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  • 1.
    Bayuh Lakew, Ewnetu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Birke, Robert
    Perez, Juan F.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Lydia Y.
    SmallTail: Scaling Cores and Probabilistic Cloning Requests for Web Systems2018In: 15TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC 2018), IEEE , 2018, p. 31-40Conference paper (Refereed)
    Abstract [en]

    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%.

  • 2.
    Bayuh Lakew, Ewnetu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Xu, Lei
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Pahl, Claus
    A Tree-based Protocol for Enforcing Quotas in Clouds2014In: 2014 IEEE WORLD CONGRESS ON SERVICES (SERVICES), 2014, p. 279-286Conference paper (Refereed)
    Abstract [en]

    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.

  • 3. Farokhi, Soodeh
    et al.
    Jamshidi, Pooyan
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Brandic, Ivona
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A hybrid cloud controller for vertical memory elasticity: a control-theoretic approach2016In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 65, p. 57-72Article in journal (Refereed)
    Abstract [en]

    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.

  • 4. Farokhi, Soodeh
    et al.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Brandic, Ivona
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints2015In: 2015 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), 2015, p. 69-80Conference paper (Refereed)
    Abstract [en]

    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.

  • 5. Goumas, Georgios
    et al.
    Nikas, Konstantinos
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Kotselidis, Christos
    Attwood, Andrew
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Flouris, Michail
    Foutris, Nikos
    Goodacre, John
    Grohmann, Davide
    Karakostas, Vasileios
    Koutsourakis, Panagiotis
    Kersten, Martin
    Lujan, Mikel
    Rustad, Einar
    Thomson, John
    Tomás, Luis
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Vesterkjaer, Atle
    Webber, Jim
    Zhang, Ying
    Koziris, Nectarios
    ACTiCLOUD: Enabling the Next Generation of Cloud Applications2017In: 2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017) / [ed] Lee, K Liu, L, IEEE Computer Society, 2017, p. 1836-1845Conference paper (Refereed)
    Abstract [en]

    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.

  • 6.
    Ibidunmoye, Olumuyiwa
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments2017In: 2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017), IEEE, 2017, p. 22-33Conference paper (Refereed)
    Abstract [en]

    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. 

  • 7.
    Ibidunmoye, Olumuyiwa
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Moghadam, Mahshid Helali
    Department of Computer Engineering, University of Kashan.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments2017In: UCC '17 Proceedings of the10th International Conference on Utility and Cloud Computing, IEEE/ACM , 2017, p. 19-28Conference paper (Refereed)
    Abstract [en]

    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.

  • 8. Karakostas, Vasileios
    et al.
    Goumas, Georgios
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Gerangelos, Stefanos
    Kolberg, Simon
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Nikas, Konstantinos
    Psomadakis, Stratos
    Siakavaras, Dimitrios
    Svärd, Petter
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Koziris, Nectarios
    Efficient Resource Management for Data Centers: The ACTiCLOUD Approach2018In: 2018 International conference on embedded computer systems: architectures, modeling, and simulation (SAMOS XVIII) / [ed] Mudge T., Pnevmatikatos D.N., Association for Computing Machinery (ACM), 2018, p. 244-246Conference paper (Refereed)
    Abstract [en]

    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.

  • 9. Kolodner, Elliot K
    et al.
    Tal, Sivan
    Kyriazis, Dimosthenis
    Naor, Dalit
    Allalouf, Miriam
    Bonelli, Lucia
    Brand, Per
    Eckert, Albert
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Gogouvitos, Spyridon V
    Harnik, Danny
    Hernandez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Jaeger, Michael C
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lopez, Jose Manuel
    Lorenz, Mirko
    Messina, Alberto
    Schulman-Peleg, Alexandra
    Talyansky, Roman
    Voulodimos, Athanasios
    Wolfsthal, Yaron
    A cloud environment for data-intensive storage services2011In: IEEE third international conference on Cloud computing technology and science (CloudCom), 2011, IEEE conference proceedings, 2011, p. 357-366Conference paper (Refereed)
    Abstract [en]

    The emergence of cloud environments has made feasible the delivery of Internet-scale services by addressing a number of challenges such as live migration, fault tolerance and quality of service. However, current approaches do not tackle key issues related to cloud storage, which are of increasing importance given the enormous amount of data being produced in today's rich digital environment (e.g. by smart phones, social networks, sensors, user generated content). In this paper we present the architecture of a scalable and flexible cloud environment addressing the challenge of providing data-intensive storage cloud services through raising the abstraction level of storage, enabling data mobility across providers, allowing computational and content-centric access to storage and deploying new data-oriented mechanisms for QoS and security guarantees. We also demonstrate the added value and effectiveness of the proposed architecture through two real-life application scenarios from the healthcare and media domains.

