The Experts below are selected from a list of 2019 Experts worldwide ranked by ideXlab platform
Ying Song - One of the best experts on this subject based on the ideXlab platform.
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analysis model for Server Consolidation of virtualized heterogeneous data centers providing internet services
Cluster Computing, 2019Co-Authors: Bo Wang, Ying SongAbstract:Server Consolidation based on virtualization technology simplifies system administration, reduces the cost of power and physical infrastructure, and improves resource utilizations in today’s service-oriented Internet data centers. How many Servers for the underlying physical infrastructure are saved via Server Consolidation in virtualized data centers is of great interest to the administrators and designers of the data centers. Various workload Consolidations differ in saving physical Servers for the infrastructure. The impacts caused by virtualization to these concurrent services are fluctuating considerably which may have a great effect on Server Consolidation. This paper proposes an analytic model for Server Consolidation in virtualized Internet data centers based on the queuing theory. According to the features of these services’ workloads, this model can provide the supremum number of consolidated physical Servers needed to guarantee QoS with same loss probabilities of requests as in dedicated Servers. We verify the model via a case study. The experiments results confirm the superior accuracy of our model and show that the virtual machine-based Server Consolidation saves up to 50% physical infrastructure and improves 50% CPU resource utilization as well as 2.67 times in I/O bandwidth utilization, satisfying required QoS.
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Mathematical programming for Server Consolidation in cloud data centers
2017 4th International Conference on Systems and Informatics (ICSAI), 2017Co-Authors: Bo Wang, Ying SongAbstract:Server Consolidation based on virtualization technology simplifies system administration and improves energy efficiency by improving resource utilizations and reducing the physical machine (PM) number in contemporary service-oriented data centers. These benefits prompt service providers to deliver their services on virtualized data centers. For a service provider, their total costs are mainly composed of the investment costs for buying infrastructures, such as the ownership costs of PMs, and the operational costs, such as the electricity costs for powering PMs, cooling, lighting and so on. Plenty work has studied on minimizing the operational costs. On the contrary, we study on the scale planning for minimizing the investment costs for building/updating data centers providing Internet services, in this paper. We model the scale planning as an integer program minimizing the total ownership costs of PMs. Extensive experiments results show that our scale planning is better than the plannings made by static and dynamic Consolidations and that using the scale planning improves the computing times consumed by online Consolidations without impact on their performance.
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Server Consolidation for internet applications in virtualized data centers
arXiv: Distributed Parallel and Cluster Computing, 2016Co-Authors: Bo Wang, Ying SongAbstract:Server Consolidation based on virtualization technology simplifies system administration and improves energy efficiency by improving resource utilizations and reducing the physical machine (PM) number in contemporary service-oriented data centers. The elasticity of Internet applications changes the Consolidation technologies from addressing virtual machines (VMs) to PMs mapping schemes which must know the VMs statuses, i.e. the number of VMs and the profiling data of each VM, into providing the application-to-VM-to-PM mapping. In this paper, we study on the Consolidation of multiple Internet applications, minimizing the number of PMs with required performance. We first model the Consolidation providing the application-to-VM-to-PM mapping to minimize the number of PMs as an integer linear programming problem, and then present a heuristic algorithm to solve the problem in polynomial time. Extensive experimental results show that our heuristic algorithm consumes less than 4.3% more resources than the optimal amounts with few overheads. Existing Consolidation technologies using the input of the VM statuses output by our heuristic algorithm consume 1.06% more PMs.
