An Autonomic Admission Control Policy for Distributed Web Systems

Bartolini, Novella; Bongiovanni, G.; Silvestri, Simone

doi:10.1109/mascots.2007.8

Cited by 3 publications

(9 citation statements)

References 24 publications

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“…In figures 13, 12, 14 and 15 the former version is called Flash Crowd Management while the latter is called Base. The Base version is the same policy we introduced in [5] with the addition of the new monitor module detailed in paragraphs IV-C and IV-D. Figure 11 characterizes the traffic scenario of the last set of experiments. It shows a session arrival rate that is subject to several sudden surges of growing intensity.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The design of SOC is inspired by the policy AACA we introduced in a previous work [5] to which we added the anomaly detection and decision rate adaptation mechanism that is necessary to manage flash crowd situations. With respect to [5], we also largely improved the design of the monitor module as we detail in section IV.…”

Section: Related Workmentioning

confidence: 99%

“…Although inspired by our previous work [5], this proposal is original as it includes the anomaly detection and decision rate adaptation mechanisms necessary to perform flash crowd management. It also provides a considerably improved measurement validation system as detailed in section IV.…”

Section: Introductionmentioning

confidence: 99%

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Self-* Overload Control for Distributed Web Systems

Bartolini¹,

Bongiovanni²,

Silvestri³

2008

2008 16th Interntional Workshop on Quality of Service

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Unexpected increases in demand and most of all flash crowds are considered the bane of every web application as they may cause intolerable delays or even service unavailability.Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies.We contribute an original Self-* Overload Control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters.We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Self-* Overload Control for Distributed Web Systems

Bartolini¹,

Bongiovanni²,

Silvestri³

2008

2008 16th Interntional Workshop on Quality of Service

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“…Connection requests are forwarded into two different request queues, and admission control is performed using two metrics: the accept queue length and measurement-based predictions of arrival and service rates from that class. Bartolini et al, in their recent work [6,7], introduce a quite elaborate session admission algorithm, called AACA, that self-configures a dynamic constraint on the rate of incoming new sessions to satisfy guarantees of the Service Level Agreements (SLA). However, the rate limitation for the next iteration interval is based on a relatively straightforward prediction of the session arrival rate from the previous interval measurements.…”

Section: Related Workmentioning

confidence: 99%

“…As Internet services become indispensable both for businesses and personal productivity, the efficient management of Internet services under periods where the system is overloaded or simply highly variable, is of critical importance. There is a host of solutions to maintain user-perceived performance levels in the form of service-level objectives (SLOs) that focus mainly on admission control and/or techniques for service differentiation that are threshold based [13,6,7,19] but their effectiveness can be compromised if the workload is bursty, i.e., it is characterized by sudden temporal "surges" in the intensity of user arrivals [23] and user demands [22]. While capacity planning of systems under bursty workload conditions has been recently demonstrated as critical for business success [22,23], the problem of efficient admission control and service differentiation under temporal workload bursts remains largely unexplored.…”

Section: Introductionmentioning

confidence: 99%

AWAIT: Efficient Overload Management for Busy Multi-tier Web Services under Bursty Workloads

Lü

Cherkasova

Personè

et al. 2010

Lecture Notes in Computer Science

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Abstract. The problem of service differentiation and admission control in web services that utilize a multi-tier architecture is more challenging than in a singletiered one, especially in the presence of bursty conditions, i.e., when arrivals of user web sessions to the system are characterized by temporal surges in their arrival intensities and demands. We demonstrate that classic techniques for a session based admission control that are triggered by threshold violations are ineffective under bursty workload conditions, as user-perceived performance metrics rapidly and dramatically deteriorate, inadvertently leading the system to reject requests from already accepted user sessions, resulting in business loss. Here, as a solution for service differentiation of accepted user sessions we promote a methodology that is based on blocking, i.e., when the system operates in overload, requests from accepted sessions are not rejected but are instead stored in a blocking queue that effectively acts as a waiting room. The requests in the blocking queue implicitly become of higher priority and are served immediately after load subsides. Residence in the blocking queue comes with a performance cost as blocking time adds to the perceived end-to-end user response time. We present a novel autonomic session based admission control policy, called AWAIT, that adaptively adjusts the capacity of the blocking queue as a function of workload burstiness in order to meet predefined user service level objectives while keeping the portion of aborted accepted sessions to a minimum. Detailed simulations illustrate the effectiveness of AWAIT under different workload burstiness profiles and therefore strongly argue for its effectiveness.

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Self-∗ through self-learning: Overload control for distributed web systems

2009

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Overload control is a challenging problem for web-based applications, which are often prone to unexpected surges of traffic. Existing solutions are still far from guaranteeing the necessary responsiveness under rapidly changing operative conditions. We contribute an original Self-* Overload Control (SOC) algorithm that self-configures a dynamic constraint on the rate of incoming new sessions in order to guarantee the fulfillment of the quality requirements specified in a Service Level Agreement (SLA). Our algorithm is based on a measurement activity that makes the system capable of self-learning and self-configuring even in the case of rapidly changing traffic scenarios, dynamic resource provisioning or server faults. Unlike other approaches, our proposal does not require any prior information about the incoming traffic, or any manual configuration of key parameters.We ran extensive simulations under a wide range of operating conditions. The experiments show how the proposed system self-protects from overload, meeting SLA requirements even under intense workload variations. Moreover, it rapidly adapts to unexpected changes in available capacity, as in the case of faults or voluntary architectural adjustments. Performance comparisons with other previously proposed approaches show that our algorithm has better performance and more stable behavior.

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An Autonomic Admission Control Policy for Distributed Web Systems

Cited by 3 publications

References 24 publications

Self-* Overload Control for Distributed Web Systems

Self-* Overload Control for Distributed Web Systems

AWAIT: Efficient Overload Management for Busy Multi-tier Web Services under Bursty Workloads

Self-∗ through self-learning: Overload control for distributed web systems

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