  • 10.
    Lakew, Ewnetu B.
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Performance-Based Service Differentiation in Clouds2015In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), IEEE conference proceedings, 2015, p. 505-514Conference paper (Refereed)
    Abstract [en]

    Due to fierce competition, cloud providers need to run their data-centers efficiently. One of the issues is to increase data-center utilization while maintaining applications' performance targets. Achieving high data-center utilization while meeting applications' performance is difficult, as data-center overload may lead to poor performance of hosted services. Service differentiation has been proposed to control which services get degraded. However, current approaches are capacity-based, which are oblivious to the observed performance of each service and cannot divide the available capacity among hosted services so as to minimize overall performance degradation. In this paper we propose performance-based service differentiation. In case enough capacity is available, each service is automatically allocated the right amount of capacity that meets its target performance, expressed either as response time or throughput. In case of overload, we propose two service differentiation schemes that dynamically decide which services to degrade and to what extent. We carried out an extensive set of experiments using different services -- interactive as well as non-interactive -- by varying the workload mixes of each service over time. The results demonstrate that our solution precisely provides guaranteed performance or service differentiation depending on available capacity.

  • 11.
    Lakew, Ewnetu. B.
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tail Response Time Modeling and Control for Interactive Cloud ServicesManuscript (preprint) (Other academic)
  • 12.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Autonomous cloud resource provisioning: accounting, allocation, and performance control2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The emergence of large-scale Internet services coupled with the evolution of computing technologies such as distributed systems, parallel computing, utility computing, grid, and virtualization has fueled a movement toward a new resource provisioning paradigm called cloud computing. The main appeal of cloud computing lies in its ability to provide a shared pool of infinitely scalable computing resources for cloud services, which can be quickly provisioned and released on-demand with minimal effort. The rapidly growing interest in cloud computing from both the public and industry together with the rapid expansion in scale and complexity of cloud computing resources and the services hosted on them have made monitoring, controlling, and provisioning cloud computing resources at runtime into a very challenging and complex task. This thesis investigates algorithms, models and techniques for autonomously monitoring, controlling, and provisioning the various resources required to meet services’ performance requirements and account for their resource usage.

    Quota management mechanisms are essential for controlling distributed shared resources so that services do not exceed their allocated or paid-for budget. Appropriate cloud-wide monitoring and controlling of quotas must be exercised to avoid over- or under-provisioning of resources. To this end, this thesis presents new distributed algorithms that efficiently manage quotas for services running across distributed nodes.

    Determining the optimal amount of resources to meet services’ performance requirements is a key task in cloud computing. However, this task is extremely challenging due to multi-faceted issues such as the dynamic nature of cloud environments, the need for supporting heterogeneous services with different performance requirements, the unpredictable nature of services’ workloads, the non-triviality of mapping performance measurements into resources, and resource shortages. Models and techniques that can predict the optimal amount of resources needed to meet service performance requirements at runtime irrespective of variations in workloads are proposed. Moreover, different service differentiation schemes are proposed for managing temporary resource shortages due to, e.g., flash crowds or hardware failures.

    In addition, the resources used by services must be accounted for in order to properly bill customers. Thus, monitoring data for running services should be collected and aggregated to maintain a single global state of the system that can be used to generate a single bill for each customer. However, collecting and aggregating such data across geographical distributed locations is challenging because the management task itself may consume significant computing and network resources unless done with care. A consistency and synchronization mechanism that can alleviate this task is proposed.

  • 13.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Managing Resource Usage and Allocations in Multi-Cluster Clouds2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

     The emergence of large-scale Internet services has fueled a trend toward large-scale systems composed of geographically distributed clusters. Managing resource allocations and resource usage is an important task for such services.

    Resource allocations and resource usage management mechanisms for services running across clusters play vital roles in the performance of the entire system, economical sustainability of the provider, and level of customers satisfaction provided by the system. However, when providing the utmost customer satisfaction the service provider ought to make sure not to over-commit resources beyond the agreed limit between the customer and the provider. Moreover, statistics of resources consumed by different services should be monitored and collected using an efficient mechanism with minimal overhead and interference on the system and the services. Thus, resource usage collection and allocations mechanisms should impose economical constraints to both sides, the customer and the cloud provider.

    This thesis focuses on decentralized resource allocation and resource usage management for services running in multi cluster environments. Theoretical as well as experimental results indicate that our proposed approaches provide efficient management of resources for services running in a large-scale geographically distributed systems.

