DieHard: Reliable Scheduling to Survive Correlated Failures in Cloud Data Centers

Sedaghat, Mina; Wadbro, Eddie; Wilkes, John; Luna, Sara De; Seleznjev, Oleg; Elmroth, Erik

doi:10.1109/ccgrid.2016.11

Cited by 35 publications

(18 citation statements)

References 10 publications

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“…Beyond energy consumption, a further objective in some works is to minimize the number of overloaded PMs because of the performance degradation that results from overloads . Some works also considered the cost of migration of VMs, or reliability Service level agreement (SLA) handling.…”

Section: Related Workmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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Summary One of the key problems for Infrastructure‐as‐a‐Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real‐world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

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Section: Related Workmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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“…Second, the use of alternative actuators (e.g., Running Average Power Limit (RAPL) that allows control the power consumption of CPU sockets and DRAM [46]) is planned to be evaluated against already established techniques for energy efficiency in data center server environments. Third, the power-performance tradeoffs of innovative approaches increasing the performance predictability [47,48] or reliability [49,50] of cloud computing systems should be assessed to understand at what cost the proposed improvements come. Finally, we plan to investigate novel power and performance aware load-balancing algorithms.…”

Section: Discussionmentioning

confidence: 99%

Power-performance tradeoffs in data center servers: DVFS, CPU pinning, horizontal, and vertical scaling

Krzywda

Ali-Eldin

Carlson

et al. 2018

Future Generation Computer Systems

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Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, horizontal, and vertical scaling, are four techniques that have been proposed as actuators to control the performance and energy consumption on data center servers. This work investigates the utility of these four actuators, and quantifies the power-performance tradeoffs associated with them. Using replicas of the German Wikipedia running on our local testbed, we perform a set of experiments to quantify the influence of DVFS, vertical and horizontal scaling, and CPU pinning on end-to-end response time (average and tail), throughput, and power consumption with different workloads. Results of the experiments show that DVFS rarely reduces the power consumption of underloaded servers by more than 5%, but it can be used to limit the maximal power consumption of a saturated server by up to 20% (at a cost of performance degradation). CPU pinning reduces the power consumption of underloaded server (by up to 7%) at the cost of performance degradation, which can be limited by choosing an appropriate CPU pinning scheme. Horizontal and vertical scaling improves both the average and tail response time, but the improvement is not proportional to the amount of resources added. The load balancing strategy has a big impact on the tail response time of horizontally scaled applications.

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“…[88] provide a theoretical framework for the allocation of batch and service jobs in a set of constrained resources where some resources can be attacked or fail. Achieving a target reliability level can simply be a matter of placing extra replicas in different failure domains [139]. Defining failure domains at the edge, especially with end-user devices, can be difficult and requires proper observability and late characterisation of the failure modes of the devices.…”

Section: Eventually Consistent/probabilistic Orchestrationmentioning

confidence: 99%

Research challenges in nextgen service orchestration

Vaquero

Cuadrado

Elkhatib

et al. 2019

Future Generation Computer Systems

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Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.

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DieHard: Reliable Scheduling to Survive Correlated Failures in Cloud Data Centers

Cited by 35 publications

References 10 publications

Which is the best algorithm for virtual machine placement optimization?

Which is the best algorithm for virtual machine placement optimization?

Power-performance tradeoffs in data center servers: DVFS, CPU pinning, horizontal, and vertical scaling

Research challenges in nextgen service orchestration

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