The magnitude of data being stored and processed in the Cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and mobile code offloading. Concurrently, cloud services are getting more global and geographically distributed. To handle such changes in its usage scenario, the Cloud needs to transform into a completely decentralized, federated and ubiquitous environment similar to the historical transformation of the Internet. Indeed, research ideas for the transformation has already started to emerge including but not limited to Cloud Federations, Multi-Clouds, Fog Computing, Edge Computing, Cloudlets, Nano data centers, etc.
Standardization and resource management come up as the most significant issues for the realization of the distributed cloud paradigm. The focus in this thesis is the latter: efficient management of limited computing and network resources to adapt to the decentralization. Specifically, cloud services that consist of several virtual machines, dedicated network connections and databases are mapped to a multi-provider, geographically distributed and dynamic cloud infrastructure. The objective of the mapping is to improve quality of service in a cost-effective way. To that end; network latency and bandwidth as well as the cost of storage and computation are subjected to a multi-objective optimization.
The first phase of the resource mapping optimization is the topology mapping. In this phase, the virtual machines and network connections (i.e. the virtual cluster) of the cloud service are mapped to the physical cloud infrastructure. The hypothesis is that mapping the virtual cluster to a group of data centers with a similar topology would be the optimal solution.
Replication management is the second phase where the focus is on the data storage. Data objects that constitute the database are replicated and mapped to the storage as a service providers and end devices. The hypothesis for this phase is that an objective function adapted from the facility location problem optimizes the replica placement.
Detailed experiments under real-world as well as synthetic workloads prove that the hypotheses of the both phases are true.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
The magnitude of data being stored and processed in the cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and computation offloading. Efficient management of limited computing and network resources is necessary to handle such an increase in cloud workload. Some of the critical issues in resource management for cloud computing are \emph{modeling resources / requirements} and \emph{allocating resources to users}. Potential benefits of tackling these issues include increases in utilization, scalability, Quality of Service (QoS) and throughput as well as decreases in latency and costs.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.
One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost. Up to 85 percent of computing capacity remains idle in distributed computing environments and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).
The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that network latency and infrastructure cost is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm. While, in the second period, we completed the algorithm design and tuning, carried out detailed, progressive evaluation on the suggested algorithm and documented our work.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
As the volume and velocity of data in the cloud is increasing, the geographical distribution of where it is produced, processed and consumed is also gaining more significance. It is getting less feasible to move data to a distant data center for processing and move output again to the consumer location. Several promising approaches including Fog Computing, Mobile Cloud Computing, Cloudlets and Nano Data Centers are instead suggesting to bring processing entities to the edge of the cloud network to reduce latency. One issue we have identified in this scenario regarding resource management is the optimal selection of processing entity count and location. Since processing entities may also be communicating among themselves and possibly with a centralized data storage, we suggest that a hierarchical caching mechanism for the distant data will increase computation performance. We formulated this problem in the third period of the thesis and planned to further study during the fourth period.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.
One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost \cite{Zhang2010}. Up to 85 percent of computing capacity remains idle in distributed computing environments \cite{Papagianni2013} and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).
The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that latency is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm and evaluated it.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Proposal]AtakanAral
As cloud services continue to grow rapidly, the need for more efficient utilization of resources and better optimized distribution of workload emerges. Our aim is to provide a solution to this optimization problem from three perspectives: (1) Infrastructure level; (2) Application level; and (3) Platform level.
In infrastructure level, the problem is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that latency is minimized while WAN bandwidth and datacenter capacity limits are respected. We plan to model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure can be represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach will employ weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
VM request is an input to our optimization in infrastructure level but the requested number and capacity of VMs as well as their inter-connections may not be optimal for a specific cloud service. In application level, on the other hand, we plan to deal with the configuration of the MapReduce programming model (More specifically one of its implementations: Apache Hadoop) especially in terms of number of maps and reduces. Previous work has shown that the optimum configuration depends on the resource consumption of the cloud service and it can be determined by executing a fraction to generate a signature. Our aim is to determine the number of maps and reduces statically by analyzing the design model, source code and/or the input data.
Furthermore, in platform level we aim to analyze the shuffle and scheduling algorithms of the MapReduce system to achieve a better load balancing and less amount communication between nodes. Shuffle is the phase where the output of all map processors with the same key are assigned to the same reduce processor. We will carry out a cost based formal analysis of the existing Hadoop schedulers to determine which scheduler is more appropriate for given costs of map, reduce and shuffle.
In summary, a holistic approach will be carried out to improve the performance of cloud software by optimizing resource allocation. Intended optimizations are on infrastructure level (Resource Selection and Assignment), application level (Map Reduce Configuration) and platform level (Work Distribution and Load Balancing). Cloud environment and resource allocation problem will be modeled based on graphs and automata, and the solutions will be within this context as well.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
The magnitude of data being stored and processed in the cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and computation offloading. Efficient management of limited computing and network resources is necessary to handle such an increase in cloud workload. Some of the critical issues in resource management for cloud computing are \emph{modeling resources / requirements} and \emph{allocating resources to users}. Potential benefits of tackling these issues include increases in utilization, scalability, Quality of Service (QoS) and throughput as well as decreases in latency and costs.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.