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CLUSTER - Utility analysis for Internet-oriented Server Consolidation in VM-based data centers
2009 IEEE International Conference on Cluster Computing and Workshops, 2009Co-Authors: Ying Song, Yanwei ZhangAbstract:Server Consolidation based on virtualization technology will simplify system administration, reduce the cost of power and physical infrastructure, and improve utilization in today's Internet-service-oriented enterprise data centers. How much power and how many Servers for the underlying physical infrastructure are saved via Server Consolidation in VM-based data centers is of great interest to administrators and designers of those data centers. Various workload Consolidations differ in saving power and physical Servers for the infrastructure. The impacts caused by virtualization to those concurrent services are fluctuating considerably which may have a great effect on Server Consolidation. This paper proposes a utility analytic model for Internet-oriented Server Consolidation in VM-based data centers, modelling the interaction between Server arrival requests with several QoS requirements, and capability flowing amongst concurrent services, based on the queuing theory. According to features of those services' workloads, this model can provide the upper bound of consolidated physical Servers needed to guarantee QoS with the same loss probability of requests as in dedicated Servers. At the same time, it can also evaluate the Server Consolidation in terms of power and utility of physical Servers. Finally, we verify the model via a case study comprised of one e-book database service and one e-commerce Web service, simulated respectively by TPC-W and SPECweb2005 benchmarks. Our experiments show that the model is simple but accurate enough. The VM-based Server Consolidation saves up to 50% physical infrastructure, up to 53% power, and improves 1.7 times in CPU resource utilization, without any degradation of concurrent services' performance, running on Rainbow — our virtual computing platform.
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Utility analysis for Internet-oriented Server Consolidation in VM-based data centers
Proceedings - IEEE International Conference on Cluster Computing ICCC, 2009Co-Authors: Ying Song, Yuzhong Sun, Yanwei Zhang, Weisong ShiAbstract:Server Consolidation based on virtualization technology will simplify system administration, reduce the cost of power and physical infrastructure, and improve utilization in today's Internet-service-oriented enterprise data centers. How much power and how many Servers for the underlying physical infrastructure are saved via Server Consolidation in VM-based data centers is of great interest to administrators and designers of those data centers. Various workload Consolidations differ in saving power and physical Servers for the infrastructure. The impacts caused by virtualization to those concurrent services are fluctuating considerably which may have a great effect on Server Consolidation. This paper proposes a utility analytic model for Internet-oriented Server Consolidation in VM-based data centers, modelling the interaction between Server arrival requests with several QoS requirements, and capability flowing amongst concurrent services, based on the queuing theory. According to features of those services' workloads, this model can provide the upper bound of consolidated physical Servers needed to guarantee QoS with the same loss probability of requests as in dedicated Servers. At the same time, it can also evaluate the Server Consolidation in terms of power and utility of physical Servers. Finally, we verify the model via a case study comprised of one e-book database service and one e-commerce Web service, simulated respectively by TPC-W and SPECweb2005 benchmarks. Our experiments show that the model is simple but accurate enough. The VM-based Server Consolidation saves up to 50% physical infrastructure, up to 53% power, and improves 1.7 times in CPU resource utilization, without any degradation of concurrent services' performance, running on Rainbow - our virtual computing platform.
Huaimin Wang - One of the best experts on this subject based on the ideXlab platform.
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SmartCity - Storage-Aware Server Consolidation for Cloud Services Utilizing Local Storage
2015 IEEE International Conference on Smart City SocialCom SustainCom (SmartCity), 2015Co-Authors: Huaimin Wang, Bo Ding, Haibo MiAbstract:Server Consolidation is one of the critical techniques for energy-efficiency in cloud data centers. As it is often assumed that cloud service instances (e.g., Amazon EC2 instances) utilize the shared storage only, most existing work did not consider the problems introduced by utilizing local storage. In recent years, however, cloud service providers have been providing local storage for cloud users, e.g., Amazon EC2, Aliyun ECS and RDS, since local storage can offer a better performance with identified price. Thus, several problems might be incurred, e.g., migrating much more data, consuming much more migration time and network bandwidth. To address these problems, this paper proposes SaSercon, a storage-aware Server Consolidation approach to minimize the total migrated data size (stored on the local storage) by releasing the Servers which utilize lower data size. Evaluation results on production traces demonstrate that SaSercon significantly reduces the total migrated data size.
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Storage-Aware Server Consolidation for Cloud Services Utilizing Local Storage
2015 IEEE International Conference on Smart City SocialCom SustainCom (SmartCity), 2015Co-Authors: Huaimin Wang, Bo Ding, Haibo MiAbstract:Server Consolidation is one of the critical techniques for energy-efficiency in cloud data centers. As it is often assumed that cloud service instances (e.g., Amazon EC2 instances) utilize the shared storage only, most existing work did not consider the problems introduced by utilizing local storage. In recent years, however, cloud service providers have been providing local storage for cloud users, e.g., Amazon EC2, Aliyun ECS and RDS, since local storage can offer a better performance with identified price. Thus, several problems might be incurred, e.g., migrating much more data, consuming much more migration time and network bandwidth. To address these problems, this paper proposes SaSercon, a storage-aware Server Consolidation approach to minimize the total migrated data size (stored on the local storage) by releasing the Servers which utilize lower data size. Evaluation results on production traces demonstrate that SaSercon significantly reduces the total migrated data size.
