Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

Jayasinghe, Deepal; Pu, Calton; Eilam, Tamar; Steinder, Małgorzata; Whally, Ian; Snible, E. C.

doi:10.1109/scc.2011.28

Cited by 144 publications

(73 citation statements)

References 22 publications

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“…The solution is fully decentralized and highly Most works in the literature, for instance [19][20][21][22][23][24][25][26][27], address only a single management objective. The result in [28] supports multiple management objectives, but each objective with a different algorithm. In contrast, the work in this paper covers multiple objectives with a single algorithm, and switching between management objectives is achieved by switching the objective functions f in the algorithm.…”

Section: Related Workmentioning

confidence: 95%

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Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

2013

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We consider the problem of jointly allocating compute and network resources in a large Infrastructure-as-a-Service (IaaS) cloud. We formulate the problem of optimally allocating resources to virtual data centers (VDCs) for four well-known management objectives: balanced load, energy efficiency, fair allocation, and service differentiation. Then, we outline an architecture for resource allocation, which centers around a set of cooperating controllers, each solving a problem related to the chosen management objective. We illustrate how a global management objective is mapped onto objectives that govern the execution of these controllers. For a key controller, the Dynamic Placement Controller, we give a detailed distributed design, which is based on a gossip protocol that can switch between management objectives. The design is applicable to a broad class of management objectives, which we characterize through a property of the objective function. The property ensures the applicability of an iterative descent method that the gossip protocol implements. We evaluate, through simulation, the dynamic placement of VDCs for a large cloud under changing load and VDC churn. Simulation results show that this controller is effective and highly scalable, up to 100'000 nodes, for the management objectives considered.

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Section: Related Workmentioning

confidence: 95%

“…Most results to date focus on offline (or static) placement of VDCs or VMs [22,24,28,37]. More recently, more attention has been devoted to the problem of online (or adaptive) placement, whereby a sequence of VDC or VM requests is processed in an iterative fashion [26,27].…”

Section: Related Workmentioning

confidence: 99%

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

2013

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“…Jayasinghe et al model cloud infrastructure as a tree structure with arbitrary depth [35]. Physical hosts on which Virtual Machines (VMs) are hosted are the leaves of this tree, while the ancestors comprise regions and availability zones.…”

Section: Level 3: Service Provisionmentioning

confidence: 99%

Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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Abstract. The chapter summarizes activities of COST IC1304 ACROSS European Project corresponding to traffic management for Cloud Federation (CF). In particular, we provide a survey of CF architectures and standardization activities. We present comprehensive multilevel model for traffic management in CF that consists of five levels: Level 5 -Strategies for building CF, Level 4 -Network for CF, Level 3 -Service specification and provision, Level 2 -Service composition and orchestration, and Level 1 -Task service in cloud resources. For each level we propose specific methods and algorithms. The effectiveness of these solutions were verified by simulation and analytical methods. Finally, we also describe specialized simulator for testing CF solution in IoT environment.

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“…Our approach is similar to application component placement approaches [20,21,22,23], where applications consisting of multiple components, represented as a set of Virtual Machines (VMs), are placed within a datacenter taking the relation between components into account. These approaches typically focus on colocation, anti-colocation and other placement constraints used to impact application security, performance, and reliability.…”

Section: Related Workmentioning

confidence: 99%

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Moens

Dhoedt

Turck

2015

Future Generation Computer Systems

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Multi-tenancy, where multiple end users make use of the same application instance, is often used in clouds to reduce hosting costs. A disadvantage of multi-tenancy is however that it makes it difficult to create customizable applications, as all end users use the same application instance. In this article, we describe an approach for the development and management of highly customizable multi-tenant cloud applications. We apply software product line engineering techniques to cloud applications, and use an approach where applications are composed of multiple interacting components, referred to as application features. Using this approach, multiple features can be shared between different applications. Allocating resources for these feature-based applications is complex, as relations between components must be taken into account, and is referred to as the feature placement problem.In this article, we describe dynamic feature placement algorithms that minimize migrations between subsequent invocations, and evaluate them in dynamic scenarios where applications are added and removed throughout the evaluation scenario. We find that the developed algorithm achieves a low cost, while resulting in few resource migrations. In our evaluations, we observe that adding migration-awareness to the management algorithms reduces the number of instance migrations by more than 77% and reduces the load moved between instances by more than 96% when compared to a static management approach. Despite this reduction in number of migrations, a cost that is on average less than 3% more than the optimal cost is achieved.

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Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

Cited by 144 publications

References 22 publications

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

Traffic Management for Cloud Federation

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

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