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50
PRESS: PRedictive Elastic ReSource Scaling for cloud systems
"... Abstract—Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic ..."
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Cited by 62 (8 self)
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Abstract—Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages light-weight signal processing and statistical learning algorithms to achieve online predictions of dynamic application resource requirements. We have implemented the PRESS system on Xen and tested it using RUBiS and an application load trace from Google. Our experiments show that we can achieve good resource prediction accuracy with less than 5 % over-estimation error and near zero under-estimation error, and elastic resource scaling can both significantly reduce resource waste and SLO violations. I.
Efficient resource provisioning in compute clouds via vm multiplexing,”
- in The 7th IEEE/ACM International Conference on Autonomic Computing and Communications,
, 2010
"... ABSTRACT Resource provisioning in compute clouds often requires an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with demand. In contrast to the traditional practice of estimating the size of VMs individually, we p ..."
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Cited by 54 (2 self)
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ABSTRACT Resource provisioning in compute clouds often requires an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with demand. In contrast to the traditional practice of estimating the size of VMs individually, we propose a joint-VM provisioning approach in which multiple VMs are consolidated and provisioned together, based on an estimate of their aggregate capacity needs. This new approach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be borrowed by the other co-located VMs with high utilization. Compared to individual-VM based provisioning, joint-VM provisioning could lead to much higher resource utilization. This paper presents three design modules to enable such a concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimating the aggregate size of multiplexed VMs; a VM selection algorithm that seeks to find those VM combinations with complementary workload patterns. We showcase that the proposed three modules can be seamlessly plugged into applications such as resource provisioning, and providing resource guarantees for VMs. The proposed method and applications are evaluated by performance data collected from about 16 thousand VMs in commercial data centers. The results demonstrate more than 45% improvements in terms of the overall resource utilization.
CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems
"... Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates finegrained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale emplo ..."
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Cited by 53 (6 self)
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Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates finegrained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (< 2 % CPU in Domain 0) to the virtualized computing cluster.
Benefits and Limitations of Tapping into Stored Energy For Datacenters
, 2011
"... Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this p ..."
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Cited by 53 (7 self)
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Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during “valleys”- periods of lower demand, which can be drained during “peaks ”- periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45 % peak power reduction, corresponding to 6-18 % savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy- typically lying untapped in the datacenter- to address the peak power draw problem.
Power budgeting for virtualized data centers
- In USENIX
, 2011
"... Power costs are very significant for data centers. To maximally utilize the provisioned power capacity, data centers often employ over-subscription, that is, the sum of peak consumptions of individual servers may be greater than the provisioned capacity. Power budgeting methods are employed to ensur ..."
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Cited by 34 (2 self)
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Power costs are very significant for data centers. To maximally utilize the provisioned power capacity, data centers often employ over-subscription, that is, the sum of peak consumptions of individual servers may be greater than the provisioned capacity. Power budgeting methods are employed to ensure that actual consumption never exceeds capacity. However, current power budgeting methods enforce capacity limits in hardware and are not well suited for virtualized servers because the hardware is shared among multiple applications. We present a power budgeting system for virtualized infrastructures that enforces power limits on individual distributed applications. Our system enables multiple applications to share the same servers but operate with their individual quality of service guarantees. It responds to workload and power availability changes, by dynamically allocating appropriate amount of power to different applications and tiers within applications. The design is mindful of practical constraints such the data center’s limited visibility into hosted application performance. We evaluate the system using workloads derived from real world data center traces. 1
Data center demand response: Avoiding the coincident peak via workload shifting and local generation
- In ACM SIGMETRICS
, 2013
"... Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers ’ participation in demand response is becoming increasingly important given their high and inc ..."
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Cited by 32 (3 self)
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Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers ’ participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center’s peak loads and energy expenditure: workload shifting and the use of local power generations. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two optimization based algorithms by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides the optimal worst-case guarantee. We evaluate these algorithms via trace-based simulations. The results show that using workload shifting in combination with local generation can provide significant cost savings compared to either alone. 1.
Power routing: dynamic power provisioning in the data center
- ACM SIGPLAN Notices
"... Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensur ..."
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Cited by 32 (3 self)
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Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensure availability, these proposals employ power capping, which throttles server performance during utilization spikes to enforce safe power budgets. However, because budgets must be enforced locally—at each power distribution unit (PDU)—local utilization spikes may force throttling even when power delivery capacity is available elsewhere. Moreover, the need to maintain reserve capacity for fault tolerance on power delivery paths magnifies the impact of utilization spikes. In this paper, we develop mechanisms to better utilize installed power infrastructure, reducing reserve capacity margins and avoiding performance throttling. Unlike conventional high-availability data centers, where collocated servers share identical primary and secondary power feeds, we reorganize power feeds to create shuffled power distribution topologies. Shuffled topologies spread secondary power feeds over numerous PDUs, reducing reserve capacity requirements to tolerate a single PDU failure. Second, we propose Power Routing, which schedules IT load dynamically across redundant power feeds to: (1) shift slack to servers with growing power demands, and (2) balance power draw across AC phases to reduce heating and improve electrical stability. We describe efficient heuristics for scheduling servers to PDUs (an NP-complete problem). Using data collected from nearly 1000 servers in three production facilities, we demonstrate that these mechanisms can reduce the required power infrastructure capacity relative to conventional high-availability data centers by 32 % without performance degradation.
Dynamic resource allocation and power management in virtualized data centers
- in Network Operations and Management Symposium (NOMS), 2010 IEEE
, 2010
"... Abstract—We investigate optimal resource allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for resource provisioning. In this work, we take an alternate approach that makes ..."
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Cited by 29 (0 self)
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Abstract—We investigate optimal resource allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for resource provisioning. In this work, we take an alternate approach that makes use of the queueing information available in the system to make online control decisions. Specifically, we use the recently developed technique of Lyapunov Optimization to design an online admission control, routing, and resource allocation algorithm for a virtualized data center. This algorithm maximizes a joint utility of the average application throughput and energy costs of the data center. Our approach is adaptive to unpredictable changes in the workload and does not require estimation and prediction of its statistics. Index Terms—Data Center Automation, Cloud Computing, Virtualization, Resource Allocation, Lyapunov Optimization
SHIP: Scalable Hierarchical Power Control for Large-Scale Data Centers
"... In today’s data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are no ..."
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Cited by 28 (11 self)
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In today’s data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are not scalable to control the power consumption of an entire large-scale data center, because these solutions are designed only for a single server or a rack enclosure. In a modern data center, however, power control needs to be enforced at three levels: rack enclosure, power distribution unit, and the entire data center, due to the physical and contractual power limits at each level. This paper presents SHIP, a highly scalable hierarchical power control architecture for large-scale data centers. SHIP is designed based on well-established control theory for analytical assurance of control accuracy and system stability. Empirical results on a physical testbed show that our control solution can provide precise power control, as well as power differentiations for optimized system performance. In addition, our extensive simulation results based on a real trace file demonstrate the efficacy of our control solution in large-scale data centers composed of thousands of servers.
Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters
- In ACM ASPLOS
, 2012
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 24 (6 self)
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