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ICPADS - A Hierarchical Memory Service Mechanism in Server Consolidation Environment
2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011Co-Authors: Liufeng Wang, Huaimin Wang, Pengfei ZhangAbstract:Increasing Internet business and computing footprint motivate Server Consolidation in data centers. Through virtualization technology, Server Consolidation can reduce physical hosts and provide scalable services. However, the ineffective memory usage among multiple virtual machines (VMs) becomes the bottleneck in Server Consolidation environment. Because of inaccurate memory usage estimate and the lack of memory resource managements, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. In order to improve this scenario, we first introduce VM's memory division view and VM's free memory division view. Based on them, we propose a hierarchal memory service mechanism. We have designed and implemented the corresponding memory scheduling algorithm to enhance memory efficiency and achieve service level agreement. The benchmark test results show that our implementation can save 30% physical memory with 1% to 5% performance degradation. Based on Xen virtualization platform and balloon driver technology, our works actually bring dramatic benefits to commercial cloud computing center which is providing more than 2,000 VMs' services to cloud computing users.
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A Hierarchical Memory Service Mechanism in Server Consolidation Environment
2011 IEEE 17th International Conference on Parallel and Distributed Systems, 2011Co-Authors: Liufeng Wang, Huaimin Wang, Pengfei ZhangAbstract:Increasing Internet business and computing footprint motivate Server Consolidation in data centers. Through virtualization technology, Server Consolidation can reduce physical hosts and provide scalable services. However, the ineffective memory usage among multiple virtual machines (VMs) becomes the bottleneck in Server Consolidation environment. Because of inaccurate memory usage estimate and the lack of memory resource managements, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. In order to improve this scenario, we first introduce VM's memory division view and VM's free memory division view. Based on them, we propose a hierarchal memory service mechanism. We have designed and implemented the corresponding memory scheduling algorithm to enhance memory efficiency and achieve service level agreement. The benchmark test results show that our implementation can save 30% physical memory with 1% to 5% performance degradation. Based on Xen virtualization platform and balloon driver technology, our works actually bring dramatic benefits to commercial cloud computing center which is providing more than 2,000 VMs' services to cloud computing users.
Shahin Kamali - One of the best experts on this subject based on the ideXlab platform.
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Robust Multi-tenant Server Consolidation in the Cloud for Data Analytics Workloads
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenants on a Server machine. Given a sequence of data analytics tenant loads defined by the amount of resources that the tenants require and a service-level agreement (SLA) between the customer and the cloud service provider, significant cost savings can be achieved by consolidating multiple tenants. Since Server machines can fail causing their tenants to become unavailable, service providers can place replicas of each tenant on multiple Servers and reserve capacity to ensure that tenant failover will not result in overload on any remaining Server. We present the CUBEFIT algorithm for Server Consolidation that reduces costs by utilizing fewer Servers than existing approaches for data analytics workloads. Unlike existing Consolidation algorithms, CUBEFIT can tolerate multiple Server failures while ensuring that no Server becomes overloaded. Through theoretical analysis and experimental evaluation, we show that CUBEFIT is superior to existing algorithms and produces near-optimal tenant allocation when the number of tenants is large. Through evaluation and deployment on a cluster of 73 machines as well as through simulation studies, we experimentally demonstrate the efficacy of CUBEFIT.
