| 1. |
- Alhamazani, Khalid, et al.
(författare)
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An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art
- 2015
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Ingår i: Computing. - : Springer Science and Business Media LLC. - 0010-485X .- 1436-5057. ; 97:4, s. 357-377
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Tidskriftsartikel (refereegranskat)abstract
- Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite challenging considering a large number of heterogeneous cloud resources. Further, considering the fact that at given point of time, there may be need to change cloud resource configuration (number of VMs, types of VMs, number of appliance instances, etc.) for meet application QoS requirements under uncertainties (resource failure, resource overload, workload spike, etc.). Hence, cloud monitoring tools can assist a cloud providers or application developers in: (i) keeping their resources and applications operating at peak efficiency, (ii) detecting variations in resource and application performance, (iii) accounting the service level agreement violations of certain QoS parameters, and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes. In this paper, we identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools. We further discuss how the aforementioned research dimensions and design issues are handled by current academic research as well as by commercial monitoring tools.
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| 2. |
- Alhamazani, Khalid, et al.
(författare)
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CLAMS : Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework
- 2014
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Ingår i: Proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014). - Piscataway, NJ : IEEE Communications Society. - 9781479950652 - 9781479950669 ; , s. 283-290
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Konferensbidrag (refereegranskat)abstract
- Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS—Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure); and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.
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| 3. |
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| 4. |
- Alhamazani, Khalid, et al.
(författare)
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Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
- 2019
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Ingår i: I E E E Transactions on Cloud Computing. - Los Alamitos : IEEE. - 2168-7161. ; 7:1, s. 48-61
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Tidskriftsartikel (refereegranskat)abstract
- Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.
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| 5. |
- Alhamazani, Khalid, et al.
(författare)
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Real-time QoS Monitoring for Cloud-based Big Data Analytics Application in Mobile Environments
- 2014
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Ingår i: 2014 15th IEEE International Conference on Mobile Data Management (MDM 2014). - Piscataway, NY : IEEE Communications Society. - 9781479957057 ; , s. 337-340
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Konferensbidrag (refereegranskat)abstract
- The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.
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| 6. |
- Alqahtani, Awatif, et al.
(författare)
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The Integration of Scheduling, Monitoring, and SLA in Cyber Physical Systems
- 2020
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Ingår i: Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things. - Switzerland : Springer. ; , s. 237-254
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Bokkapitel (refereegranskat)abstract
- Cyber-Physical Systems (CPS) is a very complex system where a new management layer must be developed. This chapter presents the benefits and challenges of deploying real-time Scheduling, Monitoring, and End-to-End SLA (SMeSLA) in CPS. We propose an SMeSLA conceptual architecture which allows end-users to submit their service level requirements to an SLA manager; as a result, scheduling and monitoring managers would operate accordingly. The SMeSLA management layer empowers CPS system to meet consumers’ satisfaction and achieve optimal performance. However, in order to successfully deploy SMeSLA in CPS, many technical and general challenges must be addressed.
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| 7. |
- Casas, Israel, et al.
(författare)
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A balanced scheduler with data reuse and replication for scientific workflows in cloud computing systems
- 2017
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Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 74, s. 168-178
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Tidskriftsartikel (refereegranskat)abstract
- Cloud computing provides substantial opportunities to researchers who demand pay-as-you-go computing systems. Although cloud provider (e.g., Amazon Web Services) and application provider (e.g., biologists, physicists, and online gaming companies) both have specific performance requirements (e.g. application response time), it is the cloud scheduler’s responsibility to map the application to underlying cloud resources. This article presents a Balanced and file Reuse-Replication Scheduling (BaRRS) algorithm for cloud computing environments to optimally schedule scientific application workflows. BaRRS splits scientific workflows into multiple sub-workflows to balance system utilization via parallelization. It also exploits data reuse and replication techniques to optimize the amount of data that needs to be transferred among tasks at run-time. BaRRS analyzes the key application features (e.g., task execution times, dependency patterns and file sizes) of scientific workflows for adapting existing data reuse and replication techniques to cloud systems. Further, BaRRS performs a trade-off analysis to select the optimal solution based on two optimization constraints: execution time and monetary cost of running scientific workflows. BaRRS is compared with a state-of-the-art scheduling approach; experiments prove its superior performance. Experiments include four well known scientific workflows with different dependency patterns and data file sizes. Results were promising and also highlighted most critical factors affecting execution of scientific applications on clouds.
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| 8. |
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| 9. |
- Casas, Israel, et al.
(författare)
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PSO-DS : a scheduling engine for scientific workflow managers
- 2017
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Ingår i: Journal of Supercomputing. - : Springer Science and Business Media LLC. - 0920-8542 .- 1573-0484. ; 73:9, s. 3924-3947
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Tidskriftsartikel (refereegranskat)abstract
- Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user-provider affiliation (CUPA) to guide workflow's owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.
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| 10. |
- Demirbaga, Umit, et al.
(författare)
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AutoDiagn : An Automated Real-time Diagnosis Framework for Big Data Systems
- 2022
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Ingår i: IEEE Transactions on Computers. - USA : IEEE. - 0018-9340 .- 1557-9956. ; 71:5, s. 1035-1048
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Tidskriftsartikel (refereegranskat)abstract
- Big data processing systems, such as Hadoop and Spark, usually work on large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems' performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as straggler and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. In this paper, we propose, develop and validate AutoDiagn. This generic and flexible framework provides holistic monitoring of a big data system while detecting performance degradation and enabling root-cause analysis. We present the implementation and evaluation of AutoDiagn that interacts with a Hadoop cluster deployed on a public cloud and tested with real-world benchmark applications. Experimental results show that AutoDiagn has a small resource footprint, high throughput and low latency.
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