Open Source Cloud Management Platforms: A Review

Ismaeel, Salam; Miri, Ali; Chourishi, Dharmendra; Dibaj, S. M. Reza

doi:10.1109/cscloud.2015.84

Cited by 20 publications

(5 citation statements)

References 18 publications

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“…DCIM profiles the power consumed by every single part of the hardware in the data centre [44]. Cloud monitoring tools and platforms properties, issues, analyzing, and comparisons surveys can be found in [86][87][88][89][90][91][92].…”

Section: Resources State Subsystemsmentioning

confidence: 99%

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

Ismaeel¹

2021

Preprint

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<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

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Section: Resources State Subsystemsmentioning

confidence: 99%

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

Ismaeel¹

2021

Preprint

Self Cite

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“…& An open source community (env5) develops and shares applications, platforms, experiences and best practices. Organizations being actively involved in an open source community can benefit from the accumulated know-how (Floerecke and Lehner 2018b;Ismaeel et al 2015).…”

Section: Finalisation Of the Pace Modelmentioning

confidence: 99%

Cloud computing ecosystem model: evaluation and role clusters

2020

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Several significantly different attempts have been undertaken to visualize the complex and opaque cloud computing ecosystem by a descriptive model. Recently, scholars have developed a new role-based cloud ecosystem model (Passau Cloud Computing Ecosystem Model (PaCE model)) based on a systematic analysis and synthesis of the previous models. However, its scientific evaluation is still outstanding. The main goal is therefore to conduct an evaluation regarding its structural equivalence and completeness compared to the real cloud business according to the design science research paradigm. The second goal is to demonstrate the model’s usefulness by using it as an instrument for the identification of role clusters, which are frequently covered by organizations. To this end, a cross-sectional analysis is conducted, in which 758 organizations are manually analysed and matched with the model’s current roles. As a result, roles have been newly added, removed, broken down into sub-roles, abstracted to a meta-role and renamed. The cluster analysis unveiled five role clusters. Whereas some roles were predominantly found as single role, further roles were exclusively or very frequently found in combination with others. Overall, the revised model creates, compared to the common rather technically orientated market overviews by market research institutes, a detailed categorization schema helping to understand and investigate the organizations belonging to the cloud ecosystem.

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“…Hence, cloud management software was regularly utilized by organization for simplifying the task of managing their cloud. The definition of cloud computing management software is "Software and technologies utilized by public or enterprise organization to build and operate in-house or on-premise cloud platform and infrastructure, the tool in cloud management guaranteed resources of cloud computing are utilized or distributed effectively and able to appropriately interacting external services and users via common interface" [9]. The cloud management software has two deployment categories; it can be deployed using open source software such as OpenStack, Eucalyptus and CloudStack or using closed-source solutions such as Citrix XenServer and VMware vCenter One of the core functions of the cloud computing software is to manage and orchestrate the operation of physical hardware which pools it resources for the cloud platform [10].…”

Section: A Cloud Management Softwarementioning

confidence: 99%

API Vulnerabilities In Cloud Computing Platform: Attack And Detection

Ariffin¹,

Ibrahim²,

Kasiran³

2020

IJETT

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Nowadays most of cloud management software such as OpenStack and CloudStack provide an API to facilitate the communication and exchange of data between users, application, cloud components and infrastructure. Due to complexity of cloud management software implementation, the provided API has vulnerabilities which can be exploited by malicious party. Once exploited, it can cause security issue and disrupt the availability of services running on the cloud infrastructure. Hence; it is importance to address cloud API security by identifying potential threats, demonstrating how such threats could be exploited and how to detect such threat. This paper presents the topic of API Vulnerabilities in Cloud Computing Platform: Attack and Detection. We will discuss the vulnerabilities of the API in cloud management software. Based on these vulnerabilities, this paper will demonstrate how eavesdropping on cloud API authentication services and API exhaustion attack can be initiated. To address the threat due to the vulnerabilities of the API, we need to detect attack which exploits the vulnerabilities. Thus this paper also proposes and demonstrates methods to detect such attack effectively. Method to detect ongoing API exhaustion attack will be based on AD3 algorithm. From the experiments result, it shows that attacks on the cloud platform AP can be detected effectively.

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Open Source Cloud Management Platforms: A Review

Cited by 20 publications

References 18 publications

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

Cloud computing ecosystem model: evaluation and role clusters

API Vulnerabilities In Cloud Computing Platform: Attack And Detection

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