure for profitable cloud system operation. The management schemes are proposed with various objectives such as Quality of Service (Qos), performance, latency, and power consumption. We use efficient mathematical modeling strategies such as Linear Programming (LP), Mixed Integer Linear Programming (MILP), Mixed Integer Non-linear Programming(MINP), convex programming, queuing theory, and probabilistic modeling strategies and prove the efficiency of the proposed strategies through various simulations

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

[EN] Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.The results of this work have been partially supported by ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing), funded by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154.Alfonso Laguna, CD.; Caballer Fernández, M.; Calatrava Arroyo, A.; Moltó, G.; Blanquer Espert, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing. 17(1):191-204. https://doi.org/10.1007/s10723-018-9449-zS191204171Buyya, R.: High Performance Cluster Computing: Architectures and Systems. 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Evaluation of Cloud-Based Cyber Security System

Cloud-based cyber security systems leverage the power of cloud computing to protect digital assets from cyber threats. By utilizing remote servers and advanced algorithms, these systems provide real-time monitoring, threat detection, and incident response. They offer scalable solutions, enabling businesses to adapt to evolving threats and handle increasing data volumes. Cloud-based security systems provide benefits such as reduced infrastructure costs, continuous updates and patches, centralized management, and global threat intelligence. They protect against various attacks, including malware, phishing, DDoS, and unauthorized access. With their flexibility, reliability, and ease of deployment, cloud-based cyber security systems are becoming essential for organizations seeking robust protection in today's interconnected digital landscape. The research significance of cloud-based cyber security systems lies in their ability to address the growing complexity and scale of cyber threats in today's digital landscape. By leveraging cloud computing, these systems offer several key advantages for researchers and organizations: Scalability: Cloud-based systems can scale resources on-demand, allowing researchers to handle large volumes of data and analyze complex threat patterns effectively. Cost-efficiency: The cloud eliminates the need for extensive on-premises infrastructure, reducing costs associated with hardware, maintenance, and upgrades. Researchers can allocate resources based on their needs, optimizing cost-effectiveness. Real-time monitoring and threat detection: Cloud-based systems provide real-time monitoring of network traffic, enabling quick identification of suspicious activities and potential threats. Researchers can leverage advanced analytics and machine learning algorithms to enhance threat detection capabilities. Collaboration and knowledge sharing: Cloud platforms facilitate collaboration among researchers and organizations by enabling the sharing of threat intelligence, best practices, and research findings. Compliance and regulatory requirements: Cloud platforms often offer built-in compliance features and tools to meet regulatory requirements, assisting researchers in adhering to data protection and privacy standards. Overall, the research significance of cloud-based cyber security systems lies in their ability to provide scalable, cost-effective, and advanced security capabilities, empowering researchers to mitigate evolving cyber threats and protect sensitive data and systems effectively. We will be using Weighted Product Methodology (WPM) which is a decision-making technique that assigns weights to various criteria and ranks alternatives based on their weighted scores. It involves multiplying the ratings of each criterion by their corresponding weights and summing them up to determine the overall score. This method helps prioritize options and make informed decisions in complex situations. Taken of Operational, Technological, Organizational Recorded Electronic Delivery, Recorded Electronic Deliver, Blockchain technology, Database security, Software updates, Antivirus and antimalware The Organizational cyber security measures comes in last place, while Technological cyber security measures is ranked top and Operational measures comes in between the above two in second place. In conclusion, a cloud-based cyber security system revolutionizes the way organizations safeguard their digital assets. By utilizing remote servers, advanced algorithms, and real-time monitoring, it offers scalable and robust protection against evolving threats. With features like threat detection, data encryption, and centralized management, it ensures enhanced security, agility, and efficiency. Embracing a cloud-based approach empowers organizations to stay ahead in the ever-changing landscape of cyber security, effectively safeguarding their critical data and infrastructure

Organization support for cloud computing implementation success in education system: scale development and validity in Delphi

Cloud computing (CC) support for learning systems has been viewed as one of the most discussed issues that promise to modernize computing by providing visualized resources as a service over the internet. To be stable in cloud computing acquisition requires an education institution to address many of the same concerns they face in implementing an Information System (IS) service. Currently, there is still lack of CC implementation standard and organizational support that impacted VLE system performance. Previous research has reported that the influence of the CC implementation decision depends on the impact of various factors studied. Nonetheless, organizational support is the least factor mentioned especially studies from Malaysia. Thus, the main purpose of this study is to develop a validated scale of organizational support in implementation decision activities towards CC implementation success. In this paper, the Delphi process adopted to measure consensus among nominal group technique (also known as the expert panel). Key methodological issues in using the methods are discussed, along with the distinct contribution of consensus methods as aids to decision making in education service development. The study has adapted stages of proses flow of scale development and validation of measurement items according to legitimate measures in the Delphi technique. The measurement scales formed are based on literature review and field studies conducted to increase the reliability and validity values. Organizational support constructs were divided into top management support, firm size, awareness, Technology Readiness and cost effectiveness. A total of 5 items have been successfully set up for further validatio