Papers by Mustafa Daraghmeh
Ensemble Learning for Predicting Task Connectivity Over Time in Cloud Data Centers
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Incorporating Data Preparation and Clustering Techniques for Workload Segmentation in Large-Scale Cloud Data Centers
2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)
Developing Machine Learning and Deep Learning Models for Host Overload Detection in Cloud Data Center
2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
Cloud service providers need to deal with many challenging problems to provide services such as g... more Cloud service providers need to deal with many challenging problems to provide services such as guaranteeing Service Level Agreement (SLA) with clients and minimizing energy consumption of the data centers. Cloud resource management tackles these problems using different techniques such as consolidation. In consolidation, the migration of VMs depends on host overloading which evaluates the state of a host (overloaded or under loaded) before migrating a VM. Forecasting the state of a host accurately and in time is crucial to guarantee SLA with clients and reduce energy consumption in a datacenter. CPU utilization is used in the present work to determine the state of the host. In this paper, we propose a methodology to evaluate how eight different algorithms including machine and deep learning, which forecast the servers' CPU utilization, impact the migration of VMs affecting SLA and energy consumption in datacenters. The outcomes enable decision-makers to take better decisions which result in the reduction of energy consumption and improvement in SLA with clients. The performances of the models are evaluated with random and real users' workloads. We propose a methodology to develop, evaluate and implement host overload detection models in cloud data center based on machine learning (ML) and deep learning (DL) techniques. We validate the methodology and demonstrate that Long Short-Term Memory (LSTM) and Simple Moving Average (SMA) provide excellent results with on average over 2.5 times reduction of Energy and SLA Violation (ESV) metric when compared to other algorithms. Also, LSTM is found to be the most robust solution to predict CPU utilization using unseen data.
Multi-Agent Based Dynamic Resource Provisioning and Monitoring In Cloud Computing Systems
The cloud computing paradigm provides a shared pool of resources and services with different mode... more The cloud computing paradigm provides a shared pool of resources and services with different models delivered to the customers through the Internet via an on-demand dynamically-scalable form charged using a pay-per-use mod el. The main problem we tackle in this paper is to optimize the resource provisioning task by shortening the completion ti me for the customers’ tasks while minimizing the associated co st. This study presents the Dynamic Resources Provisioning and Monitoring (DRPM) system, a multi-agent system to manage the cloud provider’s resources while taking into account the customers’ quality of service (QoS) requirements as determined by the service-level agreement (SLA). Moreover, DRPM inclu des a new Virtual Machine (VM) selection algorithm called the Host Fault Detection (HFD) algorithm. The proposed DRPM system i s evaluated using the CloudSim tool. The results show that usi ng the DRPM system increases resource utilization and decreas e power consumption while avoidin...
Regression-Based Dynamic Provisioning and Monitoring for Responsive Resources in Cloud Infrastructure Networks
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), 2018
Cloud computing model is the most complex computing model that requires implementing effective te... more Cloud computing model is the most complex computing model that requires implementing effective techniques to manage infrastructure resources of datacenters. Unproductive tasks scheduling can lead to an increase in the operational cost of cloud provider side, which in turn increases the cloud services cost at cloud consumer side. One of the effective techniques to address these issues in cloud datacenters is the elasticity by allowing dynamic resource provisioning based on the current demand and varying workload running upon virtual machines (VMs) over time. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, the dynamic resource provisioning due to the growing service demand and higher quality of service requirements of the users can cause a violation of service level agreement. In this paper, we propose a model based on linear regression to manage and reformulate cloud users requests and dynamicall...
Using Logistic Regression to Improve Virtual Machines Management in Cloud Computing Systems
2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 2017
Cloud computing (CC) is a computing model that enables its customers to access a shared pool of r... more Cloud computing (CC) is a computing model that enables its customers to access a shared pool of resources (e.g., storage, network, servers, etc.) through the Internet with a pay-per-use pricing model. Different service models are employed in CC including the Platform-as-a-Service (PaaS) model, in which the costumers request a certain set of resources and the cloud service providers provide these resources in the form of a virtual machine (VM) running on one of the thousands of hosting servers or physical machines (PMs) of a data center. Where to "place" VMs, how to "execute" them and whether there is a need to "move/migrate" them are important decisions that affect the overall resource utilization and power consumption in the hosting data center. VM consolidation is a technique of migrating or consolidating VMs to PMs in order to prevent the PMs from being overloaded or reduce the number of active PMs and increase their utilization. Consolidation techni...
