Energy-Efficient Resource-Provisioning Algorithms for Optical Clouds

Buysse, Jens; Georgakilas, Konstantinos; Tzanakaki, Anna; Leenheer, Marc De; Dhoedt, Bart; Develder, Chris

doi:10.1364/jocn.5.000226

Cited by 39 publications

(17 citation statements)

References 26 publications

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“…Jens Buysse et al [5] proposed a two step approach to reduce the energy consumption in optical clouds. In this paper a routing and scheduling algorithm is implemented, which selects unused nodes and turnoff these nodes.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

Goyal¹,

Chahal²

2015

IJCA

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Cloud computing has transmuted the IT industry on the whole and made easy to access the resources from anywhere and at anytime. These resources can be accessed as a service rather than as a product. Clients pay for the service used as per their requirement. The huge demand of cloud computing has given rise to the creation of hefty amount of data centers. These data centers consist of different types of resources and each resource is bound to its own power consumption scenario. Besides a number of benefits; Cloud data centers also pose the problem of large amount of power consumption. Lots of techniques and methods have been developed to solve this issue out of which load balancing is one such technique. This paper discusses load balancing using a nature inspired algorithm known as Particle Swarm Optimization (PSO) algorithm. A different nature inspired algorithm known as Firefly Algorithm (FA) is proposed to solve the above said issue.

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Section: Related Workmentioning

confidence: 99%

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

Goyal¹,

Chahal²

2015

IJCA

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“…Jens Buysse et al [10] described a two-step approach to minimize the energy consumption in optical clouds. This routing and scheduling algorithm selects unused nodes and turnoff these nodes.…”

Section: Related Workmentioning

confidence: 99%

Response Time Minimization Task Scheduling Algorithm

Hemamalini¹,

Srinath²

2016

IJCA

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Computational grid has been measured as the best model for managing large scale distributed system having geographically owed resources. Load balancing algorithms are important in the research of network applications. The proposed algorithm minimizes the average execution time and response time. The proposed Response Time Minimization algorithm is implemented with Cloudanalyst toolkit which can simulate a decentralized module. The Cloudanalyst toolkit abstracts the features and behavior of complex fundamental elements such as tasks, resources and users. The proposed algorithm presents service namely resource discovery. The proposed Response Time Minimization Approach is compared with Round Robin Algorithm and equally spread execution algorithm. The main objective of the Response time minimization algorithm is to share the load to the available Virtual Machine (VM) efficiently in order to improve the response time, data processing time and minimize delay time. The performance analysis indicates that the response time of the proposed task scheduling algorithm is much better than the Round Robin Algorithm and equally spread execution algorithm. Results support the proposed approach.

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“…To compute P IT , the power consumed by both the servers and network equipment must be considered. The power consumption of a server depends on the CPU utilization as function of the load, and can be estimated as P server (load) = P server-idle + (P server-max -P server-idle )*load (1) where load represents the ratio between the current load and the maximum capacity of the server, whilst P server-idle and P server-max represent the power consumed by the server when it is idle and when it operates at its maximum capacity, respectively [15]. The power consumed by network equipment depends on the specific architecture of the DC.…”

Section: 1mentioning

confidence: 99%

Orchestrating connectivity services to support elastic operations in datacenter federations

2015

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Abstract-Datacenter federations are able to manage appropriately the green energy resources available in each datacenter (DC) thanks to their geographically distributed infrastructure, thus reducing energy expenditure. Scheduling algorithms can compute virtual machine migration, transferring huge amounts of raw data from one DC to another to minimize operational costs and ensuring a certain Quality of Experience (QoE). Because green energy availability greatly depends on weather conditions, in this work we present a statistical model to improve green solar energy availability estimation accuracy and we use it in a mixed integer linear programming (MILP) formulation to compute optimal virtual machine placement. Optical connections can be used to provide connectivity services of enough capacity to support those migrations. In particular, elastic optical networks can provide connections with multi-granular bitrate, which can be adapted on demand. DC resource managers can request optical connections and control their capacity. However, that scheme involves the resource managers to implement algorithms and interfaces to deal with network specifics and complexity. To solve that issue, in this paper we propose coordinating transfer-based inter-DC connectivity services; inter-DC connectivity is requested in terms of volume of data and completion time. We analyze cost savings when each connectivity model is applied in a DC federation. For the sake of a compelling analysis, exhaustive simulation experiments are carried out considering realistic scenarios. Results show that the notificationbased model can save up to 20% of energy costs and more than 40% of communication costs in the evaluated scenarios.Keywords: Federated datacenters, Energy costs minimization, Inter-datacenter networks. INTRODUCTIONThe huge energy consumption of datacenters (DC) requires an elastic resource management, e.g. by turning servers off when they are not used or turning them on to satisfy increments in the demand. Thanks to virtualization, jobs (e.g., web applications) can be encapsulated in virtual machines (VM) and run in the most proper server according to their performance goals. The local resource manager can migrate VMs from one server to another looking for reducing energy consumption while ensuring the committed quality of experience (QoE) [1]. In that regard, the live-migration technique allows migrating VMs from one server to another without stopping them resulting in reduced downtimes. Large Internet companies, such as Google, have their own IT infrastructures consisting in a number of large DCs placed in geographically diverse locations to guarantee the appropriate QoE to users; DCs are interconnected through a wide area network [2]. Using such infrastructure, workloads can be moved among DCs to take advantage of reduced energy cost during off-peak energy periods in some locations while using green energy when it is available in some other locations. Servers are turned off when they are not used, thus minimizing their energy expendit...

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Energy-Efficient Resource-Provisioning Algorithms for Optical Clouds

Cited by 39 publications

References 26 publications

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

Response Time Minimization Task Scheduling Algorithm

Orchestrating connectivity services to support elastic operations in datacenter federations

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