Oceano-SLA based management of a computing utility

Appleby, Karen M.; Fakhouri, S.; Fong, Liana; Goldszmidt, G.; Kalantar, Michael; Krishnakumar, S.; Pazel, Donald P.; Pershing, James A.; Rochwerger, Benny

doi:10.1109/inm.2001.918085

Cited by 195 publications

(159 citation statements)

References 5 publications

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“…On the experimental side, most prior work in data center resource management focusses on placing one type of resource at a time: for example, placing storage requirements assuming job computation location is fixed (file allocation problem [14], [1][2][3]) or placing computation requirements assuming job storage location is fixed [4,11]. These in a sense are variants of GAP.…”

Section: Related Workmentioning

confidence: 99%

“…Most prior work in data center resource management is focussed on placing one type of resource at a time: e.g., placing storage requirements assuming job computation location is fixed [14,3] or placing computation requirements assuming job storage location is fixed [4,11]. One sided placement methods cannot suitably take advantage of the proximities and heterogeneities that exist in modern data centers.…”

Section: Introductionmentioning

confidence: 99%

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Coupled and k-sided placements: generalizing generalized assignment

et al. 2015

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In modern data centers and cloud computing systems, jobs often require resources distributed across nodes providing a wide variety of services. Motivated by this, we study the Coupled Placement problem, in which we place jobs into computation and storage nodes with capacity constraints, so as to optimize some costs or profits associated with the placement. The coupled placement problem is a natural generalization of the widely-studied generalized assignment problem (GAP), which concerns the placement of jobs into single nodes providing one kind of service. We also study a further generalization, the k-Sided Placement problem, in which we place jobs into k-tuples of nodes, each node in a tuple offering one of k services.For both the coupled and k-sided placement problems, we consider minimization and maximization versions. In the minimization versions (MinCP and MinkSP), the goal is to achieve minimum placement cost, while incurring a minimum blowup in the capacity of the individual nodes. Our first main result is an algorithm for MinkSP that achieves optimal cost while increasing capacities by at most a factor of k + 1, also yielding the first constantfactor approximation for MinCP. In the maximization versions (MaxCP and MaxkSP), the goal is to maximize the total weight of the jobs that are placed under hard capacity constraints. MaxkSP can be expressed as a k-column sparse integer program, and can be approximated to within a factor of O(k) factor using randomized rounding of a linear program relaxation. We consider alternative combinatorial algorithms that are much more efficient in practice. Our second main result is a local search based combinatorial algorithm that yields a 15-approximation and O(k 2 )-approximation for MaxCP and MaxkSP respectively. Finally, we consider an online version of MaxkSP and present algorithms that achieve logarithmic competitive ratio under certain necessary technical assumptions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coupled and k-sided placements: generalizing generalized assignment

et al. 2015

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“…A popular approach to dynamic server provisioning is to allocate full machines to applications as needed [10], which does not allow applications to share machines. In contrast, our placement controller allows this sharing and is optimized for it.…”

Section: Related Workmentioning

confidence: 99%

Utility-based placement of dynamic Web applications with fairness goals

Carrera

Steinder

Whalley

et al. 2008

NOMS 2008 - 2008 IEEE Network Operations and Management Symposium

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Abstract-We study the problem of dynamic resource allocation to clustered Web applications. We extend application server middleware with the ability to automatically decide the size of application clusters and their placement on physical machines. Unlike existing solutions, which focus on maximizing resource utilization and may unfairly treat some applications, the approach introduced in this paper considers the satisfaction of each application with a particular resource allocation and attempts to at least equally satisfy all applications. We model satisfaction using utility functions, mapping CPU resource allocation to the performance of an application relative to its objective. The demonstrated online placement technique aims at equalizing the utility value across all applications while also satisfying operational constraints, preventing the over-allocation of memory, and minimizing the number of placement changes. We have implemented our technique in a leading commercial middleware product. Using this real-life testbed and a simulation we demonstrate the benefit of the utility-driven technique as compared to other state-of-the-art techniques.

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“…For example, in [15], the authors combine the use of analytic predictive multiclass queuing network models and combinatorial search techniques to design a controller for determining the required number of servers for each application environment in order to continuously meet the SLA goals under a dynamically varying workload. Another example is Oceano [16], which is a SLAdriven prototype for server farms, which enables the dynamic moving of servers across clusters depending on the customer changing needs. The addition and removal of servers from clusters is triggered by SLA violations.…”

Section: Related Workmentioning

confidence: 99%

Introducing Virtual Execution Environments for Application Lifecycle Management and SLA-Driven Resource Distribution within Service Providers

Goiri

Julià

Ejarque

et al. 2009

2009 Eighth IEEE International Symposium on Network Computing and Applications

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Abstract-Resource management is a key challenge that service providers must adequately face in order to ensure their profitability. This paper describes a proof-of-concept framework for facilitating resource management in service providers, which allows reducing costs and at the same time fulfilling the quality of service agreed with the customers. This is accomplished by means of virtualization. Our approach provides application-specific virtual environments and consolidates them in order to achieve a better utilization of the providers resources. In addition, it implements self-adaptive capabilities for dynamically distributing the providers resources among these virtual environments based on Service Level Agreements. The proposed solution has been implemented as a part of the Semantically-Enhanced Resource Allocator prototype developed within the BREIN European project. The evaluation shows that our prototype is able to react in very short time under changing conditions and avoid SLA violations by rescheduling efficiently the resources.

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Oceano-SLA based management of a computing utility

Cited by 195 publications

References 5 publications

Coupled and k-sided placements: generalizing generalized assignment

Coupled and k-sided placements: generalizing generalized assignment

Utility-based placement of dynamic Web applications with fairness goals

Introducing Virtual Execution Environments for Application Lifecycle Management and SLA-Driven Resource Distribution within Service Providers

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