  • 14.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Cristian, Klein
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Francisco, Hernandez-Rodriguez
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Erik, Elmroth
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Towards faster response time models for vertical elasticity2014In: 2014 IEEE/ACM 7TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2014, p. 560-565Conference paper (Refereed)
    Abstract [en]

    Resource provisioning in cloud computing is typ- ically coarse-grained. For example, entire CPU cores may be allocated for periods of up to an hour. The Resource-as-a- Service cloud concept has been introduced to improve the efficiency of resource utilization in clouds. In this concept, resources are allocated in terms of CPU core fractions, with granularities of seconds. Such infrastructures could be created using existing technologies such as lightweight virtualization using LXC or by exploiting the Xen hypervisor’s capacity for vertical elasticity. However, performance models for de- termining how much capacity to allocate to each application are currently lacking. To address this deficit, we evaluate two performance models for predicting mean response times: the previously proposed queue length model and the novel inverse model. The models are evaluated using 3 applications under both open and closed system models. The inverse model reacted rapidly and remained stable even with targets as low as 0.5 seconds. 

  • 15.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Xu, Lei
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Management of distributed resource allocations in multi-cluster environments2012In: Performance Computing and Communications Conference (IPCCC) 2012, 31st International, IEEE, New York, USA: IEEE , 2012, p. 275-284Conference paper (Other academic)
    Abstract [en]

    We present a fully distributed solution for managing resource allocation for services running across multiple clusters in a large-scale cloud computing environment. Our solution allows individual services running across clusters to compete dynamically for allocations based on their rate of consumption while maintaining the global cloud level allocation limits. The solution monitors resource consumption by services that are spread over a number of clusters. Global polls are triggered only when the allocated balance in a cluster decreases below a threshold and allocations are reassigned in a manner that avoids further immediate global polls. Our solution achieves scalability by minimizing global message exchanges, increases performance by distributing requests, and improves availability by avoiding a single point of failure. We perform a range of simulations to verify the accuracy of our approach, to validate our theoretical results, and to evaluate against competing approaches.

  • 16.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lei, Xu
    School of Computing, Dublin City University, Ireland.
    Francisco, Hernandez-Rodriguez
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Erik, Elmroth
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Claus, Pahl
    School of Computing, Dublin City University, Ireland.
    A Tree-based Protocol for Enforcing Quotas in Clouds2014In: the IEEE 10th 2014 World Congress on Services (SERVICES 2014), IEEE Computer Society, 2014Conference paper (Refereed)
    Abstract [en]

    Services are more and more hosted on cloud nodes for enhancing their performance and increasing their availability. The virtually unlimited availability of resources enables service owners to consume resources without quantitative restrictions, paying only for what they consume. To avoid cost overrun, resource consumption must be controlled and capped when necessary.We present a distributed tree-based protocol to manage quotas in clouds that minimizes communication overhead and reduces the time required to inspect if a quota has been exhausted. Experimental evaluation shows that our protocol provides 42% more communication savings and is up to 15 times faster compared to a distributed baseline solution.

  • 17.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Papadopoulos, Alessandro Vittorio
    Maggio, Martina
    Klein, Cristian
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    KPI-agnostic Control for Fine-Grained Vertical Elasticity2017In: 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), IEEE , 2017, p. 589-598Conference paper (Refereed)
    Abstract [en]

    Applications hosted in the cloud have become indispensable in several contexts, with their performance often being key to business operation and their running costs needing to be minimized. To minimize running costs, most modern virtualization technologies such as Linux Containers, Xen, and KVM offer powerful resource control primitives for individual provisioning - that enable adding or removing of fraction of cores and/or megabytes of memory for as short as few seconds. Despite the technology being ready, there is a lack of proper techniques for fine-grained resource allocation, because there is an inherent challenge in determining the correct composition of resources an application needs, with varying workload, to ensure deterministic performance.

    This paper presents a control-based approach for the management of multiple resources, accounting for the resource consumption, together with the application performance, enabling fine-grained vertical elasticity. The control strategy ensures that the application meets the target performance indicators, consuming as less resources as possible. We carried out an extensive set of experiments using different applications – interactive with response-time requirements, as well as non-interactive with throughput desires – by varying the workload mixes of each application over time. The results demonstrate that our solution precisely provides guaranteed performance while at the same time avoiding both resource over- and under-provisioning.

  • 18.
    Lakew, Ewnetu Bayuh
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Xu, Lei
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Pahl, Claus
    A synchronization mechanism for cloud accounting systems2014In: 2014 International Conference on Cloud and Autonomic Computing (ICCAC 2014), 2014, p. 111-120Conference paper (Refereed)
    Abstract [en]

    In current cloud systems, services run across multiple geographically distributed clusters and continuously generate resource usage data due to constant resource consumption. In the context of accounting, resource usage data generated from each cluster during service runtime must be collected and aggregated into a single cloud-wide record so that a single bill can be created. This paper presents a mechanism to synchronize accounting records among distributed accounting system peers. Run time resource usage generated from different clusters is synchronized to maintain a single cloud-wide view of the data so that a single bill can be created. We provide a set of accounting system requirements and an evaluation which verifies that the solution fulfills these requirements. Experimental results show that our solution produces less overhead in terms of data exchange and scales near-linearly with the size of clusters with no single point of failure.