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robust multi tenant Server Consolidation in the cloud for data analytics workloads
International Conference on Distributed Computing Systems, 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenantson a Server machine. Given a sequence of data analyticstenant loads defined by the amount of resources that thetenants require and a service-level agreement (SLA) between thecustomer and the cloud service provider, significant cost savingscan be achieved by consolidating multiple tenants. Since Servermachines can fail causing their tenants to become unavailable,service providers can place replicas of each tenant on multipleServers and reserve capacity to ensure that tenant failover willnot result in overload on any remaining Server. We present theCubeFit algorithm for Server Consolidation that reduces costsby utilizing fewer Servers than existing approaches for dataanalytics workloads. Unlike existing Consolidation algorithms,CubeFit can tolerate multiple Server failures while ensuring thatno Server becomes overloaded. Through theoretical analysis andexperimental evaluation, we show that CubeFit is superior toexisting algorithms and produces near-optimal tenant allocationwhen the number of tenants is large. Through evaluation anddeployment on a cluster of 73 machines as well as throughsimulation studies, we experimentally demonstrate the efficacyof CubeFit.
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ICDCS - Robust Multi-tenant Server Consolidation in the Cloud for Data Analytics Workloads
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenantson a Server machine. Given a sequence of data analyticstenant loads defined by the amount of resources that thetenants require and a service-level agreement (SLA) between thecustomer and the cloud service provider, significant cost savingscan be achieved by consolidating multiple tenants. Since Servermachines can fail causing their tenants to become unavailable,service providers can place replicas of each tenant on multipleServers and reserve capacity to ensure that tenant failover willnot result in overload on any remaining Server. We present theCubeFit algorithm for Server Consolidation that reduces costsby utilizing fewer Servers than existing approaches for dataanalytics workloads. Unlike existing Consolidation algorithms,CubeFit can tolerate multiple Server failures while ensuring thatno Server becomes overloaded. Through theoretical analysis andexperimental evaluation, we show that CubeFit is superior toexisting algorithms and produces near-optimal tenant allocationwhen the number of tenants is large. Through evaluation anddeployment on a cluster of 73 machines as well as throughsimulation studies, we experimentally demonstrate the efficacyof CubeFit.
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on the online fault tolerant Server Consolidation problem
ACM Symposium on Parallel Algorithms and Architectures, 2014Co-Authors: Khuzaima Daudjee, Shahin Kamali, Alejandro LopezortizAbstract:In the Server Consolidation problem, the goal is to minimize the number of Servers needed to host a set of clients. The clients appear in an online manner and each of them has a certain load. The Servers have uniform capacity and the total load of clients assigned to a Server must not exceed this capacity. Additionally, to have a fault-tolerant solution, the load of each client should be distributed between at least two different Servers so that failure of one Server avoids service interruption by migrating the load to the other Servers hosting the respective second loads. In a simple setting, upon receiving a client, an online algorithm needs to select two Servers and assign half of the load of the client to each Server. We analyze the problem in the framework of competitive analysis. First, we provide upper and lower bounds for the competitive ratio of two well known heuristics which are introduced in the context of tenant placement in the cloud. In particular, we show their competitive ratios are no better than 2. We then present a new algorithm called Horizontal Harmonic and show that it has an improved competitive ratio which converges to 1.59. The simplicity of this algorithm makes it a good choice for use by cloud service providers. Finally, we prove a general lower bound that shows any online algorithm for the online fault-tolerant Server Consolidation problem has a competitive ratio of at least 1.42.
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SPAA - On the online fault-tolerant Server Consolidation problem
Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures, 2014Co-Authors: Khuzaima Daudjee, Shahin Kamali, Alejandro López-ortizAbstract:In the Server Consolidation problem, the goal is to minimize the number of Servers needed to host a set of clients. The clients appear in an online manner and each of them has a certain load. The Servers have uniform capacity and the total load of clients assigned to a Server must not exceed this capacity. Additionally, to have a fault-tolerant solution, the load of each client should be distributed between at least two different Servers so that failure of one Server avoids service interruption by migrating the load to the other Servers hosting the respective second loads. In a simple setting, upon receiving a client, an online algorithm needs to select two Servers and assign half of the load of the client to each Server. We analyze the problem in the framework of competitive analysis. First, we provide upper and lower bounds for the competitive ratio of two well known heuristics which are introduced in the context of tenant placement in the cloud. In particular, we show their competitive ratios are no better than 2. We then present a new algorithm called Horizontal Harmonic and show that it has an improved competitive ratio which converges to 1.59. The simplicity of this algorithm makes it a good choice for use by cloud service providers. Finally, we prove a general lower bound that shows any online algorithm for the online fault-tolerant Server Consolidation problem has a competitive ratio of at least 1.42.