One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost. Up to 85 percent of computing capacity remains idle in distributed computing environments and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).
The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that network latency and infrastructure cost is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm. While, in the second period, we completed the algorithm design and tuning, carried out detailed, progressive evaluation on the suggested algorithm and documented our work.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
As the volume and velocity of data in the cloud is increasing, the geographical distribution of where it is produced, processed and consumed is also gaining more significance. It is getting less feasible to move data to a distant data center for processing and move output again to the consumer location. Several promising approaches including Fog Computing, Mobile Cloud Computing, Cloudlets and Nano Data Centers are instead suggesting to bring processing entities to the edge of the cloud network to reduce latency. One issue we have identified in this scenario regarding resource management is the optimal selection of processing entity count and location. Since processing entities may also be communicating among themselves and possibly with a centralized data storage, we suggest that a hierarchical caching mechanism for the distant data will increase computation performance. We formulated this problem in the third period of the thesis and planned to further study during the fourth period.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
In addition to software delivered as services over the Internet, hardware and software systems that make the delivery possible are referred as cloud computing. In cloud computing, resources such as CPU, memory, bandwidth and storage are treated as utilities that can scale up and down on demand. It also allows per-usage metering and billing of these resources. By means of cloud computing, cloud users can handle unexpected high demands without over-provisioning and they do not need to invest for abundant hardware resources initially. Cloud providers, on the other hand, have the opportunity of reallocating idle resources for other cloud users.
One general research challenge in cloud computing is the efficient allocation of cloud resources to users since cloud providers should satisfy quality of service (QoS) objectives while minimizing their operational cost \cite{Zhang2010}. Up to 85 percent of computing capacity remains idle in distributed computing environments \cite{Papagianni2013} and this wastage is mainly due to poor optimization of job placement, parallelization and scheduling. We aim to model resource allocation problem in cloud systems, analyze and optimize it using graphs and formal behavioral models (e.g. finite automata).
The problem that we are currently interested, is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that latency is minimized while WAN bandwidth and DC capacity limits are respected. We model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure are represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach employs weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
In the first six months period of the thesis project, we collected evaluation data, developed the simulation environment and defined the experimental setup which contains metrics and baseline methods. In addition, we developed the preliminary version of the resource selection algorithm and evaluated it.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Proposal]AtakanAral
As cloud services continue to grow rapidly, the need for more efficient utilization of resources and better optimized distribution of workload emerges. Our aim is to provide a solution to this optimization problem from three perspectives: (1) Infrastructure level; (2) Application level; and (3) Platform level.
In infrastructure level, the problem is to distribute virtual machines (VMs) to the datacenters (DCs) with geographical locations in such a way that latency is minimized while WAN bandwidth and datacenter capacity limits are respected. We plan to model latency as a function of DC load, inter-DC communication and proximity to user. Both VM requests and cloud infrastructure can be represented by graphs where vertices correspond to machines (either physical or virtual) and edges correspond to network connections between them. Our approach will employ weighted graph similarity and subgraph matching to suggest an efficient placement or the list of migrations to reach an efficient placement.
VM request is an input to our optimization in infrastructure level but the requested number and capacity of VMs as well as their inter-connections may not be optimal for a specific cloud service. In application level, on the other hand, we plan to deal with the configuration of the MapReduce programming model (More specifically one of its implementations: Apache Hadoop) especially in terms of number of maps and reduces. Previous work has shown that the optimum configuration depends on the resource consumption of the cloud service and it can be determined by executing a fraction to generate a signature. Our aim is to determine the number of maps and reduces statically by analyzing the design model, source code and/or the input data.
Furthermore, in platform level we aim to analyze the shuffle and scheduling algorithms of the MapReduce system to achieve a better load balancing and less amount communication between nodes. Shuffle is the phase where the output of all map processors with the same key are assigned to the same reduce processor. We will carry out a cost based formal analysis of the existing Hadoop schedulers to determine which scheduler is more appropriate for given costs of map, reduce and shuffle.
