Dynamic Request Allocation and Scheduling for Context Aware Applications Subject to a Percentile Response Time SLA in a Distributed Cloud

Boloor, Keerthana; Chirkova, Rada; Viniotis, Yannis; Salo, Timo J.

doi:10.1109/cloudcom.2010.96

Cited by 39 publications

(30 citation statements)

References 12 publications

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“…So in our approach multilevel feedback queue is used for giving more priority for deadline based jobs. The First Come First Served, Shortest Job First and Heuristic Cost Based Scheduling algorithms were implemented and their performances are evaluated [8]. Here the quality of service is measured using the response time of individual jobs.…”

Section: Related Workmentioning

confidence: 99%

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Rajavel

Mala

2012

Advances in Intelligent and Soft Computing

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Abstract.One of the challenging issues in Cloud computing Environment is meeting the Service Level Agreement (SLA). The SLA is an agreement signed between the service provider and the service consumer for accessing the service provided by the service provider over the internet. We can investigate the negotiation strategy between the service provider and the service consumer through the third party called a Broker. In many approaches SLA is designed and trusted through the measurement of various non-functional requirements such as response time of job, CPU usage, memory usage and the storage used by the consumer. Main focus of the business process is satisfying the customer need by quick response. In our proposed approach for satisfying the service consumer (customer), parameter such as response time of deadline based job is considered. The response time of the job is affected due to improper scheduling of the job. Therefore a novel hierarchical scheduling with job prioritization is used to give more priority for deadline based jobs. This approach will satisfy the service consumer and meet the SLA by increasing the performance of the scheduling algorithm.

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Section: Related Workmentioning

confidence: 99%

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Rajavel

Mala

2012

Advances in Intelligent and Soft Computing

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“…The authors in [2] propose an approximate model of JSQ in processing shared models for web server farms. Also, in previous work, [3] develops dynamic request allocation to complete jobs within a deadline, for a certain percentile of number of service requests in context-aware applications.…”

Section: Introductionmentioning

confidence: 99%

Average delay SLAs in Cloud computing

Saovapakhiran

Devetsikiotis

Michailidis

et al. 2012

2012 IEEE International Conference on Communications (ICC)

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In this paper, we conduct feasibility studies on the average delay space for Cloud computing, and we propose a heuristic method to control the vector of average delays, subject to predefined delay constraints. Our work is strongly motivated by the fact that delay control plays a critical role to improve Service Level Agreements (SLA) between users and Cloud service providers, which is necessary for empowering online business.Specifically, our main contributions are two-fold: First, the feasible regions of various routing algorithms for the system's dispatcher are investigated in depth. Second, a simple heuristic algorithm is designed, to move the average delay point along the feasible direction until achieving the delay constraints.Average delay is dependent on multiple factors such as job size, inter-arrival time, flow rate, and the dispatching rules of the system. Therefore, we vary their distribution, parameters and routing rules to examine how the feasible regions move or change. After establishing the feasible delay space, then by moving along the feasible directions, we show that a simple heuristic algorithm can achieve the delay constraints for a two queue system.

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“…, , ⟩), the above parameters , , / ( ), ∑ =1 ( * ), and are fixed. As a result, the steady-state probability of each state can be obtained from the probability equations (14), (15), and (16). Thus, the probabilities of each allocation scheme and blocking probability can also be achieved through the steady-state probability of each state.…”

Section: Performance Analysismentioning

confidence: 99%

“…Thus, according to the uniformization equations (14), (15), (16), and (17), the uniformized maximal long-term expected reward is obtained as …”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

“…Tesauro et al [15] propose a reinforcement learning based management system for dynamic allocation of servers trying to maximize the profit of the host data center in Cloud computing. In [16], Boloor et al propose a generic request allocation and scheduling scheme to achieve desired percentile service level agreements (SLA) goals of consumers and to increase the profits to the cloud provider.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Computing Resource Allocation for Mobile Cloud Computing

Liang

Xing

Cai

et al. 2013

International Journal of Distributed Sensor Networks

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Mobile cloud computing (MCC) enables mobile devices to outsource their computing, storage and other tasks onto the cloud to achieve more capacities and higher performance. One of the most critical research issues is how the cloud can efficiently handle the possible overwhelming requests from mobile users when the cloud resource is limited. In this paper, a novel MCC adaptive resource allocation model is proposed to achieve the optimal resource allocation in terms of the maximal overall system reward by considering both cloud and mobile devices. To achieve this goal, we model the adaptive resource allocation as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests. Extensive simulations are conducted to demonstrate that our proposed model can achieve higher system reward and lower service blocking probability compared to traditional approaches based on greedy resource allocation algorithm. Performance comparisons with various MCC resource allocation schemes are also provided.

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Dynamic Request Allocation and Scheduling for Context Aware Applications Subject to a Percentile Response Time SLA in a Distributed Cloud

Cited by 39 publications

References 12 publications

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Achieving Service Level Agreement in Cloud Environment Using Job Prioritization in Hierarchical Scheduling

Average delay SLAs in Cloud computing

Adaptive Computing Resource Allocation for Mobile Cloud Computing

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