IEEE Access, 2020
Resource management for cloud computing environments that are characterized by many layers emerge... more Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top–bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put ...
Live VM Migration Across Cloud Data Centers
Live VM migration is a technique that consists of a selection process and a migration process to ... more Live VM migration is a technique that consists of a selection process and a migration process to migrate a VM from one host to another in the same data center without changing the IP address, or in a different data center with necessity to the VM to get a new IP address. The changing of IP address results into a mobility problem, which may render the service unreachable. In this paper, we propose a system model that selects data center randomly for VM placement while reducing this IP address reconfiguration. A new metric is proposed to indicate number of users that need IP reconfiguration. We extended CloudSim to simulate our work to identify the number of IP reconfigurations required for VM migration across the data centers on random workload.
There are many hot topics related to information retrieval paradigm, and one of these important f... more There are many hot topics related to information retrieval paradigm, and one of these important fields is Automatic text indexing that aims to m ake process of online retrieving documents easier for the web searchers. In this paper we intend to introduced a comprehensive study on Indexing Arabic Documents, since there have been little works deals with. The introduced papers here addressed this prob lem from deferent views, some deals with single - term indexing while others deal with phrase indexing, other researchers made comparisons between deferent techniques and gave us preferability to one against others based on some experimental results. On the other hand, some papers proposed new technique or made some enhancements on existing ones either depends on statistical or un - statistical methods. The rest of papers proposed tools as key - terms extractors to be used in text indexing. Till now there are no optimal suggested solutions that solve the indexing problem that could be con...
Local Regression Based Box-Cox Transformations for Resource Management in Cloud Networks
Understanding and implementing approaches to efficiently manage the infrastructure resources of c... more Understanding and implementing approaches to efficiently manage the infrastructure resources of cloud data centers has become essential. Energy consumption and disorganized resource usage can expensively produce an impressive increase in the operational cost of cloud services. This increase turns to a remarkable rise in the cloud customers’ invoices. Providing an exceptional quality of service running on well-organized resources with efficient energy is a critical issue that needs to be carefully considered by both industrial and academics. Although, the cloud providers are trying to deliver sufficient quality of services to their customers with a comparatively proper cost. One of the effective techniques to address these issues in cloud data centers is a dynamic virtual machine consolidation. This technique intends to improve energy efficiency and resource utilization by reallocating multiple virtual machines including various workload among available hosts and turning the unutiliz...
Linear and Logistic Regression Based Monitoring for Resource Management in Cloud Networks
2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)
Understanding and implementing the effective techniques to manage the infrastructure resources of... more Understanding and implementing the effective techniques to manage the infrastructure resources of cloud datacenter has recently become important. The energy consumption and ineffective resource utilization can lead to an increase in the operational cost of cloud provider side, which in turn increases the cloud services cost at cloud consumer side. One of the effective techniques to address these issues in cloud datacenters is the server consolidation by allowing multiple virtual machines (VMs) include varying workload to host in a single physical machine. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, consolidating the virtual machines due to varying workload in cloud applications can cause a violation of service level agreement. In this paper, we propose a model based on linear and logistic regression to detect overloaded hosts by dynamically generating rules based on historical data of hosts and datacenter in order to update association functions to address and adapt the changes of different types of workloads running on the cloud provider datacenter. The experiments and simulation results based on dynamic workloads show the proposed algorithm significantly outperforms the other competitive host detection algorithms.