In summary, a holistic approach will be carried out to improve the performance of cloud software by optimizing resource allocation. Intended optimizations are on infrastructure level (Resource Selection and Assignment), application level (Map Reduce Configuration) and platform level (Work Distribution and Load Balancing). Cloud environment and resource allocation problem will be modeled based on graphs and automata, and the solutions will be within this context as well.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Survey on Division and Replication of Data in Cloud for Optimal Performance a...IJSRD
Outsourcing information to an outsider authoritative control, as is done in distributed computing, offers ascend to security concerns. The information trade off may happen because of assaults by different clients and hubs inside of the cloud. Hence, high efforts to establish safety are required to secure information inside of the cloud. On the other hand, the utilized security procedure should likewise consider the advancement of the information recovery time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that all in all methodologies the security and execution issues. In the DROPS procedure, we partition a record into sections, and reproduce the divided information over the cloud hubs. Each of the hubs stores just a itary part of a specific information record that guarantees that even in the event of a fruitful assault, no important data is uncovered to the assailant. Additionally, the hubs putting away the sections are isolated with certain separation by method for diagram T-shading to restrict an assailant of speculating the areas of the sections. Moreover, the DROPS procedure does not depend on the customary cryptographic procedures for the information security; in this way alleviating the arrangement of computationally costly approaches. We demonstrate that the likelihood to find and bargain the greater part of the hubs putting away the sections of a solitary record is to a great degree low. We likewise analyze the execution of the DROPS system with ten different plans. The more elevated amount of security with slight execution overhead was watched.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
Resource Allocation and Task scheduling are the most important key words in today’s dynamic cloud based applications. Task scheduling involves assigning tasks to available processors with the aim of producing minimum execution time, whereas resource allocation involves deciding on an allocation policy to allocate resources to various tasks so as to have maximum resource utilization. Algorithms used for scheduling resources for virtual machines are designed for both homogeneous and heterogeneous environments. Majority of the algorithms focus on processing ability often neglecting other features such as network bandwidth and actual resource requirements. One of the major pitfalls in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation is performed with the objective of minimizing the costs associated with it. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper we will focus on the challenges faced in task scheduling and resource allocation in dynamic heterogeneous clouds.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD C...ijccsa
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
Survey on Division and Replication of Data in Cloud for Optimal Performance a...IJSRD
Outsourcing information to an outsider authoritative control, as is done in distributed computing, offers ascend to security concerns. The information trade off may happen because of assaults by different clients and hubs inside of the cloud. Hence, high efforts to establish safety are required to secure information inside of the cloud. On the other hand, the utilized security procedure should likewise consider the advancement of the information recovery time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that all in all methodologies the security and execution issues. In the DROPS procedure, we partition a record into sections, and reproduce the divided information over the cloud hubs. Each of the hubs stores just a itary part of a specific information record that guarantees that even in the event of a fruitful assault, no important data is uncovered to the assailant. Additionally, the hubs putting away the sections are isolated with certain separation by method for diagram T-shading to restrict an assailant of speculating the areas of the sections. Moreover, the DROPS procedure does not depend on the customary cryptographic procedures for the information security; in this way alleviating the arrangement of computationally costly approaches. We demonstrate that the likelihood to find and bargain the greater part of the hubs putting away the sections of a solitary record is to a great degree low. We likewise analyze the execution of the DROPS system with ten different plans. The more elevated amount of security with slight execution overhead was watched.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Survey on Dynamic Resource Allocation Strategy in Cloud Computing EnvironmentEditor IJCATR
Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
Resource Allocation and Task scheduling are the most important key words in today’s dynamic cloud based applications. Task scheduling involves assigning tasks to available processors with the aim of producing minimum execution time, whereas resource allocation involves deciding on an allocation policy to allocate resources to various tasks so as to have maximum resource utilization. Algorithms used for scheduling resources for virtual machines are designed for both homogeneous and heterogeneous environments. Majority of the algorithms focus on processing ability often neglecting other features such as network bandwidth and actual resource requirements. One of the major pitfalls in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation is performed with the objective of minimizing the costs associated with it. The other challenges of resource allocation are meeting customer demands and application requirements. In this paper we will focus on the challenges faced in task scheduling and resource allocation in dynamic heterogeneous clouds.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD C...ijccsa
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
Hadoop is famously scalable. Cloud Computing is famously scalable. R – the thriving and extensible open source Data Science software – not so much. But what if we seamlessly combined Hadoop, Cloud Computing, and R to create a scalable Data Science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based Web Service. Learn how to leverage the magic of Hadoop on-premises or in the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine learning algorithms at scale.
Congresso Sociedade Brasileira de Computação CSBC2016 Porto Alegre (Brazil)
Workshop on Cloud Networks & Cloudscape Brazil
Sergio Takeo Kofuji, Assistant Professor at the University of São Paulo, Coordinator to FI WARE LAB in University of São Paulo, Brazil
The European Commission, in a recent communication (April 19th), has identified 5G and Internet of Things (IoT) amongst the ICT standardisation priorities for the Digital Single Market (DSM). This session will discuss the emergence of the mobile edge computing paradigm to reduce the latency for processing near the source large quantities of data and the need of the emerging 5G technology to satisfy the requirements of different verticals. Mobile Edge Clouds have the potential to provide an enormous amount of resources, but it raises several research challenges related to the resilience, security, data portability and usage due to the presence of multiple trusted domains, as well as energy consumption of battery powered devices. Large and centralized clouds have been deployed and have shown how this paradigm can greatly improve performance and flexibility while reducing costs. However, there are many issues requiring solutions that are user and context aware, dynamic, and with the capability to handle heterogeneous demands and systems. This is a challenge triggered by the Internet of Things (IoT) scenario, which strongly requires cloud-based solutions that can be dynamically located and managed, on demand and with self-organization capabilities to serve the purposes of different verticals.
It is a seminar presentation on a technology called Virtual reality. It key features are what is virtual reality, its history and evolution, its types, devices that are used for Virtual reality and where virtual reality is applicable.
