A Deadline and Budget Constrained Cost and Time Optimization Algorithm for Cloud Computing

Chintapalli, Venkatarami Reddy

doi:10.1007/978-3-642-22726-4_47

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…-service response time expressed as an integer in the cloud service descriptions (e.g., 20msec) and as a fuzzy number when a potential cloud consumer provides her requirements (e.g., (10,20,25) …”

Section: Cloud Service Recommendation Methodsmentioning

confidence: 99%

“…Such efforts, mainly target Infrastructure as a Service (IaaS) level optimisation issues from the perspective of the cloud provider. Other efforts, that examine cloud service optimisation from the cloud consumer perspective, focus on comparing low level performance aspects of cloud services such as CPU and network throughput [9] addressing mainly IaaSrelated issues [5,10] or they are concerned with comparing similar services based on mainly cost issues [11]. Although these works aim to identify optimal cloud services from a cloud consumer perspective by considering user preferences, they are limited because they only consider quantitative metrics and they do not use advanced MCDM strategies to solve the multi-criteria optimisation problem.…”

Section: Related Workmentioning

confidence: 99%

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PuLSaR: preference-based cloud service selection for cloud service brokers

Patiniotakis

Verginadis

Mentzas

2015

J Internet Serv Appl

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Over the last few years, the vast increase of cloud service offerings that are available from heterogeneous cloud vendors, has made the evaluation and selection of desired cloud services, a cumbersome task for service consumers. In that respect, there is an increasing need for user guidance and intermediation during the service selection process but also during the cloud service consumption that should always refer to the best possible choice based on user preferences. In this paper, we discuss the Preference-based cLoud Service Recommender (PuLSaR) that uses a holistic multi-criteria decision making (MCDM) approach for offering optimisation as a brokerage service. The specification and implementation details of this proposed software mechanism are thoroughly discussed while the background method used is summarised. Both method and brokerage service allow for the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics and dealing with their fuzziness. We cope with the fuzziness of imprecise metrics in the sense that this approach deals with linguistically expressed preferences and cloud service characteristics that lack a fixed or precise value and entail a level of vagueness which can only be captured using the Zadeh's Fuzzy Set Theory. Furthermore, this paper reports on a number of experiments that were conducted in order to measure PuLSaR's performance and scalability.

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Section: Cloud Service Recommendation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

PuLSaR: preference-based cloud service selection for cloud service brokers

Patiniotakis

Verginadis

Mentzas

2015

J Internet Serv Appl

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“…Therefore, these resources with on-demand will be handed over to each user in the most appropriate time. Similarly, Chintapalli [72] proposes an algorithm for assigning resources to the cloud user's demand with lower cost and a specified constraints budget and deadline. At this point, the study considers several cloud providers for assigning these cloud user's requirements.…”

Section: Cost Aware Resource Allocationmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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“…The main batch scheduling algrithms are Min-min [2] and DBCT [3] (under the deadline and budget constrain time optimization algorithm). The idea of Min-min algorithm is that the best resources will be allocated to the minimum task, thus the system throughput rate can be improved and the minimum time of the job can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…These factors make task schedule in cloud computing very complicated. So it is a technical difficulty to schedule the massive tasks in cloud computing in order to get a better scheduling results with the constrain of budget and deadline.The main batch scheduling algrithms are Min-min [2] and DBCT [3] (under the deadline and budget constrain time optimization algorithm). The idea of Min-min algorithm is that the best resources will be allocated to the minimum task, thus the system throughput rate can be improved and the minimum time of the job can be obtained.…”

mentioning

confidence: 99%

Research on Batch Scheduling in Cloud Computing

Jiao¹,

Yu²

2016

TELKOMNIKA

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In the existing cloud computing environment, batch scheduling strategies mainly focus on the management of resources allocation. This paper provides the task scheduling algorithm based on service quality which fully considers priority and scheduling deadline. IntroductionCloud computing is a new business model, and it is a product of information technology [1]. In cloud computing market, user scale is very large, and the QoS(Quality of Service) requests of each task are not identical. These factors make task schedule in cloud computing very complicated. So it is a technical difficulty to schedule the massive tasks in cloud computing in order to get a better scheduling results with the constrain of budget and deadline.The main batch scheduling algrithms are Min-min [2] and DBCT [3] (under the deadline and budget constrain time optimization algorithm). The idea of Min-min algorithm is that the best resources will be allocated to the minimum task, thus the system throughput rate can be improved and the minimum time of the job can be obtained. While the idea of DBCT is to schedule the task on the resource that can complete the task earliest, also request the cost no more than user's budget and the finish time no more than deadline. With the popularity of cloud computing, some users with less budget may also want to use cloud computing; also there are some tasks with sufficient budget and intensive demand of deadline. For these cases, the number of completed tasks of Min-min and DBCT algorithms will be less. There are also a lot of people put forward other algorithms, but some did not consider the deadline [4], some did not consider the user's budget [5]; they did not balance the relationship between the deadline and the budget.From the perspective of QoS, this paper proposes DBCMNCT (deadline and budget constrained maximized number of completed tasks scheduling algorithm) algorithm in order to meet the budget and deadline constrains. Compared with Min-min and DBCT algorithm, DBCMNCT algorithm can accomplish higher priority tasks better, at the same time the new algorithm can maximize the number of completed tasks. So the user's demands can be meet; on the other hand, the cloud computing can attract more consumers and get more benefits.

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A Deadline and Budget Constrained Cost and Time Optimization Algorithm for Cloud Computing

Cited by 7 publications

References 11 publications

PuLSaR: preference-based cloud service selection for cloud service brokers

PuLSaR: preference-based cloud service selection for cloud service brokers

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

Research on Batch Scheduling in Cloud Computing

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