| 1. |
- Bayuh Lakew, Ewnetu, et al.
(författare)
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A Tree-based Protocol for Enforcing Quotas in Clouds
- 2014
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Ingår i: 2014 IEEE WORLD CONGRESS ON SERVICES (SERVICES). - 9781479950690 ; , s. 279-286
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Konferensbidrag (refereegranskat)abstract
- Services are increasingly being hosted on cloud nodes to enhance their performance and increase their availability. The virtually unlimited availability of cloud resources enables service owners to consume resources without quantitative restrictions, paying only for what they use. To avoid cost overruns, resource consumption must be controlled and capped when necessary. We present a distributed tree-based protocol for managing quotas in clouds that minimizes communication overheads and reduces the time required to determine whether a quota has been exhausted. Experimental evaluation shows that our protocol reduces communication costs by 42% relative to a distributed baseline solution and is up to 15 times faster.
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| 2. |
- Bayuh Lakew, Ewnetu, et al.
(författare)
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SmallTail : Scaling Cores and Probabilistic Cloning Requests for Web Systems
- 2018
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Ingår i: 15TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC 2018). - : IEEE. - 9781538651391 ; , s. 31-40
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Konferensbidrag (refereegranskat)abstract
- Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%.
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| 3. |
- Farokhi, Soodeh, et al.
(författare)
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A hybrid cloud controller for vertical memory elasticity : a control-theoretic approach
- 2016
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Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 65, s. 57-72
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Tidskriftsartikel (refereegranskat)abstract
- Web-facing applications are expected to provide certain performance guarantees despite dynamic and continuous workload changes. As a result, application owners are using cloud computing as it offers the ability to dynamically provision computing resources (e.g., memory, CPU) in response to changes in workload demands to meet performance targets and eliminates upfront costs. Horizontal, vertical, and the combination of the two are the possible dimensions that cloud application can be scaled in terms of the allocated resources. In vertical elasticity as the focus of this work, the size of virtual machines (VMs) can be adjusted in terms of allocated computing resources according to the runtime workload. A commonly used vertical resource elasticity approach is realized by deciding based on resource utilization, named capacity-based. While a new trend is to use the application performance as a decision making criterion, and such an approach is named performance-based. This paper discusses these two approaches and proposes a novel hybrid elasticity approach that takes into account both the application performance and the resource utilization to leverage the benefits of both approaches. The proposed approach is used in realizing vertical elasticity of memory (named as vertical memory elasticity), where the allocated memory of the VM is auto-scaled at runtime. To this aim, we use control theory to synthesize a feedback controller that meets the application performance constraints by auto-scaling the allocated memory, i.e., applying vertical memory elasticity. Different from the existing vertical resource elasticity approaches, the novelty of our work lies in utilizing both the memory utilization and application response time as decision making criteria. To verify the resource efficiency and the ability of the controller in handling unexpected workloads, we have implemented the controller on top of the Xen hypervisor and performed a series of experiments using the RUBBoS interactive benchmark application, under synthetic and real workloads including Wikipedia and FIFA. The results reveal that the hybrid controller meets the application performance target with better performance stability (i.e., lower standard deviation of response time), while achieving a high memory utilization (close to 83%), and allocating less memory compared to all other baseline controllers.
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| 4. |
- Farokhi, Soodeh, et al.
(författare)
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Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints
- 2015
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Ingår i: 2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC). - 9781467395663 ; , s. 69-80
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Konferensbidrag (refereegranskat)abstract
- Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either CPU or memory, while an application may need arbitrary combinations of these resources at different stages of its execution. Nonetheless, the existing techniques cannot be readily used as-is without proper orchestration since they may lead to either under-or over-provisioning of resources and consequently result in undesirable behaviors such as performance disparity. The contribution of this paper is the design of an autonomic resource controller using a fuzzy control approach as a coordination technique. The novel controller dynamically adjusts the right amount of CPU and memory required to meet the performance objective of an application, namely its response time. We perform a thorough experimental evaluation using three different interactive benchmark applications, RUBiS, RUBBoS, and Olio, under workload traces generated based on open and closed system models. The results show that the coordination of memory and CPU elasticity controllers using the proposed fuzzy control provisions the right amount of resources to meet the response time target without over-committing any of the resource types. In contrast, with no coordinating between controllers, the behaviour of the system is unpredictable e.g., the application performance may be met but at the expense of over-provisioning of one of the resources, or application crashing due to severe resource shortage as a result of conflicting decisions.