HYBRID OPTICAL AND ELECTRICAL NETWORK FLOWS SCHEDULING IN CLOUD DATA CENTRESijcsit
Hybrid intra-data centre networks, with optical and electrical capabilities, are attracting research interest
in recent years. This is attributed to the emergence of new bandwidth greedy applications and novel
computing paradigms. A key decision to make in networks of this type is the selection and placement of
suitable flows for switching in circuit network. Here, we propose an efficient strategy for flow selection and
placement suitable for hybrid Intra-cloud data centre networks. We further present techniques for
investigating bottlenecks in a packet networks and for the selection of flows to switch in circuit network.
The bottleneck technique is verified on a Software Defined Network (SDN) testbed. We also implemented
the techniques presented here in a scalable simulation experiment to investigate the impact of flow
selection on network performance. Results obtained from scalable simulation experiment indicate a
considerable improvement on average throughput, lower configuration delay, and stability of offloaded
flows..
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud C...neirew J
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
Dynamic Resource Provisioning with Authentication in Distributed DatabaseEditor IJCATR
Data center have the largest consumption amounts of energy in sharing the power. The public cloud workloads of different
priorities and performance requirements of various applications [4]. Cloud data center have capable of sensing an opportunity to present
different programs. In my proposed construction and the name of the security level of imperturbable privacy leakage rarely distributed
cloud system to deal with the persistent characteristics there is a substantial increases and information that can be used to augment the
profit, retrenchment overhead or both. Data Mining Analysis of data from different perspectives and summarizing it into useful
information is a process. Three empirical algorithms have been proposed assignments estimate the ratios are dissected theoretically and
compared using real Internet latency data recital of testing methods
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
Paper sharing_resource optimization scheduling and allocation for hierarchica...YOU SHENG CHEN
From Future Generation Computer Systems 107
Author:Jing Li (2020)
Declaration:There are some problems in this paper. I have tried my best to explain that the experimental data are not from the data of this paper.
On the Optimal Allocation of VirtualResources in Cloud Compu.docxhopeaustin33688
On the Optimal Allocation of Virtual
Resources in Cloud Computing Networks
Chrysa Papagianni, Aris Leivadeas, Symeon Papavassiliou,
Vasilis Maglaris, Cristina Cervelló-Pastor, and �Alvaro Monje
Abstract—Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of
computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented
models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational
services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the
efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and
optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within
networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping
problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal
networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of
the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method
for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and
adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation
environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-
concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization
platform augmented with network and computing facilities.
Index Terms—Federated infrastructures, resource allocation, resource mapping, virtualization, cloud computing, quality of service
Ç
1 INTRODUCTION
CLOUD computing promises reliable services deliveredthrough next generation data centers that are built on
compute and storage virtualization technologies. According
to Buyya et al., [1] “a cloud is a type of parallel and distributed
system consisting of a collection of interconnected and virtualized
computers that are dynamically provisioned and presented as one
or more unified computing resources based on service-level
agreements established through negotiation between the service
provider and the consumers” and accessible as a composable
service via web 2.0 technologies.
Therefore, with respect to cloud computing there exist
the “as a service” definitions, which include software as a
service (SaaS), infrastructure as a se.
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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Efficient Resource Sharing In Cloud Using Neural NetworkIJERA Editor
In cloud computing, collaborative cloud computing(CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. In previous research, Harmony enables a node to locate its desired resources and also find the reputation of the located resources, so that a client can choose resource providers not only by resource availability but also by the provider’s reputation of providing the resource. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
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1. Resource Mapping Optimization for Distributed Cloud Services
Resource Mapping Optimization
for Distributed Cloud Services
PhD Thesis Defense
Atakan Aral
Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman
Istanbul Technical University – Department of Computer Engineering
November 3, 2016
2. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Motivation
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
6. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Motivation
Motivation
Scientific computing
Internet of Things
Finance services
Health care systems
e-Learning
(Mobile) image processing, VR
Social networking
On-demand or live video streaming
Online gaming
. . .
Real time, distributed, data- and computation-intensive cloud services
need low latency and low-cost communication.
7. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
8. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Cloud Computing
Definition
Applications and services that run on a distributed network using virtualized
resources and accessed by common Internet protocols and networking standards.
Broad network access: Platform-independent, via standard methods
Measured service: Pay-per-use, e.g. amount of storage/processing power,
number of transactions, bandwidth etc.
On-demand self-service: No need to contact provider to provision resources
Rapid elasticity: Automatic scale up/out, illusion of infinite resources
Resource pooling: Abstraction, virtualization, multi-tenancy
9. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Cloud Computing – Service Models
10. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Cloud Computing – Deployment Models
11. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Inter-Cloud
Definition
A cloud model that allows on-demand reassignment of resources and transfer of
workload through an interworking of cloud systems of different cloud providers.
Depending on the initiator of the Inter-Cloud endeavour:
Cloud Federation: A voluntary collaboration between a group of cloud
providers to exchange their resources
Multi-Cloud: An uninformed combination of multiple clouds by a service
provider to host the components of the service
12. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Preliminary Information
Edge Computing
Definition
Pushing the frontier of computing applications, data, and services away from
centralized nodes to the logical extremes of a network (e.g. mobile devices,
sensors, micro data centers, cloudlets, routers, modems, . . .
