Context-Aware Job Scheduling for Cloud Computing Environments

Assunção, Marcos Dias de; Netto, Marco A. S.; Koch, Fernando; Bianchi, Silvia

doi:10.1109/ucc.2012.33

Cited by 22 publications

(16 citation statements)

References 14 publications

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“…Some of the used algorithms include FIFO, priority-based, deadline-driven, hybrid approaches that use backfilling techniques [15], among others [16,17]. In addition to priorities and deadlines, other factors have been considered, such as fairness [18], energy-consumption [19], and context-awareness [9]. Moreover, utility functions were used to model how the importance of results to users varies over time [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, in a society where human attention is increasingly becoming scarce, and where users perform multiple concurrent activities [4] 1 and use multiple devices [5], 2 response time might not be the sole element defining the perceptions endusers have from a service's QoS. Our previous work investigated how application instrumentation [6][7][8], and end-user context and profiling [9] could be used in the collection of honest signals determining how clients interact with a service provider and what their levels of patience are when making requests.…”

Section: Introductionmentioning

confidence: 99%

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Impact of user patience on auto-scaling resource capacity for cloud services

Assunção

Cardonha

Netto

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s• Mechanisms for resource auto-scaling in clouds considering users' patience.• Methods for determining the step size of scaling operations under bound and unbounded maximum capacity. • Users patience model inspired in prospect theory. a b s t r a c tAn important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms generally take into account aspects such as system load and response time to determine when and by how much a resource pool capacity should be extended or shrunk. In this article, we propose a scheduling algorithm and auto-scaling triggering strategies that explore user patience, a metric that estimates the perception end-users have from the Quality of Service (QoS) delivered by a service provider based on the ratio between expected and actual response times for each request. The proposed strategies help reduce costs with resource allocation while maintaining perceived QoS at adequate levels. Results show reductions on resource-hour consumption by up to approximately 9% compared to traditional approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of user patience on auto-scaling resource capacity for cloud services

Assunção

Cardonha

Netto

et al. 2016

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

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“…Other efforts made in literature in these areas of resource scheduling include: Greedy Particle Swarm Optimization (GPSO) [39], Task Length and User Priority (ie. Credit Based Scheduling Algorithm) [40], Cost based scheduling [41], Energy efficient optimization methods [42], Activity based costing [43], [44], Reliability Factor Based [45], Context aware scheduling [46],Dynamic slot based scheduling [47], [48], Multi-Objective Tasks Scheduling Algorithm [49], Public Cloud Scheduling Algorithm with Load Balancing [50], Agent-based elastic Cloud bag-of-tasks concurrent scheduling [51], Analytic hierarchy process (task scheduling and resource allocation) [52], Swarm scheduling [53], Profitdriven scheduling [54], Dynamic trusted scheduling [55], Community-aware scheduling algorithm [56], Adaptive energy-efficient scheduling [57], grid, cloud and workflow scheduling [58]. In these algorithms, job/task length and priority are mostly the parameters analyzed.…”

Section: B Related Research Effortsmentioning

confidence: 99%

Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

Okafor¹,

Ugwoke²,

Obayi³

et al. 2016

ijacsa

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Abstract-Network failure in cloud datacenter could result from inefficient resource allocation; scheduling and logical segmentation of physical machines (network constraints). This is highly undesirable in Distributed Cloud Computing Networks (DCCNs) running mission critical services. Such failure has been identified in the University of Nigeria datacenter network situated in the south eastern part of Nigeria. In this paper, the architectural decomposition of a proposed DCCN was carried out while exploring its functionalities for grid performance. Virtualization services such as resource allocation and task scheduling were employed in heterogeneous server clusters. The validation of the DCCN performance was carried out using trace files from Riverbed Modeller 17.5 in order to ascertain the influence of virtualization on server resource pool. The QoS metrics considered in the analysis are: the service delay time, resource availability, throughput and utilization. From the validation analysis of the DCCN, the following results were obtained: average throughput (bytes/Sec) for DCCN = 40.00%, DCell = 33.33% and BCube = 26.67%. Average resource availability response for DCCN = 38.46%, DCell = 33.33%, and BCube = 28.21%. DCCN density on resource utilization = 40% (when logically isolated) and 60% (when not logically isolated). From the results, it was concluded that using virtualization in a cloud DataCenter servers will result in enhanced server performance offering lower average wait time even with a higher request rate and longer duration of resource use (service availability). By evaluating these recursive architectural designs for network operations, enterprises ready for Spine and leaf model could further develop their network resource management schemes for optimal performance.

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“…These jobs are distributed based on several techniques for job scheduling proposed in the literature, such as those found in [24] [25] [26] [27]. However, the job distribution may still lead to overwhelming of resources at a VM.…”

Section: B Job Schedulermentioning

confidence: 99%

Controlled Virtual Resource Access to Mitigate Economic Denial of Sustainability (EDoS) Attacks against Cloud Infrastructures

Baig

Binbeshr

2013

2013 International Conference on Cloud Computing and Big Data

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Service providers of the cloud have witnessed a rapidly growing demand to provide services to end-users in a timely manner. Security vulnerabilities against the cloud infrastructure cannot be overlooked. Through exploitation of such weaknesses, the adversary class may disrupt routine cloud operations, and have a debilitating effect on the reputation of the service provider. One attack type specifically affecting cloud services is the Economic Denial of Sustainability (EDoS) attack. Through such a malicious attack, the ability of the service provider to dynamically stretch and accommodate increasing numbers of requests from end-users, is exploited, to make it economically unviable for the service provider to sustain further demand for service from legitimate end-users. In this paper, we propose a novel approach for selectively controlling user requests for service, implemented at the service provider's end. Through this scheme, we reduce i.e mitigate the effects of an imminent EDoS attack against critical cloud resources. Incoming requests are classified into normal or suspicious. Subsequently, further analysis is conducted to ensure that priority to cloud service access is given to those end-users tagged as being legitimate, whereas, suspect users are given lesser priority to service access, until they are eventually removed from the suspect list. Simulations were conducted to study the performance of the scheme, with results showing promise.

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Context-Aware Job Scheduling for Cloud Computing Environments

Cited by 22 publications

References 14 publications

Impact of user patience on auto-scaling resource capacity for cloud services

Impact of user patience on auto-scaling resource capacity for cloud services

Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

Controlled Virtual Resource Access to Mitigate Economic Denial of Sustainability (EDoS) Attacks against Cloud Infrastructures

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