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| 5. |
- Goumas, Georgios, et al.
(författare)
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ACTiCLOUD : Enabling the Next Generation of Cloud Applications
- 2017
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Ingår i: 2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017). - : IEEE Computer Society. - 9781538617915 - 9781538617922 - 9781538617939 ; , s. 1836-1845
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Konferensbidrag (refereegranskat)abstract
- Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently. Resources are stranded and fragmented, ultimately limiting cloud systems' applicability to large classes of critical applications that pose non-moderate resource demands. Eliminating current technological barriers of actual fluidity and scalability of cloud resources is essential to strengthen cloud computing's role as a critical cornerstone for the digital economy. ACTiCLOUD proposes a novel cloud architecture that breaks the existing scale-up and share-nothing barriers and enables the holistic management of physical resources both at the local cloud site and at distributed levels. Specifically, it makes advancements in the cloud resource management stacks by extending state-of-the-art hypervisor technology beyond the physical server boundary and localized cloud management system to provide a holistic resource management within a rack, within a site, and across distributed cloud sites. On top of this, ACTiCLOUD will adapt and optimize system libraries and runtimes (e.g., JVM) as well as ACTiCLOUD-native applications, which are extremely demanding, and critical classes of applications that currently face severe difficulties in matching their resource requirements to state-of-the-art cloud offerings.
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| 6. |
- Ibidunmoye, Olumuyiwa, et al.
(författare)
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A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments
- 2017
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Ingår i: 2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017). - : IEEE. - 9781538619391 ; , s. 22-33
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Konferensbidrag (refereegranskat)abstract
- Virtualization technologies allow cloud providers to optimize server utilization and cost by co-locating services in as few servers as possible. Studies have shown how applications in multi-tenant environments are susceptible to systems anomalies such as abnormal resource usage due to performance interference. Effective detection of such anomalies requires techniques that can adapt autonomously with dynamic service workloads, require limited instrumentation to cope with diverse applications services, and infer relationship between anomalies non-intrusively to avoid "alarm fatigue" due to scale. We propose a black-box framework that includes an unsupervised prediction-based mechanism for automated anomaly detection in multi-dimensional resource behaviour of datacenter nodes and a graph-theoretic technique for ranking anomalous nodes across the datacenter. The proposed framework is evaluated using resource traces of over 100 virtual machines obtained from a production cluster as well as traces obtained from an experimental testbed under realistic service composition. The technique achieve average normalized root mean squared forecast error and R^2 of (0.92, 0.07) across hosts servers and (0.70, 0.39) across virtual machines. Also, the average detection rate is 88% while explaining 62% of SLA violations with an average lead-time of 6 time-points when the testbed is actively perturbed under three contention scenarios.
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| 7. |
- Ibidunmoye, Olumuyiwa, et al.
(författare)
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Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments
- 2017
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Ingår i: UCC '17 Proceedings of the10th International Conference on Utility and Cloud Computing. - New York, NY, USA : IEEE/ACM. - 9781450351492 ; , s. 19-28
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Konferensbidrag (refereegranskat)abstract
- Designing efficient control mechanisms to meet strict performance requirements with respect tochanging workload demands without sacrificing resource efficiency remains a challenge in cloudinfrastructures. A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models. While it is difficult at design time to specify optimal threshold rules, it is even more challenging inferring precise performance models for the multitude of services. Recently, reinforcement learning have been applied to address this challenge. However, such approaches require many learning trials to stabilize at the beginning and when operational conditions vary thereby limiting their application under dynamic workloads. To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states in parallel to speed up learning. Through multiple experiments on a real virtualized testbed, we demonstrate that our approach converges quickly, meets performance targets at high efficiency without explicit service models.