Low latency access to powerful computing resources
Cyber-foraging: Computation or data offloading from mobile devices to
servers for extending computation power, storage capacity and/or battery life.
13. Resource Mapping Optimization for Distributed Cloud Services
Introduction
General Problem
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
14. Resource Mapping Optimization for Distributed Cloud Services
Introduction
General Problem
General Problem
Deployment on multiple clouds can benefit cloud services
Allows seemingly infinite scalability,
Provides better geographical coverage,
Avoids vendor lock-in and eases hybridization,
Increases fault tolerance and availability,
Allows exploiting differences in pricing schemes, . . .
Business solutions that allow basic inter-cloud deployment are already here,
e.g. Nuvla, EGI, RightScale, Equinix, . . .
However, such solutions leave cloud data center selection to user.
There is no automatic mapping between provider resources and user
requirements.
15. Resource Mapping Optimization for Distributed Cloud Services
Introduction
General Problem
General Problem (continued)
Resource mapping in distributed cloud is a complex problem due to factors
such as:
Multiple objectives and perspectives (QoS, access latency, throughput,
availability, cost, profit, . . . ),
Constraints (SLAs, processing capacity, network bandwidth, energy
consumption, . . . ),
Geographical distribution and nonuniform network conditions,
Heterogeneity and dynamicity of both entities (i.e. resources and requests),
The large number of the entities (especially in the case of Edge Computing),
Inter-dependencies among the components that constitute a cloud service (e.g.
Virtual Machines, Databases).
16. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Solution Proposal
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
17. Resource Mapping Optimization for Distributed Cloud Services
Introduction
Solution Proposal
Proposed Solution
Hypothesis
Employing resource mapping algorithms that consider and utilize structural
characteristics of the distributed cloud services would increase the QoS
experienced by service users in a cost-efficient way.
QoS indicators are: (i) access latency to the service, (ii) access latency
between dependent components, and (iii) access latency to the data.
Cost indicators are: (i) VM provisioning cost, (ii) data storage cost, and (iii)
data transfer (bandwidth) cost.
18. V MnV M1 V M2
...
Service ModelEntity
Cloud based Service SaaS
Resource Mapper PaaS
Topology Mapper Replication Manager
• Replica Placement
• Replica Discovery
• Network Embedding
• Resource Allocation
DB
Cloud Infrastructure IaaS
DC1
DC2
DC4 DC5
DC3
19. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Motivating Example
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
20. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Motivating Example
Motivating Example
A small cloud-based navigation service for public transportation
Two-tier architecture: User interface and route planning
VM 1: 8 CPU cores, 16 GB memory, 2 TB HDD storage
VM 2: 32 CPU cores, 60 GB memory, 80 GB SSD storage
500 Mbps of dedicated bandwidth between the two tiers is desired
21. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Motivating Example
Motivating Example (continued)
First tier is replicated in two
separate DCs to improve:
Availability
Fault tolerance
Proximity
Second tier is not replicated
due to:
Economical constraints
Uncriticality to the service
But it is still deployed on a
third DC because of:
Pricing differences
Fairness
22. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Motivating Example
Motivating Example (continued)
There are 5 (federated) cloud providers in the area.
Not all have dedicated
network connections
between them.
Heterogeneous bandwidth
capacities and latencies
Heterogeneous resource
capacities and loads
How to map user virtual machines to cloud data centers?
23. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Problem Definition
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
25. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Problem Definition
Virtual Machine Cluster Embedding (continued)
GC = VC, EC, AV
C, AE
C
GF = VF , EF , AV
F , AE
F
∀ (v ∈ VC) ∃ v ∈ VF | v → v
∀ (e ∈ EC) ∃ E ⊆ EF | e → E
Validity conditions:
1 Capacity sufficiency
2 Path creation
3 No cycle forming
CP4
CP1
CP5
CP2
CP3
VM1 VM3VM2
CP4
CP1
CP5
CP2VM1
VM2VM3
CP3
f = (VM1 → CP3, VM2 → CP5, VM3 → CP4)
g = D1,2 → {C1,3, C3,5}, D2,3 → {C4,5}
26. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
27. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Topology based Mapping (TBM) Algorithm
Map VM clusters to the subgraphs of the federation topology that are
isomorphic to their topology.
Mapping function f is injective and ∀ (e ∈ EC) ∃ (E ⊆ EF ) | e → E ∧ |E | = 1
In case of multiple alternatives, choose by average latency to the user.
Fall back to a greedy heuristic in case of failure.
Instead, map to a homeomorphic subgraph where |E | ≥ 1
28. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Topology based Mapping (TBM) Algorithm (continued)
29. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Topology based Mapping (TBM) Algorithm (continued)
30. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Topology based Mapping (TBM) Algorithm (continued)
31. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Solution Details
Topology based Mapping (TBM) Algorithm (continued)
32. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Evaluation
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
33. Resource Mapping Optimization for Distributed Cloud Services
Topology Mapping
Evaluation
Experimental Setup
Simulated on the RalloCloud framework which is based on CloudSim.