Haibo Mi - One of the best experts on this subject based on the ideXlab platform.
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SmartCity - Storage-Aware Server Consolidation for Cloud Services Utilizing Local Storage
2015 IEEE International Conference on Smart City SocialCom SustainCom (SmartCity), 2015Co-Authors: Huaimin Wang, Bo Ding, Haibo MiAbstract:Server Consolidation is one of the critical techniques for energy-efficiency in cloud data centers. As it is often assumed that cloud service instances (e.g., Amazon EC2 instances) utilize the shared storage only, most existing work did not consider the problems introduced by utilizing local storage. In recent years, however, cloud service providers have been providing local storage for cloud users, e.g., Amazon EC2, Aliyun ECS and RDS, since local storage can offer a better performance with identified price. Thus, several problems might be incurred, e.g., migrating much more data, consuming much more migration time and network bandwidth. To address these problems, this paper proposes SaSercon, a storage-aware Server Consolidation approach to minimize the total migrated data size (stored on the local storage) by releasing the Servers which utilize lower data size. Evaluation results on production traces demonstrate that SaSercon significantly reduces the total migrated data size.
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Storage-Aware Server Consolidation for Cloud Services Utilizing Local Storage
2015 IEEE International Conference on Smart City SocialCom SustainCom (SmartCity), 2015Co-Authors: Huaimin Wang, Bo Ding, Haibo MiAbstract:Server Consolidation is one of the critical techniques for energy-efficiency in cloud data centers. As it is often assumed that cloud service instances (e.g., Amazon EC2 instances) utilize the shared storage only, most existing work did not consider the problems introduced by utilizing local storage. In recent years, however, cloud service providers have been providing local storage for cloud users, e.g., Amazon EC2, Aliyun ECS and RDS, since local storage can offer a better performance with identified price. Thus, several problems might be incurred, e.g., migrating much more data, consuming much more migration time and network bandwidth. To address these problems, this paper proposes SaSercon, a storage-aware Server Consolidation approach to minimize the total migrated data size (stored on the local storage) by releasing the Servers which utilize lower data size. Evaluation results on production traces demonstrate that SaSercon significantly reduces the total migrated data size.
Khuzaima Daudjee - One of the best experts on this subject based on the ideXlab platform.
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Robust Multi-tenant Server Consolidation in the Cloud for Data Analytics Workloads
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenants on a Server machine. Given a sequence of data analytics tenant loads defined by the amount of resources that the tenants require and a service-level agreement (SLA) between the customer and the cloud service provider, significant cost savings can be achieved by consolidating multiple tenants. Since Server machines can fail causing their tenants to become unavailable, service providers can place replicas of each tenant on multiple Servers and reserve capacity to ensure that tenant failover will not result in overload on any remaining Server. We present the CUBEFIT algorithm for Server Consolidation that reduces costs by utilizing fewer Servers than existing approaches for data analytics workloads. Unlike existing Consolidation algorithms, CUBEFIT can tolerate multiple Server failures while ensuring that no Server becomes overloaded. Through theoretical analysis and experimental evaluation, we show that CUBEFIT is superior to existing algorithms and produces near-optimal tenant allocation when the number of tenants is large. Through evaluation and deployment on a cluster of 73 machines as well as through simulation studies, we experimentally demonstrate the efficacy of CUBEFIT.
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robust multi tenant Server Consolidation in the cloud for data analytics workloads
International Conference on Distributed Computing Systems, 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenantson a Server machine. Given a sequence of data analyticstenant loads defined by the amount of resources that thetenants require and a service-level agreement (SLA) between thecustomer and the cloud service provider, significant cost savingscan be achieved by consolidating multiple tenants. Since Servermachines can fail causing their tenants to become unavailable,service providers can place replicas of each tenant on multipleServers and reserve capacity to ensure that tenant failover willnot result in overload on any remaining Server. We present theCubeFit algorithm for Server Consolidation that reduces costsby utilizing fewer Servers than existing approaches for dataanalytics workloads. Unlike existing Consolidation algorithms,CubeFit can tolerate multiple Server failures while ensuring thatno Server becomes overloaded. Through theoretical analysis andexperimental evaluation, we show that CubeFit is superior toexisting algorithms and produces near-optimal tenant allocationwhen the number of tenants is large. Through evaluation anddeployment on a cluster of 73 machines as well as throughsimulation studies, we experimentally demonstrate the efficacyof CubeFit.