  • 19.
    Lei, Xu
    et al.
    School of Computing, Dublin City University, Ireland.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Claus, Pahl
    School of Computing, Dublin City University, Ireland.
    Resource State Monitoring of Service Transactions in Cloud Systems2014In: 2014 IEEE International Conference on Services Computing (SCC 2014), IEEE conference proceedings, 2014, p. 512-519Conference paper (Refereed)
    Abstract [en]

    In cloud systems, services constituting a transaction may spread over a large number of servers or clusters. Theoretically, these services could consume cloud resources unlimitedly. To avoid financial loss due to resource overuse, clouds have to monitor the state of resources consumed by the services – collect values of consumption, and evaluate whether the combined usage of resources has excessed a pre-defined upper bound or not. The distributed nature of the services introduces a challenge to the monitoring system on how to summarise distributed state information with low cost. We present our resource state monitoring solution to capture the challenge introduced by services hosted in clouds. Our solution tracks the resource consumed by each service constituting a transaction individually whilst ensures the whole transaction does not overuse the allocated resource. It improves availability by avoiding single points of failure, and achieves scalability by minimising message exchanges.We performed experimental analyses that indicate this work can provide an inexpensive resource monitoring solution for transactions in clouds.

  • 20.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds2018Report (Other academic)
    Abstract [en]

    Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoTs) applications in MECs. The scheduler at the higher level runs periodically – monitors system state and the performance of applications – and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application’s performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example.

    We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

  • 21.
    Mehta, Amardeep
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Tordsson, Johan
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds2018In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoT) applications in MECs. The scheduler at the higher level runs periodically - monitors system state and the performance of applications - and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application's performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example. We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

  • 22. Talyansky, Roman
    et al.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Klein, Cristian
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Levy, Eliezer
    Towards Optimized Self-Management of Distributed Object Storage Systems2015Report (Other academic)
    Abstract [en]

    Cloud storage is increasingly adopted by users due to simplified storage systems compared to on-premise storage. These systems are mostly presented as Object Storage Systems (OSSs), hiding issues, such as redundancy, from users. As new industries are considering adopting clouds for storage, OSSs have to evolve to support new needs. Among the most challenging is assuring guaranteed performance.

    In this paper, we present Controllable Trade-offs (CTO), an OSS-agnostic solution to add performance guarantees. CTO presents itself as a thin layer that mediates requests between the user and the OSS. For generic support, performance is controlled by tuning the rejection probability, and implemented as a user-side queue. Results show that CTO may reduce penalties 3.23 times on average and up to 68 times when the load is high.

  • 23.
    Tomas, Luis
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bayuh Lakew, Ewnetu
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Service Level and Performance Aware Dynamic Resource Allocation in Overbooked Data Centers2016In: 2016 16TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), 2016, p. 42-51Conference paper (Refereed)
    Abstract [en]

    Many cloud computing providers use overbooking to increase their low utilization ratios. This however increases the risk of performance degradation due to interference among co-located VMs. To address this problem we present a service level and performance aware controller that: (1) provides performance isolation for high QoS VMs; and (2) reduces the VM interference between low QoS VMs by dynamically mapping virtual cores to physical cores, thus limiting the amount of resources that each VM can access depending on their performance. Our evaluation based on real cloud applications and both stress, synthetic and realistic workloads demonstrates that a more efficient use of the resources is achieved, dynamically allocating the available capacity to the applications that need it more, which in turn lead to a more stable and predictable performance over time.

  • 24.
    Xu, Lei
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Lakew, Ewnetu Bayuh
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hernandez-Rodriguez, Francisco
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    A Scalable Accounting Solution for Prepaid Services in Cloud Systems2012In: Proceedings of the 2012 IEEE Ninth International Conference on Services Computing, 2012, p. 81-89Conference paper (Refereed)
    Abstract [en]

    Prepaid charging, an essential option for the accounting of cloud services, provides effective financial control for both service providers and customers. However, it has to be supported by real-time credit checking and cost calculation. These real-time actions consume resources of the providers' network and impose high overhead. To tackle this issue, we present a scalable accounting solution in which an accounting component is hosted in every cluster constituting a cloud system. Each of our accounting component supervises service consumptions based on a calculated interval of a service bundle that is composed of all services hosted in a cluster and consumed by one customer simultaneously. Credit will be re-allocated when a customer's credit in one cluster is not enough to compensate further usage, and the allocation is performed based on service consumptions. This work is intended to reduce the cost of prepaid services and to ensure service provision is not hampered by the charging part. Additionally, we perform theoretical and experimental analyses that indicate this work can provide an inexpensive accounting solution for the long-lived services in storage clouds.

1 - 24 of 24
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