Federation topology is taken from the FEDERICA project and contains 14
clouds across Europe.
Virtual machine clusters are randomly generated based on population density.
Simulation period is 50 hours.
45. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Motivating Examples
Motivating Examples
Edge Computing provides low-latency access to computing resources for
mobile code offloading.
However, many services need to access data that is stored centrally due to:
Limited storage capacity of the edge entities
Economic constraints
Availability for offline analysis
Simpler maintenance and concurrency control
High latency to central storage harms QoS.
How to decide the number and location of data replicas so that the data
access latency is decreased in a cost efficient way?
46. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Motivating Examples
Locality of Reference
The patterns that the user data accesses exhibit: Temporal Locality, Spatial
Locality, and Geographical Locality
[0,0.5)
[0.5,1)
[1,1.5)
[1.5,2)
[2,2.5)
[2.5,3)
[3,3.5)
[3.5,4)
[4,4.5)
[4.5,5)
[5,5.5)
[5.5,6)
[6,6.5)
[6.5,7)
[7,7.5)
[7.5,8)
[8,8.5)
[8.5,9)
[9,9.5)
[9.5,10)
[10,10.5)
[10.5,11)
[11,11.5)
[11.5,12)
[12,12.5)
[12.5,13)
[13,13.5)
[13.5,14)
[14,14.5)
[14.5,15)
[15,15.5)
[15.5,16)
[16,16.5)
[16.5,17)
[17,17.5)
[17.5,18)
[18,18.5)
[18.5,19)
[19,19.5)
[19.5,20)
0
2
4
6
·104
Distance (1, 000 × km)
NumberofRequestPairs
The CAIDA Anonymized Internet Traces 2015 Dataset (2.3 Billion IPv4 packets)
47. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Problem Definition
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
48. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Problem Definition
Edge Replica Placement
a
c
b d cs
1 Which data objects to replicate?
2 When to create/destroy a replica?
3 How many replicas for each object?
4 Where to store each replica?
5 How to redirect requests to the
closest replica?
In order to minimize average replica-to-client distance in a bandwidth-
and cost-effective way.
49. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Problem Definition
Facility Location Problem
minimize :
j
fj · Yj +
i j
hi · dij · Xij
fj = unit_pricej · replica_size · epoch
hi = num_requestsi · replica_size
dij = latencyij · λ
50. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Problem Definition
Requirements
Centralized solutions are not feasible due to the large number of entities.
The solution must be distributed with minimal input and communication.
Edge users continuously enter and leave the network topology through
connections with nonuniform latencies.
The solution must be dynamic and online.
Edge entities should be aware of the closest replica when they need a certain
data object.
A Replica Discovery technique is necessary.
51. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Solution Details
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
52. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Solution Details
Decentralized Replica Placement (D-ReP) Algorithm
Storage nodes that host replicas act as local optimizers.
They evaluate experienced demand, storage cost as well as expected latency
improvement to carry out either:
Duplication num_requestsknh · latencynh · λ > unit_pricen · epoch
Migration (num_reqsknh − i∈N
i=n
num_reqskih) · latencynh · λ >
unit_pricen − unit_priceh · epoch
Removal i∈N num_requestskih < original_num_requestsh · α
The replicas are incrementally pushed from the central storage to the edge.
λ allows user to control the trade-off between cost- and latency-optimization.
53. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Solution Details
Replica Discovery
Only the most relevant nodes are notified of the replica creations or removals.
Temporal locality: requesters of the source during the n most recent epochs
Geographical locality: m-hop sphere of the destination
Each active node keeps a Known Replica Locations (KRL) table.
54. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Evaluation
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
55. Resource Mapping Optimization for Distributed Cloud Services
Replication Management
Evaluation
Experimental Setup
Simulated on the RalloCloud framework which is based on CloudSim.
The CAIDA Anonymized Internet Traces 2015 Dataset: IPv4 packets data
from February 19, 2015 between 13:00-14:00 (UTC) which contains more
than 2.3 Billion records
GeoLite2 IP geolocation database
Synthetic workloads based on uniform, exponential, normal, Chi-squared, and
Pareto distributions of request locations
Barabási–Albert scale-free network generation model: 1000 nodes, 2994
edges, and a heavy-tailed distribution of bandwidth in [10, 1024] mbps
Amazon Web Services S3 prices
61. Resource Mapping Optimization for Distributed Cloud Services
Conclusion
Outline
1 Introduction
Motivation
Preliminary Information
General Problem
Solution Proposal
2 Topology Mapping
Motivating Example
Problem Definition
Solution Details
Evaluation
3 Replication Management
Motivating Examples
Problem Definition
Solution Details
Evaluation
4 Conclusion
62. Resource Mapping Optimization for Distributed Cloud Services
Conclusion
Contribution
Topology Mapping Algorithm1 4 5
First attempt to employ subgraph isomorphism to find a injective match between
virtual and physical cloud/grid topologies
First to explicitly evaluate network latency for the VMNE
Minimum Span Heuristic3 5
Novel heuristic algorithm to defer MIP
Decentralized Replica Placement Algorithm2 5
First completely decentralized, partial-knowledge replica placement algorithm
KRL based Discovery Technique2
Novel replica discovery technique with low overhead
RalloCloud: A simulation environment for Inter-Cloud resource mapping1
First simulator for inter-cloud systems
63. Resource Mapping Optimization for Distributed Cloud Services
Conclusion
Publications
1 Aral, A., and Ovatman, T. 2016. Network-Aware Embedding of Virtual Machine Clusters onto Federated
Cloud Infrastructure. The Journal of Systems and Software, vol. 120, pp. 89-104. DOI:
