SLA based resource allocation policies in autonomic environments

Ardagna, Danilo; Trubian, Marco; Zhang, Li

doi:10.1016/j.jpdc.2006.10.006

Cited by 91 publications

(43 citation statements)

References 14 publications

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“…Ardagna et al proposed a runtime resource allocation scheduler that assigns multi-tier applications to physical resources at fine timescales to satisfy cost and performance objectives [26,27]. The approach uses a multi-class QNM and a heuristic search algorithm that solves a mixed non-linear programming problem.…”

Section: Queueing-model Based Techniquesmentioning

confidence: 99%

Burstiness-aware service level planning for enterprise application clouds

Youssef

Krishnamurthy

2017

J Cloud Comp

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Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber's applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers' SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads.

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Section: Queueing-model Based Techniquesmentioning

confidence: 99%

Burstiness-aware service level planning for enterprise application clouds

Youssef

Krishnamurthy

2017

J Cloud Comp

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“…These approaches provide sound foundational basis for medium term or offline capacity estimation, in contrast to our adaptive approach based on measured performance. Several studies [4,20] have proposed approaches for performance modeling and SLA based resource allocations for multi-tier applications. Providing QoS guarantees to applications while overcommitting the servers has been studied in [23,29].…”

Section: Related Workmentioning

confidence: 99%

Defragmenting the cloud using demand-based resource allocation

Shanmuganathan

Gulati

Varman

2013

Proceedings of the ACM SIGMETRICS/international Conference on Measurement and Modeling of Computer Systems

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Public clouds sell capacity in the form of pre-defined virtual machine (VM) configurations to their tenants. This forces tenants to buy the VM configuration based on the peak usage. This diminishes the value proposition of moving to a public cloud as compared to doing consolidation in a private virtualized datacenter. Ideally we would like the cloud tenants to buy capacity in bulk and benefit from statistical multiplexing among workloads. This requires dynamic allocation of bulk capacity among VMs of a tenant that may be running on different servers across different datacenters.In this paper, we propose two novel algorithms called BPX and IDD that are able to provide the abstraction of buying bulk capacity to a cloud customer. These algorithms dynamically allocate the overall capacity between VMs based on their demand and user-set importance. Both algorithms are highly scalable and are designed to work in a large scale environment. Our analysis shows that BPX is able to meet all the desirable properties in providing the abstraction. We implemented the prototype of BPX as part of VMware's management software and showed that BPX is able to closely mimic the behavior of a centralized allocator, in a distributed manner. IntroductionConsider an IT department of a small company that is considering moving its workloads to a public cloud. Currently the company is running a private cloud, where they are able to take advantage of server consolidation by using in-house virtualization and cloud management software. Several companies offer solutions in this space, such as Nebula [16] Within the private cloud the VMs run in a controlled physical infrastructure maintained by the IT department of the same company. The private cloud is able to exploit temporal variations in the VM loads to reduce the amount of provisioned resources, by over-committing server CPU and memory. Hypervisors such as VMware ESX Server provide several techniques (like transparent page-sharing, ballooning, compression, and swap-to-SSD) to facilitate high consolidation ratios. The gains from statistical multiplexing benefit the bottom line of the company by reducing both its capital and operating expenses. In a public cloud the physical infrastructure is distributed over one or more mega data centers, supporting thousands of servers and hosting VMs belonging to multiple paying customers. In this situation the benefits of workload multiplexing accrue to the cloud service provider and not directly to the tenant, as the former increases consolidation ratios without regard to specific customers.In the public cloud, the tenant's is forced to purchase VMs based on their configured sizes. Typically VMs are configured for peak usage and the consolidation helps because VMs can use resource from each other based on their run-time demands on the host. An interesting study [12] on buying capacity in terms of fixed T-shirt sizes vs. doing time sharing showed that buying per VM capacity can be twice as expensive as compared to having a time sharing system. Lets...

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“…The authors proposed a heuristic to solve the problem of assigning different client classes to different servers to maximize the total profit. Ardagna et al [10] extend their work to multi-tier profit optimization for continuous utility functions and proposed an iterative approach to reach the solution.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Profit in Cloud Computing System via Resource Allocation

Goudarzi

Pedram

2011

2011 31st International Conference on Distributed Computing Systems Workshops

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Abstract-With increasing demand for high performance computing and data storage, distributed computing systems have attracted a lot of attention. Resource allocation is one of the most important challenges in the distributed systems specially when the clients have some Service Level Agreements (SLAs) and the total profit in the system depends on how the system can meet these SLAs. In this paper, an SLA-based resource allocation problem for cloud computing is considered and a distributed solution to this problem is presented. The processing, data storage, and communication resources are considered as three dimensions in which optimizations are performed. Simulation results demonstrate that the proposed heuristic algorithm is robust (produces high quality solutions independent of the initial solution provided) and produces solutions very close to the "optimum" (best solution found by Monte Carlo simulation). 1

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SLA based resource allocation policies in autonomic environments

Cited by 91 publications

References 14 publications

Burstiness-aware service level planning for enterprise application clouds

Burstiness-aware service level planning for enterprise application clouds

Defragmenting the cloud using demand-based resource allocation

Maximizing Profit in Cloud Computing System via Resource Allocation

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