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| 8. |
- Ibidunmoye, Olumuyiwa, 1983-
(författare)
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Performance anomaly detection and resolution for autonomous clouds
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Fundamental properties of cloud computing such as resource sharing and on-demand self-servicing is driving a growing adoption of the cloud for hosting both legacy and new application services. A consequence of this growth is that the increasing scale and complexity of the underlying cloud infrastructure as well as the fluctuating service workloads is inducing performance incidents at a higher frequency than ever before with far-reaching impact on revenue, reliability, and reputation. Hence, effectively managing performance incidents with emphasis on timely detection, diagnosis and resolution has thus become a necessity rather than luxury. While other aspects of cloud management such as monitoring and resource management are experiencing greater automation, automated management of performance incidents remains a major concern.Given the volume of operational data produced by cloud datacenters and services, this thesis focus on how data analytics techniques can be used in the aspect of cloud performance management. In particular, this work investigates techniques and models for automated performance anomaly detection and prevention in cloud environments. To familiarize with developments in the research area, we present the outcome of an extensive survey of existing research contributions addressing various aspects of performance problem management in diverse systems domains. We discuss the design and evaluation of analytics models and algorithms for detecting performance anomalies in real-time behaviour of cloud datacenter resources and hosted services at different resolutions. We also discuss the design of a semi-supervised machine learning approach for mitigating performance degradation by actively driving quality of service from undesirable states to a desired target state via incremental capacity optimization. The research methods used in this thesis include experiments on real virtualized testbeds to evaluate aspects of proposed techniques while other aspects are evaluated using performance traces from real-world datacenters.Insights and outcomes from this thesis can be used by both cloud and service operators to enhance the automation of performance problem detection, diagnosis and resolution. They also have the potential to spur further research in the area while being applicable in related domains such as Internet of Things (IoT), industrial sensors as well as in edge and mobile clouds.
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| 9. |
- Karakostas, Vasileios, et al.
(författare)
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Efficient Resource Management for Data Centers : The ACTiCLOUD Approach
- 2018
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Ingår i: 2018 International conference on embedded computer systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450364942 ; , s. 244-246
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Konferensbidrag (refereegranskat)abstract
- Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast resources efficiently. Resources are stranded and fragmented, limiting cloud applicability only to classes of applications that pose moderate resource demands. In addition, the need for reduced cost through consolidation introduces performance interference, as multiple VMs are co-located on the same nodes. To avoid such issues, current providers follow a rather conservative approach regarding resource management that leads to significant underutilization. ACTiCLOUD is a three-year Horizon 2020 project that aims at creating a novel cloud architecture that breaks existing scale-up and share-nothing barriers and enables the holistic management of physical resources, at both local and distributed cloud site levels. This extended abstract provides a brief overview of the resource management part of ACTiCLOUD, focusing on the design principles and the components.
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| 10. |
- Kolberg, Simon, et al.
(författare)
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Spreading the Heat: Multi-cloud Controller for Failover and Cross-site Offloading
- 2020
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Ingår i: Web, Artificial Intelligence and Network Applications. - Cham : Springer Nature. - 9783030440381 - 9783030440374 ; , s. 1154-1164
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Konferensbidrag (refereegranskat)abstract
- Despite the ubiquitous adoption of cloud computing and a very rich set of services offered by cloud providers, current systems lack efficient and flexible mechanisms to collaborate among multiple cloud sites. In order to guarantee resource availability during peaks in demand and to fulfill service level objectives, cloud service providers cap resource allocations and as a consequence, face severe underutilization during non-peak periods. In addition, application owners are forced to make independent contracts to deploy their application at different sites. To illustrate how these shortcomings can be overcome, we present a lightweight cross-site offloader for OpenStack. Our controller utilizes templates and site weights to enable offloading of virtual machines between geographically disperse sites. We present and implement a proposed architecture and demonstrate its feasibility in both a typical cross-site offloading, as well as a failover scenario.
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