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ICDCS - Robust Multi-tenant Server Consolidation in the Cloud for Data Analytics Workloads
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Joseph Mate, Khuzaima Daudjee, Shahin KamaliAbstract:Server Consolidation is the hosting of multiple tenantson a Server machine. Given a sequence of data analyticstenant loads defined by the amount of resources that thetenants require and a service-level agreement (SLA) between thecustomer and the cloud service provider, significant cost savingscan be achieved by consolidating multiple tenants. Since Servermachines can fail causing their tenants to become unavailable,service providers can place replicas of each tenant on multipleServers and reserve capacity to ensure that tenant failover willnot result in overload on any remaining Server. We present theCubeFit algorithm for Server Consolidation that reduces costsby utilizing fewer Servers than existing approaches for dataanalytics workloads. Unlike existing Consolidation algorithms,CubeFit can tolerate multiple Server failures while ensuring thatno Server becomes overloaded. Through theoretical analysis andexperimental evaluation, we show that CubeFit is superior toexisting algorithms and produces near-optimal tenant allocationwhen the number of tenants is large. Through evaluation anddeployment on a cluster of 73 machines as well as throughsimulation studies, we experimentally demonstrate the efficacyof CubeFit.
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on the online fault tolerant Server Consolidation problem
ACM Symposium on Parallel Algorithms and Architectures, 2014Co-Authors: Khuzaima Daudjee, Shahin Kamali, Alejandro LopezortizAbstract:In the Server Consolidation problem, the goal is to minimize the number of Servers needed to host a set of clients. The clients appear in an online manner and each of them has a certain load. The Servers have uniform capacity and the total load of clients assigned to a Server must not exceed this capacity. Additionally, to have a fault-tolerant solution, the load of each client should be distributed between at least two different Servers so that failure of one Server avoids service interruption by migrating the load to the other Servers hosting the respective second loads. In a simple setting, upon receiving a client, an online algorithm needs to select two Servers and assign half of the load of the client to each Server. We analyze the problem in the framework of competitive analysis. First, we provide upper and lower bounds for the competitive ratio of two well known heuristics which are introduced in the context of tenant placement in the cloud. In particular, we show their competitive ratios are no better than 2. We then present a new algorithm called Horizontal Harmonic and show that it has an improved competitive ratio which converges to 1.59. The simplicity of this algorithm makes it a good choice for use by cloud service providers. Finally, we prove a general lower bound that shows any online algorithm for the online fault-tolerant Server Consolidation problem has a competitive ratio of at least 1.42.
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SPAA - On the online fault-tolerant Server Consolidation problem
Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures, 2014Co-Authors: Khuzaima Daudjee, Shahin Kamali, Alejandro López-ortizAbstract:In the Server Consolidation problem, the goal is to minimize the number of Servers needed to host a set of clients. The clients appear in an online manner and each of them has a certain load. The Servers have uniform capacity and the total load of clients assigned to a Server must not exceed this capacity. Additionally, to have a fault-tolerant solution, the load of each client should be distributed between at least two different Servers so that failure of one Server avoids service interruption by migrating the load to the other Servers hosting the respective second loads. In a simple setting, upon receiving a client, an online algorithm needs to select two Servers and assign half of the load of the client to each Server. We analyze the problem in the framework of competitive analysis. First, we provide upper and lower bounds for the competitive ratio of two well known heuristics which are introduced in the context of tenant placement in the cloud. In particular, we show their competitive ratios are no better than 2. We then present a new algorithm called Horizontal Harmonic and show that it has an improved competitive ratio which converges to 1.59. The simplicity of this algorithm makes it a good choice for use by cloud service providers. Finally, we prove a general lower bound that shows any online algorithm for the online fault-tolerant Server Consolidation problem has a competitive ratio of at least 1.42.