10.1016/j.jss.2016.07.007
2 Aral, A., and Ovatman, T. 2017?. A Decentralized Replica Placement Algorithm for Edge Computing.
Under review in The IEEE Transactions on Parallel and Distributed Systems.
3 Aral, A., and Ovatman, T. 2014. Improving Resource Utilization in Cloud Environments using Application
Placement Heuristics. In 4th Int’l. Conf. on Cloud Comp. and Services Science (CLOSER 2014), pp.
527-534.
4 Aral, A., and Ovatman, T. 2015. Subgraph Matching for Resource Allocation in the Federated Cloud
Environment. In IEEE 8th International Conference on Cloud Computing (IEEE CLOUD 2015), pp.
1033-1036.
5 Aral, A. 2016. Network-Aware Resource Allocation in Distributed Clouds. IEEE International Conference
on Cloud Engineering (IC2E 2016), Doctoral Symposium.
64. Resource Mapping Optimization for Distributed Cloud Services
Conclusion
Future Work
Standardization of cloud APIs
Real-time performance and cost guarantees
Service self-awareness
Load balancing and fairness between cloud providers
Fault tolerance and availability
69. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Virtual Network Embedding
Cloud Federation Embedding Entity Simultaneous NW-Aware
TBM VMs
[50] VNs
[51] VMs
[52] Jobs
[53] WEs
70. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Virtual Network Embedding
[25] Tordsson, J., Montero, R.S., Moreno-Vozmediano, R. and Llorente, I.M. (2012). Cloud brokering
mechanisms for optimized placement of virtual machines across multiple providers, Future Generation
Computer Systems, 28(2), 358–367.
Exact solution with IP, cloud brokerage and uniform interface
[35] Leivadeas, A., Papagianni, C. and Papavassiliou, S. (2013). Efficient resource mapping framework over
networked clouds via iterated local search-based request partitioning, IEEE Transactions on Parallel and
Distributed Systems, 24(6), 1077–1086.
Graph partitioning, IP based embedding, no latency consideration
[38] Xin, Y., Baldine, I., Mandal, A., Heermann, C., Chase, J. and Yumerefendi, A. (2011). Embedding virtual
topologies in networked clouds, Proceedings of the 6th International Conference on Future Internet
Technologies, ACM, pp.26–29.
Connected components of the VM cluster topology are merged,
isomorphic subgraph to the resulting graph is sought
71. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Replica Placement (Centralized)
P D Environment Topology Objectives
[68] Web Tree Proximity
[69] CDN Unrestricted Proximity
[70] N/A Unrestricted Proximity
[71] Data Grid Tree Proximity, Cost, Load Balance
[72] N/A Tree Proximity
[73] N/A Unrestricted Proximity
[65] Cloud Unrestricted Proximity
[74] Cloud Unrestricted Bandwidth, Load Balance
[67] Cloud Unrestricted Proximity, Cost
[75] N/A Unrestricted Availability
[76] Data Grid Multi-Tier Proximity, Cost, Bandwidth
[77] CDN Unrestricted Proximity, Cost
72. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Replica Placement (Centralized)
P D Environment Topology Objectives
[63] Cloud Unrestricted Prox., Bandwidth, Load Balance
[78] Data Grid Multi-Tier Proximity
[64] Data Grid Unrestricted Prox., Bandwidth, Availability
[66] Cloud-CDN Unrestricted Proximity, Cost
[79] Data Grid Tree Prox., Bandwidth, Availability
[80] Data Grid Multi-Tier Proximity, Bandwidth
[81] Cloud Tree Proximity, Availability
[82] Cloud Unrestricted Bandwidth, Load Balance
[83] Cloud-CDN Unrestricted Cost, Availability
[84] Cloud (Mobile) Unrestricted Proximity, Availability
73. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Replica Placement (Decentralized)
P D Environment Topology Objectives
[89] Cloud-CDN Unrestricted Proximity
[90] Cloud Complete Prox., Cost, Bandwidth, Avail.
[91] Data Grid Unrestricted Prox., Cost, Bandwidth, Avail.