A Power Management Approach to Reduce Energy Consumption for Edge Computing Servers
2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)
With the rapid development of edge computing and its applications, requests to edge servers is ex... more With the rapid development of edge computing and its applications, requests to edge servers is expected to grow, resulting in higher edge network energy consumption. This in essence would also result in higher operational costs for running edge applications. Furthermore, service providers try to manage their resources efficiently to provide appropriate quality of services to their customers while reducing service costs. To appropriately manage resources, it is necessary to apply useful models to measure energy consumption in the edge network. The linear relationship between energy consumption and CPU utilization is one powerful modeling method used to compute the energy consumption of edge network servers. The method calculates the power consumption of a server based on its CPU utilization during run-time. In this paper, we propose a linear power model for the EdgeCloudSim simulator to measure the energy consumption of edge network servers. Moreover, we introduce a simple dynamic power management model used to minimize power consumption in the edge network by switching the edge servers on and off based on provisioned application needs. The experimental and simulation results show a notable reduction in the total energy consumption when applying the proposed simple model on two different orchestration policies to manage the edge network servers.
Time Series Forecasting using Facebook Prophet for Cloud Resource Management
2021 IEEE International Conference on Communications Workshops (ICC Workshops)
The heterogeneous nature of workloads running in cloud environments makes future resource usage p... more The heterogeneous nature of workloads running in cloud environments makes future resource usage prediction a complicated problem. Virtual machines can be described in five types of resource utilization patterns: steady, trending, seasonal, cyclic, and bursty behavior. Understanding these usage patterns and behaviors can enhance resource management on cloud data centers, especially VM scheduling, power management, and server health management systems. This paper applies the Facebook Prophet forecast framework on Microsoft Azure VM workload to predict future resource utilization required by the running tasks. We conclude that utilizing data preprocessing and transformation on real virtual machine traces, and incorporating an automatic model hyperparameter tuning process, can significantly increase forecasting accuracy with an average percentage change of over 85%. Furthermore, cloud providers can learn from their data center workloads and employ various forecasting models to gain substantial improvements in cost-efficient resource management.
Energy efficient dynamic resource management in cloud computing based on logistic regression model and median absolute deviation
Sustainable Computing: Informatics and Systems
Towards Improving Resource Management in Cloud Systems using a Multi-Agent Framework
There are many hot topics related to information retrieval paradigm, and one of these important f... more There are many hot topics related to information retrieval paradigm, and one of these important fields is Automatic text indexing that aims to make process of online retrieving documents easier for the web searchers. In this paper we intend to introduced a comprehensive study on Indexing Arabic Documents, since there have been little works deals with. The introduced papers here addressed this problem from deferent views, some deals with single-term indexing while others deal with phrase indexing, other researchers made comparisons between deferent techniques and gave us preferability to one against others based on some experimental results. On the other hand, some papers proposed new technique or made some enhancements on existing ones either depends on statistical or un-statistical methods. The rest of papers proposed tools as key-terms extractors to be used in text indexing. Till now there are no optimal suggested solutions that solve the indexing problem that could be considered ...
International Journal of Cloud Computing, 2016
With the goal of efficient sharing of resources and services, the cloud computing paradigm has ga... more With the goal of efficient sharing of resources and services, the cloud computing paradigm has gained a lot of interest recently. Using a pay-per-use model, the customers can access the available resources and services in an on-demand dynamically-scalable manner. This work focuses on improving the resource utilization by optimizing the resource provisioning which leads to many benefits such as reduced cost, improved customers experience, shortened completion time, etc. These objectives are achieved by utilizing a multi-agent framework in which different agents are responsible for different tasks including the monitoring of customers (behavior, resource usage patterns and quality of service (QoS) requirements as stated in the service level agreement (SLA)) and available resources as well as the provisioning of resources based on customers requests. Moreover, we introduce the concept of TaskFlow which allows a more elastic provisioning of the resources to match the customer real usage of the resources. The proposed system is implemented and tested on the CloudSim simulator and the results show it increases resource utilization and decreases power consumption while avoiding SLA violations. The results also show that the introduction of the concept of TaskFlow into our proposed system leads to more resource saving but with a higher risk of SLA violations.
Cluster Computing, 2015
The cloud computing paradigm provides a shared pool of resources and services with different mode... more The cloud computing paradigm provides a shared pool of resources and services with different models delivered to the customers through the Internet via an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem we tackle in this paper is to optimize the resource provisioning task by shortening the completion time for the customers' tasks while minimizing the associated cost.
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Papers by Mustafa Daraghmeh