[92] P2P Unrestricted Cost, Bandwidth, Availability
[93] Web Unrestricted Proximity, Bandwidth
[94] N/A Multi-Tier Proximity, Bandwidth
[88] Web Unrestricted Prox., Cost, Load B., Bandwidth
[95] P2P Unrestricted Proximity, Cost
[87] Web Unrestricted Proximity, Cost
[96] Web Unrestricted Proximity, Cost
D-ReP Cloud Unrestricted Proximity, Cost, Bandwidth
74. Resource Mapping Optimization for Distributed Cloud Services
Literature Review
Replica Placement
[90] Bonvin, N., Papaioannou, T.G. and Aberer, K. (2010). A self-organized, fault-tolerant and scalable replica-
tion scheme for cloud storage, Proceedings of the 1st ACM symposium on Cloud computing, pp. 205–216.
Game theoretical, replicate/migrate autonomously, each node is aware of
other replicas, prices, demand, bandwidth, . . .
[95] Shen, H. (2010). An efficient and adaptive decentralized file replication algorithm in P2P file sharing
systems, IEEE Transactions on Parallel and Distributed Systems, 21(6), 827–840.
Replicas on data access path intersections (traffic hubs), traffic analysis
[87] Pantazopoulos, P., Karaliopoulos, M. and Stavrakakis, I. (2014). Distributed placement of autonomic
internet services, IEEE Transactions on Parallel and Distributed Systems, 25(7), 1702–1712.
[96] Smaragdakis, G., Laoutaris, N., Oikonomou, K., Stavrakakis, I. and Bestavros, A. (2014). Distributed
server migration for scalable Internet service deployment, IEEE/ACM Trans. on NW, 22(3), 917–930.
Solve FLP in an r-ball, centrality, demand and FLP decisions are broadcasted
77. Resource Mapping Optimization for Distributed Cloud Services
RalloCloud
Network Modeling
Requested bandwidth between two VMs is allocated from all edges on the
shortest path between the clouds to which two VMs are deployed.
Three types of latency are considered:
Deployment Latency = M +
i∈P1
Li +
S
B
Communication Latency =
i∈P2
Li +
D
B
Data Access Latency =
i∈P3
Li +
D
B
78. Resource Mapping Optimization for Distributed Cloud Services
RalloCloud
Cost Modeling
Static Pricing and Trough Filling strategies
Total Service Cost =
i∈VC j∈AV
C
resource_sizeij · unit_priceij · durationij
+
i∈EC
resource_sizei · unit_pricei · durationi
+
i
replica_sizei · unit_pricei · durationi
79. Resource Mapping Optimization for Distributed Cloud Services
RalloCloud
Performance Criteria
Criteria
Temporal
Latency
Cloud-to-User Latency
Hop Count
Inter-Cloud Latency
Duration
Completion Time
Execution Time
Makespan
SLA Violations
Volumetrical
Throughput
Utilization Rate
Economical
Benefit-Cost Ratio
Resource Cost
Revenue
Distributional
Distribution Rate
Load Balance
Other
Acceptance/Rejection Rate
Fairness
83. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
D-ReP
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
84. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
User demand locations
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
85. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 1d: User demand received from c and f
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
86. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 1d: Cache creation decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
87. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 2f: Migration decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
88. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 2c: Duplication decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
89. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 3e: Migration decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
90. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 3a: Migration decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
91. Resource Mapping Optimization for Distributed Cloud Services
Algorithm Details
ITERATION 3c: Removal decision
a
b
c
d
f
e
a1
a2
a3
a4
e1
b3
b2
b1
e4
e2
e3
f3
d1
f1
f2
d2
c1 c2
93. Resource Mapping Optimization for Distributed Cloud Services
Intra-Cloud Mapping
Virtual Machine Cluster Embedding
Allocation of PMs to VMs in a single cloud data center.
Increase resource
utilization
Increase data center
throughput
Increase acceptance rate
Increase revenue
#PMs
i=0
#VMs
j=0
A[i][j] = 1
#PMs
i=0
#Res
j=0
#VMs
k=0
A[i][k] × RR[j][k]
≤ AR[i][j]
94. Resource Mapping Optimization for Distributed Cloud Services
Intra-Cloud Mapping
Minimum Span Heuristic
Map a VM to the PM with the maximum resource utilization evenness.
Span(p) = maxi∈[1,m] (ri) − mini∈[1,m] (ri)
Fall back to a Mixed Integer Programming (MIP) solution in case of failure.
96. Resource Mapping Optimization for Distributed Cloud Services
Intra-Cloud Mapping
Other Heuristics
SD(v) =
m
i=1
(ri − ¯r)2
CD(v) =
m
i=1
m
j=1 |ri − rj|
2
DM(v) =
n
i=1
ri − min
j∈[1,m]
rj
SK(v) =
m
i=1
ri
¯r
− 1
2
100. Resource Mapping Optimization for Distributed Cloud Services
Subgraph Matching
Subgraph Matching
Search space is all possible injective matchings from the set of pattern nodes
to the set of target nodes.
Systematically explore the search space:
Start from an empty matching
Extend the partial matching by matching a non matched pattern node to a non
matched target node
Backtrack if some edges are not matched
Repeat until all pattern nodes are matched (success) or all matchings are
already explored (fail).
Filters are necessary to reduce the search space by pruning branches that do
not contain solutions.